If you’re new here, Welcome.

Welcome back to GVP. Every week we meet one Indian founder, verify the traction, and write the story so you can make faster investment decisions. We added 32 new subscribers last week — now at 636 investors reading weekly. If someone forwarded this to you, subscribe here.

This investment banker helped SME / SMB companies raise millions.

Hey there,

Five weeks in a row, Sapan Karia drove into Jamnagar, walked up to a factory he had no appointment with, and knocked on the door.

He’s 30. He didn’t have to do this. He spent his career in investment banking and lending, the kind of work where you sell over the phone in a nice office.

He’d left Bangalore, a comfortable life, and an office five minutes from home to sit across from factory owners in a manufacturing town 1,200 kilometres away.

In his own words, he had to “rewire my brain to be shameless again.”

At 22 he’d done door-to-door sales without thinking twice.

At 30, with a tech co-founder and a real company forming, he made himself go back to knocking, because he’d figured out something about Indian exporters that almost nobody else had noticed.

There’s a leak in how they move money. It’s invisible, it’s been there for decades, and as of January 2025, it finally became legal to fix.

Let me show you what he is building

This wasn’t allowed until one and a half years ago.

For decades, an Indian exporter legally could not hold dollars abroad. You earned dollars, you brought them home, you converted them. No choice.

In January 2025, that changed. Indian exporters were allowed to open real bank accounts overseas and keep their dollars there.

So now that same textile factory can hold its dollars in the US, and pay its Chinese supplier straight from them. No rupee in the middle. No two conversions. The leak just closes.

Almost nobody moved on this. Sapan did, within months.

He spent his whole career one step away from this problem.

Sapan didn’t stumble into exporters. He’d been circling them for years.

He started in investment banking, doing IPOs for small manufacturers – going factory to factory in Gujarat to pitch them.

Then he moved into lending, then into helping Indian brands set up supply chains abroad.

Every job put him in the same rooms, with the same owners, hearing the same complaint about money lost in conversion.

He comes from Gujarat. He has cousins and friends who run these exact factories. So when the rule changed, he wasn’t reading about a market. He was thinking about people he already knew.

He picked one kind of customer and refused to widen it.

This is the part that surprised me most. Most founders cast as wide a net as possible. Sapan did the opposite.

He’ll only work with a factory that fits all of this:

• It both exports and imports, not just one

• It’s in textiles, auto components, engineering, pharma, or chemicals

• It does $5M to $50M (₹40 crore to ₹400 crore) in revenue a year

• It’s a real manufacturer, not a trader

• It’s a second-generation family business, run by someone in their 30s or 40s who studied at a good college in India or abroad

• And right now, it’s based in one city: Jamnagar - a cluster for brass products manufacturing (sub category of engineering goods)

That last filter is the whole strategy. He isn’t trying to win India.

He’s trying to win one cluster of customers completely, build a playbook that works there, and then carry it to the next one.

By his count there are a dozen-odd clusters like this in Gujarat alone. Win two, he says, and it’s a real company. Win the cluster, and the math gets very large.

He’s closing deals by hand, on purpose.

The product isn’t fully built yet, and that’s deliberate.

Today, Sapan’s team matches companies with financing options manually, running eight mandates through buyer’s credit, factoring, and LC discounting.

Tomorrow, much of that could be handled by AI: evaluating counterparties, comparing financing structures, assessing risk, and recommending the lowest-cost option for a given shipment.

The manual work isn’t separate from the product. It’s generating the knowledge the product will eventually automate.

His team has already turned that instinct into an asset: a trade database covering 5 million companies across 120 countries, pulled from public shipping records, so an exporter can check exactly who they’re dealing with before they ship.

The plan is to own one narrow wedge first, saving exporters their forex costs, then build it into an AI-native services company for cross-border trade.

GVP’s take

Four things made us stop on this one.

• He’s an ex-investment banker who sold to these exact factories. He’s not learning the customer, he already knows them.

• The problem, market, and customer are unusually clear. Most founders are fuzzy on one. He’s sharp on all three.

• He’s finding his first customers the hard way, by hand, in person, before building.

• He quietly built a 5-million-company trade database as a byproduct.

If you know a manufacturer in India, in textiles, auto parts, engineering, pharma, or chemicals, who exports and imports? This is worth a five-minute intro. They’re exactly who Sapan helps.

If you know a digital-first bank in Singapore, South Korea, or the US that works with cross-border SMEs?

That’s the other door he’s opening.

P.S. Reply and we’ll make a warm intro to Sapan, whether you’re a factory, a bank, or just know the right person to forward this to.

If you forward this newsletter to 3 investors who subscribe, we’ll make a warm intro for you to any family office or VC in our network you’d like to meet.

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If you’re new here, Welcome.

Welcome back to GVP. Every week we meet one Indian founder, verify the traction, and write the story so you can make faster investment decisions. We added 32 new subscribers last week — now at 634 investors reading weekly. If someone forwarded this to you, subscribe here.

My dear fellow investors,

She identified this problem fresh out of college.

Fresh out of college, Aisha Peenwal walked into her first job at a fintech company in Chennai.

On her first week, a new RBI circular landed. A dense, 40-something pages, full of legal references to documents from years ago.

Her manager told her to figure out what it meant for their product.

It was a major regulation, the kind that touched almost every partnership and product running in Indian fintech at the time.

She went to four senior colleagues. She got four different answers.

Nobody was sure. Everyone was guessing. And somewhere in that room, a small process gap was turning into a compliance risk.

She filed it away in the back of her mind and got on with her career.

What made her believe this was finally solvable?

Aisha spent six and a half years in fintech product management, working on loan origination systems, working capital engines, and business rule management at companies including Credable.

In 2024, she saw a large NBFC get fined 50 lakh rupees and decided the problem was now solvable with AI.

She had taught herself Python over five years of weekends, calling friends at odd hours when she got stuck on a syntax error, staying up until something worked.

She built the first version of ReQL herself.

Her first client didn’t pay her in money. She asked them to pay her in feedback.

Two hours, sometimes three, sitting with the CEO of a small NBFC, mapping out every compliance use case the company faced from the inside.

Why is this problem getting worse, not better?

India has 9,000 registered NBFCs, 140 scheduled commercial banks, 57 insurers, and over 70,000 CA firms advising regulated clients.

Together they face over 1,200 new regulatory circulars every year across RBI, SEBI, IRDAI, MCA, PFRDA, and other regulatory bodies in India.

That’s roughly 80 updates a day.

Every entity reads those circulars separately. Every entity draws its own conclusions. Every entity hopes it got it right.

In December 2025, India’s central bank shut down 35 finance companies all at once.

The reason: these companies had been breaking small rules for months, and nobody caught it in time.

Nobody is breaking these rules on purpose. They are struggling to keep up with the updates.

How is ReQL solving this big issue?

ReQL is a small language model trained on India’s regulatory corpus.

Tell it what kind of entity you are, and it maps every obligation that applies to you across every regulator.

When a new circular arrives, it tells you what changed, what it means for your specific business, and what you need to do next.

Every answer comes with a citation — the exact circular, clause, and obligation — so you can show a regulator the proof, not just the intention.

The platform covers 28 use cases today. It is deployed with two NBFCs. Aisha is in active conversation with one of India’s three largest private banks, a connection she made through the Plug and Play cohort at GIFT City.

What do we like, and why are we featuring this startup?

• The founder has six and a half years of insider knowledge of exactly the problem she’s solving.

• She built the POC herself. Her first two deployments came from personal relationships, which means she can sell.

• The enforcement data is moving in her direction 35 NBFCs lost licenses in one month, and that number is only going up.

• India’s RegTech market is projected to grow from roughly $600 million today to over $5 billion by the early 2030s.

That growth is being pulled by 9,000 NBFCs, 140 banks, and 70,000 CA firms who all face the same problem and currently have no shared solution.

What we’re watching: this is pre-revenue. The enterprise sales cycle in Indian banking is long.

The private bank conversation is promising but not closed. The real proof of concept is the first paid client.

P.S. One last thing…

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My dear fellow investors,

For ten years, Indian cab-hailing has been a race to the bottom.

Uber, Ola, and Rapido all chase the same rider, the one counting every rupee, the one who will switch apps to save twenty.

So they slash prices, pour money into discounts, and burn cash to win him over.

What none of them built for is the second rider. The one who would gladly pay more for a clean car that actually shows up on time.

For a long time, that rider had nowhere to go. Then came BluSmart. It gave that rider exactly what they wanted. It put 8,700 electric cabs on the road, gave more than 25 million rides, and made close to $98 million (₹820 crore) in a year.

Their promise was a fixed price, no sudden surge, a clean car, and a driver who would actually show up.

Then it shut down… due to some financial fraud.

But the people who wanted those rides did not go away. It proved that india has a market for premium cabs.

That is the market Naviget is going after

Where are these premium customers?

There is a myth that India will not pay for premium.

It is half true and going out of date fast.

India is not one market, but three countries stacked together.

India 1 is the small top layer, made up of tens of millions of people who earn, spend, and consume like a rich country.

India 2 is the large aspiring middle, with hundreds of millions of people who want better products, better jobs, and a better lifestyle, but still need strong value for money.

India 3 is the largest layer, where people have very limited disposable income and most spending goes toward basic needs like food, housing, healthcare, education, and transport.

The cab giants all fight over India 2, where price wins every time.

But it is India 1 that happily pays more for convenience, and nowhere more so than for the trip to the airport.

That is where Naviget started, in Bangalore.

The city’s airport handled 43.82 million passengers last year, making it the second busiest in India, and on its busiest day, more than 137,000 people passed through.

It sits about 35 km from the city, which is too far for anything but a cab for most travellers.

By our estimate, somewhere between a quarter and a third of those passengers take a taxi, which works out to roughly 30,000 to 40,000 airport cab trips every day at this one airport.

A ride between the city and the airport runs ₹700 to ₹1,100 ($8 to $13), and that is before Uber’s surge pricing kicks in during peak traffic.

So that is the slice Naviget began with, one high-value lane where the rider already wants something nobody sells well.

What makes them different

When you schedule a cab on Uber, it feels reserved, but it is not.

The app waits until your pickup time and then looks for a driver, the same as any normal booking. That is why a driver can still cancel on you, and on the big apps most riders have faced exactly that.

Naviget works the other way around. It does not offer your ride to drivers and hope one accepts. It assigns the ride, and an assigned ride cannot be declined.

If a driver has a real emergency, the trip moves instantly to another driver in the same fleet, so your booking never collapses.

Every driver comes through a verified fleet operator who has already checked them, which is why the car shows up clean and nobody cancels.

Their business model

Naviget owns no cars and employs no drivers.

It sits on top of professional fleet operators who already own the vehicles and gives them the technology they were missing.

The partners bring the cars and the people. Naviget brings the app, the fixed pricing, and the standards.

The money comes in two simple ways:

• A small cut from the fleet operator on each ride.

• A flat platform fee from the rider on top.

There is no surge and no last-minute jump, just a fixed price the rider sees before booking.

And if Naviget ever cancels on you, it pays back double.

Anmol Sharma (Co-founder, strategy and operations). Started building young. His first company, Meal24, was a food-delivery startup he scaled past ₹1 crore in revenue before it shut down. He has been a founder twice over.

Yash Garg (Co-founder, technology). An IIT-BHU graduate who worked at Amazon before this, with hands-on experience building tech-driven systems.

What stands out is how they found this idea.

They did not start with cabs. They first built a simple tool to compare cab prices across apps, and it pulled in 5,000 users in a single day.

But watching those users taught them something that changed everything.

People were not really hunting for the cheapest ride.

They wanted a ride they could trust to show up. So they stopped building a tool that finds the cheapest cab and started building one that makes sure a good cab actually arrives, every time.

That one shift in thinking is the reason Naviget exists.

The traction

Naviget has done all of this without spending a rupee on marketing.

In about three months it completed more than 2,500 rides, almost entirely in Bangalore.

The number that matters most is repeat usage.

Around 70% of riders came back for another trip, which is the clearest early sign that the service is keeping its promise. People do not return to a cab out of loyalty. They return because it worked.

The average ride is worth about ₹1,400, so these are high-value trips, not cheap hops. And the early validation goes beyond individual riders. Naviget already serves as a mobility partner for concierge platforms like HULP and CREW by Swiggy, which use it for premium airport and executive travel.

So far it has never cancelled on a rider, and it backs that with a 2x refund if it ever does.

GVP TAKE

A lot of founders would look at BluSmart’s collapse and see a warning. Naviget’s founders looked at it and saw a map. That is the thing that gives me conviction here.

BluSmart already ran the expensive experiment and proved the hard part, that millions of Indians will pay more for a cab that simply works.

It died from fraud in the company that owned its cars, not from weak demand.

Naviget is chasing that same proven demand, but it owns no cars and carries no fleet debt, so the exact thing that killed BluSmart cannot kill them. They kept the promise and threw away the part that was fragile.

Two more things stand out:

• They started in the one place where reliability is worth paying for. A missed flight costs thousands, so paying a little more for a cab that shows up is an easy yes. It is a narrow wedge, but it is the right one.
  

• The early numbers point the right way. A 70% repeat rate with zero spent on marketing is the strongest signal a young, reliable business can show, because people only come back when the promise is held.

The gap will not stay empty. The giants can move the moment it looks worth defending.

P.S. One last thing…

If you forward this newsletter to 3 investors who subscribe, we’ll make a warm intro for you to any family office or VC in our network you’d like to meet.

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Email: jaylee@globalventureplay.com

If you’re new here, Welcome.

Welcome back to GVP. Every week we meet one Indian founder, verify the traction, and write the story so you can make faster investment decisions. We added 32 new subscribers last week — now at 565 investors reading weekly. If someone forwarded this to you, subscribe here.

The metal everyone owns and nobody can clean

My dear fellow investors,

In February 2023, India announced it had found 5.9 million tonnes of lithium in Jammu and Kashmir. The headlines went wild. India is a lithium giant now.

Two years later, India still lacks meaningful domestic refining capacity, and depends on foreign processors for nearly all of it. The reserve just sits there. The government tried to auction that block twice. Both times, not one serious buyer showed up.

Here’s the part nobody told you. Owning lithium rock means almost nothing on its own. It comes out of the ground dirty, and before any of it can go into a battery, someone has to clean it.

That step is called refining, and refining is where the power sits.

China figured this out twenty years ago. It owns only about 8% of the world’s lithium, but controls roughly 70% of global refining capacity. It barely owns the rock. It owns the cleaning. That’s how it sets the price for everyone.

So when people ask if India will be the Saudi Arabia of lithium, they’re asking the wrong question. The crown was never the rock. It was the refinery. Every gram India uses still gets shipped out, cleaned abroad, and shipped back.

That’s the gap Daratha Technologies is going after. India’s need for its own battery-ready lithium by 2030 sits between 12 and 15 billion dollars. What India makes today is almost nothing

The market hiding under the mines

In 2025, India brought in 18,200 tonnes of cleaned lithium worth 1.2 billion dollars.

Most of it, 68%, came from China. Every bit of lithium India uses gets shipped out, cleaned in someone else’s factory, and shipped back.

Think about what that means.

India is building electric scooters, solar batteries, defense gear. Every one of them runs on lithium that passed through a foreign factory first. If that supply ever gets cut, India has no backup.

None….

That’s not a small problem. It’s a hole in the country’s energy independence, and it’s been sitting in plain sight while everyone talked about the rock.

A couple of Indian companies have started building refineries in the last year, so the space isn’t totally empty anymore.

But it’s close. India’s need for its own cleaned, battery-ready lithium by 2030 is somewhere between 12 and 15 billion dollars. What India makes today is almost nothing.

That’s the gap. Not the digging. The cleaning.

How a fintech founder got into this industry

Gaurav Kaushik is a mechanical engineer.

Before this, he built and ran a fintech company called Pay4You. It put banking into rural India, in villages where the nearest bank was ten kilometres away. It worked. It still runs today, doing over 4 crore in transactions every month.

And he walked away from it.

Someone else runs it now. When I asked him why he’d leave a working business behind, his answer was simple.

“The impact felt too small. Deep tech is what pulls at him, and this lithium problem felt like something India needed, not just a nice opportunity.”

There’s one detail he almost let slip after our call, once the serious questions were done. He grew up in a small village.

“The first fridge his family ever owned was made by a foreign company, not an Indian one. He said it like small talk. But it’s really the whole reason he’s doing this. He wants to build something Indian that the rest of the world depends on.”

Why can no nation break the monopoly of China in this market?

Two reasons a 12-billion-dollar gap is still sitting empty.

Cleaning lithium is a money-losing trap, on purpose.

China didn’t just build factories. It built so many that it flooded the world with cheap, clean lithium and crushed the price.

Prices have fallen more than 80%.

So when a rich country like Australia does the math, the answer is don’t bother.

Huge cost to start, nasty waste, slow permits, and then you’re selling into a market where China can drop the price below your cost any time it wants and wipe you out.

So Australia just digs up its rock and ships it to China to clean. The dirty, low-profit step turned out to be the throne, and the West handed it over without a fight.

The old way of cleaning is filthy and costly.

The traditional method dumps in mountains of harsh chemicals and spits out piles of toxic waste while using huge amounts of water.

And because rock, salty water, and dead-battery scrap are all so different, you normally need a separate factory for each one. Three factories, three times the cost. The few who try it stay too dirty and too expensive to matter.

What Daratha is actually building

Why hasn’t someone already done this?

Lithium refining is one of the most technically challenging separation processes in the critical minerals industry.

1. One factory for any source.

Dirty lithium shows up in three forms: hard rock, salty water, and the black gunk left over from recycling dead batteries. Daratha’s plan is to do a little prep on each one so they all come out looking like the same liquid.

Once they match, one factory can finish all three. Build it once, feed it anything. One times the cost instead of three.

2. Electricity instead of chemicals. Instead of dumping in harsh chemicals, the final cleaning uses special filters and an electric current to pull the lithium out. Way less toxic waste. Over 90% of the water gets reused. Zero of the nasty leftover salt.

3. A real shot at a low price. Lower startup cost, plus reusing the heat the process already makes, means a price that could survive even when China leans on the market. That’s the whole bet. Not bigger than China. Cheaper and cleaner, at small scale, made in India.

Their long-term vision: India’s first feedstock-agnostic refining platform, processing hard-rock lithium, brines, and battery black mass through one shared system.

The team

Gaurav Kaushik, Founder and CEO. Mechanical engineer. Former CEO of Pay4You, the rural fintech he built and left, still running at 4 crore in monthly transactions. Started Daratha in April 2026.

That’s the team. One person. He’s in talks with CSIR for the filter tech and with IITs for lab access to build the first test version. No co-founders, no chemists, no operations people yet. I’m telling you exactly what’s there, because at this stage that honesty is the whole point.

My take

The timing is rare. Lithium was on India’s restricted list until 2023. The legal door to private refining is barely a year old, backed by a 1.88 billion dollar critical mineral budget. This market is young by law, not by luck.

His honesty is a feature. He isn’t claiming he’ll beat China. He’s building a backup so India isn’t stuck if it gets cut off. He told me his own ceiling before I had to dig for it.

The risk is just as real. His exact way of chaining all four steps is unproven and unbuilt. A solo founder, one month in, against a problem twenty-year-old global firms still wrestle with. Back this today and you’re backing an idea and a person, not a product.

What makes it worth your time is the size of the hole. A country handed a treasure it can’t use, a gap worth billions, and almost nobody building toward it. Whether Gaurav fills it, I don’t know. That the gap is real, I’m sure of.

Most weeks I’d point you toward a funding conversation. Not this one. Daratha is too early for that, and Gaurav would be the first to tell you so.

What he actually needs right now isn’t a check. It’s a lab and a shot.

So here’s my ask. A lot of you have deep networks. Some of you have ties to chemistry labs, research institutes, universities, or battery and EV companies running pilot projects.

If you know a lab that could give him bench space, or a company that could hand him a small pilot to prove his process, that single intro is worth more to him today than money he can’t use yet.

If you forward this newsletter to 3 investors who subscribe, we'll make a warm intro for you to any family office or VC in our network you'd like to meet.

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If you want me to make a warm intro with this startup founder, feel free to reply to this email!

Email: jaylee@globalventureplay.com

If you’re new here, Welcome.

Welcome back to GVP. Every week we meet one Indian founder, verify the traction, and write the story so you can make faster investment decisions. We added 32 new subscribers last week — now at 565 investors reading weekly. If someone forwarded this to you, subscribe here.

The Great Indian Cab Bloodbath.

My dear fellow investors,

In the last ten years, Indian cab-hailing has burned more venture capital than almost any consumer category in startup history.

Uber India lost $165M (₹1,407 crore) last year alone.

Ola’s revenue is shrinking and it’s slipped to third place.

Even Rapido, the one company close to profitability, did it only after switching its business model entirely.

It’s funny that the real moat in Indian cab-hailing was never technology. It wasn’t the drivers. It wasn’t brand. It was the ability to lose money longer than everyone else.

That market is the one Macro Rides is going after.

The Market Hiding under Uber

In Delhi, millions of people use the Metro train every day to get to work.

The Metro is fast and cheap. The problem is getting to the Metro station from home that last 2 to 3 kilometers.

That distance is too far to walk and too short to take a regular taxi like Uber.

So people use a small electric three-wheeler called an e-rickshaw.

But the e-rickshaw system is broken.

In the current situation, it takes 20 to 30 minutes for what should be one 10-minute ride.

People skip the Metro entirely because of this.

A WRI India study of 7,200 commuters found that 70% of people who could use the Metro avoid it, mostly because of this last-mile mess.

Macro Rides wants to fix it.

They want to build a phone app where you can book one e-rickshaw, it comes to your house, takes you straight to the Metro at a fixed price, and you are done.

• India has 1.5 million e-rickshaws on the road today. None of them are on an app.

• The total spend on this kind of short e-rickshaw travel is roughly $1 billion a year.

• The Delhi Metro alone carries 4.6 million people every single day — most of them dealing with broken last-mile rides just to reach the train.

This is the largest passenger-mobility category in India by trip volume. And it is not organized.

How the founder understood the riders market working amazon ops team?

Alok Jha grew up in Rohini, in northwest Delhi. For eight years, his daily commute to the metro meant catching two separate e-rickshaws, haggling on price each time, and paying twice for one short ride.

He dropped out of a computer applications degree at 19.

He took a sales job at 17, then HR at a consultancy, then two years inside Amazon’s delivery operations.

The Amazon role is the one that matters. He spent two years watching how delivery drivers get paid, why they quit, and what makes a driver loyal versus disposable.

So he paid ₹800 to drive an e-rickshaw himself

Earlier this year, Alok rented a rickshaw from a driver in Rohini for ₹800 — about what the driver normally makes in a 12-hour shift. He worked the route himself for 8 hours and ran 30+ trips.

What he learned in one day:

• Drivers spend 40% of their day empty, rolling between fares

• Passengers wait 10–15 minutes at stands for a ride to start

• Fare haggling burns over a minute per pickup

At the end of the shift, the driver asked him one question.

“Bhaiya, yeh roz ho sakta hai kya?” (Brother, can this happen every day?)

The driver wasn’t asking about ride volume.

He was asking about predictability. The real product Macro Rides has to build isn’t a marketplace. It’s a guaranteed paycheck for someone who’s never had one.

Why did everyone else get this market wrong?

Two failure patterns explain why this category is still wide open.

1. The cab-hailing model doesn’t fit a 3-km ride.
   
   Uber and Ola priced for 10+ km trips in cars. A ₹35 e-rickshaw ride generates less margin than their driver incentive payouts. Uber India spent ~$295M (₹2,516 Cr) on driver incentives last year alone to retain captains who were defecting to Rapido. The economics don’t work below a certain trip length.
   
   

2. The one VC-backed company that tried e-rickshaws bought too much.
   
   OyeRickshaw raised $13.2M from Matrix Partners, Chiratae, and Xiaomi between 2017 and 2022. They built 15 battery-swapping stations, scaled to 400 employees, and added hyperlocal delivery. Last year’s revenue: ₹57,500 (~$680). They became a fat company in a category that needed a light one.
   

The model that is working in India right now is the daily-subscription one — Rapido and Namma Yatri both took zero commission and charged drivers ₹20–₹30 a day. Rapido is now operationally profitable. Namma Yatri did 130 million rides on $5M.

Macro Rides is applying that model to e-rickshaws.

What is the opportunity here?

Three numbers matter here.

1. India has about 1.5 million e-rickshaws on the road today. They are everywhere. None of them have any technology. No app. No GPS booking. No fixed prices. Just drivers, vehicles, and stands.

2. The total amount of money spent every year on this kind of short e-rickshaw travel is somewhere around $1 billion in India. That is a big pie, and right now nobody is organizing it.
   

3. The Delhi Metro alone has 4.6 million people riding it every single day. Most of them have to deal with this last-mile mess.

So the opportunity is: take the messy, offline e-rickshaw system, put a phone app on top of it, and become the company that helps millions of people get to the Metro every day.

There is also a bigger pattern in India that helps explain why this is interesting right now.

What Macro Rides is actually building?

One app. The rickshaw comes to your door. The fare is fixed before you get in.

Three things make it work:

1. The rickshaw comes to you. No walking to a stand. No haggling. Pickup in 2-3 minutes.

2. Drivers earn more. They pay a small daily fee (₹50-150) and keep every rupee passengers pay. Rapido and Namma Yatri proved this model works. Nobody has tried it with e-rickshaws yet.

3. The company owns nothing. OyeRickshaw bought rickshaws, batteries, and a 400-person team. They died. Macro Rides owns zero vehicles. Just the app.

Pricing is simple: ₹35 for a shared seat, ₹50 for a private ride.

They launch one zone at a time. Rohini first. Then corridor by corridor across D

The team

Alok Jha (Founder & CEO). Rohini-local. 8 years lived the problem. Sales, HR, and two years inside Amazon’s delivery operations. Bootstrapped Macro Rides since June 2024.

Pooja Kushwah (Co-founder & CTO). Built platform engineering at group.one and enterprise systems at Amdocs. Built the company’s mobility dashboard solo.

Vikas Sha (Senior Operations). Six years across Emotorad, Baaz Bikes, Yulu, and Wicked Ride. EV mobility ops stack.

Advisors: Abhishek Bohare (GTM, Tohands) and Siddharth Sanghvi (Finance, Renee Cosmetics). Both IIFT alumni.

Our take

Three things stand out about this bet.

Uber and Ola can’t enter this market. A 3-kilometer ride at ₹35 is too small for them. The commission they would earn on that ride is less than what they spend to acquire a customer. The math doesn’t work for big players. That means the category is protected from the giants by default.

The business model is already proven in India. Rapido and Namma Yatri don’t take a cut from rides. They charge drivers a small daily fee instead. Rapido is now operationally profitable. Namma Yatri did 130 million rides on just $5 million. Macro Rides is copying that exact playbook — but for e-rickshaws, the one category nobody has applied it to yet.

The Delhi Metro is building 21 new stations. Each new station opens up a new neighborhood that needs last-mile rides. Every station = one more zone Macro Rides can launch in. The expansion is funded, under construction, and on schedule.

P.S. One last thing…

If you forward this newsletter to 3 investors who subscribe, we'll make a warm intro for you to any family office or VC in our network you'd like to meet.

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Eight years later: 8 outlets across Kerala and Bangalore. Daily Bakehouse at 25% EBITDA.

“Eating out in India is still considered an occasion. We want to make it a habit.”

I sat down with Vishnu Menon, CEO of Tableset Pantry, the parent company behind Just Love and Daily Bakehouse. I took some notes and have added them below.

1. Start Before You’re Ready

Vishnu and his six co-founders launched a food truck with no F&B experience and no consultant. They were engineering and architecture students running the truck from evening to 3am, then heading to class the next morning. The hardest part wasn’t the hours. It was learning an industry they had already entered. That forced speed of learning is now their operational edge.

2. The Engineering Mindset in F&B

Every co-founder came from a technical background. That turned out to be an advantage, not a liability. Where most founders see a restaurant, Vishnu saw a system: central kitchen, supply chain, replicable formats, predictive ordering. Eight years in, that systems thinking is what makes a 300 sq ft outlet with 2 employees operationally viable at scale.

3. Occasion-Led to Habit-Led

India’s F&B market is massive but culturally underutilized. Eating out is still treated as a special event, not a daily norm, held back by the gap between affordability and quality. Tableset Pantry’s entire thesis is closing that gap: engineered pricing at $0.60 to $2.40, hot food (Indians don’t eat cold sandwiches), and formats small enough to be everywhere. Turn dining out into routine, and the TAM rewrites itself.

4. The Daily Bakehouse Model Is the Bet

300 sq ft. 2 employees. Grab-and-go. Products supplied from central kitchen. Three visits per customer per day, breakfast, lunch, evening, each under $2.40. This is the brand they’re raising to scale, not Just Love. Just Love generates 90% of current revenue and anchors the brand. Daily Bakehouse is the expansion vehicle.

5. Location Is Not a Soft Decision

After 8 years and bad locations, Vishnu runs a three-filter process: Is this an F&B hub where people cluster to choose? Is our target audience here? Do the rentals fit within our hardcoded percentage of revenue? Daily Bakehouse doesn’t go into malls or high streets. It goes into airports, railway stations, and tech parks, where people are already moving and need fast, affordable food. The product is strong enough that the wrong location is the only way to lose.

6. Starbucks Is the Warning, Not the Benchmark

400 outlets. 10 years. Still not profitable in India. That’s what happens when you price coffee at $4.76 in a tea-drinking country. Tableset Pantry’s brands are affordable by design, not by discount. No coupons, no offers. The price is just right for the market. Premium formats in India are losing because they solve a Western problem. Vishnu is solving an Indian one.

7. AI Is an Operations Tool, Not a Story

Vishnu isn’t building an AI startup. But he’s using it: production planning, consumption forecasting, staff quality monitoring via cameras, internal reporting summaries. The ambition is to predict order patterns by outlet by hour using 24 months of data, reducing waste and improving efficiency. AI tightens the engine. The engine is still food.

8. Family Offices First, IPO in Ten Years

The Phase 1 raise is $1.4M, too small for most VCs and right-sized for family offices. Vishnu isn’t chasing institutional money or a quick exit. The target is full India coverage across both brands, then a public listing in 10 years. Cure Foods, which acquired multiple F&B brands and is now heading to IPO, is the local proof point that the playbook works.

If you’re new here, Welcome.

Welcome back to GVP. Every week we meet one Indian founder, verify the traction, and write the story so you can make faster investment decisions. We added 32 new subscribers last week — now at 550 investors reading weekly. If someone forwarded this to you, subscribe here.

“Your margin is my opportunity.”

— Jeff Bezos

Hey there!

Tata Starbucks lost $16 million in India last year. Other funded premium cafe chains in the country tell similar stories, with rents climbing faster than revenue.

The standard read on India’s cafe market is that

The bigger you scale, the more you bleed.

This week’s startup is the counter-example.

Seven school friends from a small Kerala town, with no F&B background, have spent eight years building two cafe brands that make money at the outlet level, where every funded premium chain loses money.

Here is their story:

Seven school friends with $8,000 and a food truck.

It started in 2018 in Tirur, a small town in Kerala.

Vishnu was studying IT and the others were doing engineering and architecture.

Between them they put in $1,200 each, bought a food truck, and started selling fresh burgers that nobody else in their town was making.

They went to college during the day and ran the truck till 3 AM.

By 2019, they had outgrown the truck format.

They converted it into a sit-down diner at the same location and called it Just Loaf. That original outlet remains in operation as the first Just Loaf.

Of the original seven, five remain in full-time roles after eight years.

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The Indian cafe market has a structural problem.

In India, eating out is treated as an event, not a routine.

The two options for most people are expensive premium chains or roadside vendors where hygiene is a coin toss.

There is no clean middle ground.

The premium chains have tried to fill that gap and hit a wall.

Tata Starbucks has 479 stores and $150 million in revenue, but the FY25 loss widened to $16 million on 5% revenue growth.

Other funded premium cafe chains in India tell similar stories of rising rent and widening losses.

Rent and staff costs eat the margin before the customer walks in.

One back-end, two brands

Tableside Pantry’s reframe is that they are not a restaurant company but a food infrastructure company that runs two brands:

• Just Loaf is an 1,800 sq ft neighbourhood diner with 52 seats. The casual sit-down anchor that generates 90% of current revenue.
  

• Daily Bakehouse is a 300 sq ft grab-and-go with six seats and two employees, where everything is pre-made in a central kitchen.
  

Customers grab hot idli, coffee, or quick lunch, all priced between $0.60 and $2.50.

Think the Indian Pret A Manger, except built around hot Indian food at a price point where one person can eat there three times a day.

Both brands run on the same central kitchen and supply chain.

They built it like engineers, not restaurateurs

The co-founders’ engineering and IT backgrounds shaped how they approached the business. They broke it into solvable systems instead of running on intuition.

• Logistics. They centralised the supply chain so multiple outlets share inventory and reduce per-unit cost as they scale.
  

• Recipe. They kept the menu short and standardised, so quality stays consistent and two brands run off one kitchen.
  

• Financials. They built the model around throughput per square foot, not seating density. A 300 sq ft Daily Bakehouse turns the same monthly profit as a 1,800 sq ft Just Loaf.

Meet the team

The company is run by five of the seven school friends who started the food truck in 2018:

1. Vishnu N Menon is the CEO. An IT engineer by training, he led the company’s transition from food truck to multi-brand operator.
   

2. Jasim Menattil is the CMO, leading marketing across both brands.
   

3. Nayeem Mallik C is the COO, running outlet operations, central kitchen, and supply chain.
   

4. Sarbas Ahamed TE is the CPO, owning menu and recipe development.
   

5. Mahroof Ahammed is the CBO, shaping brand identity for Just Loaf and Daily Bakehouse.

GVP Take

Tableside Pantry is one of the rare Indian F&B operators where per-outlet economics work without venture capital.

Two reasons we featured them.

1. Their origin story: Seven college students put $1,200 each into a food truck in 2018, ran it till 3 AM, and let customer demand build the rest of the business.

   
   They had no F&B background and no industry capital, but the food worked, and the customer pull turned the truck into a multi-brand operator.
   

2. The second is the engineering discipline. The co-founders’ engineering and IT backgrounds shaped every layer of the business: centralised logistics, short standardised recipes, and a financial model built around throughput per square foot instead of seating density.
   

   That is why two formats of different sizes deliver the same monthly profit per outlet.
   
   Eight years of discipline, two profitable formats, and a team that has stayed together since school.

That is why we featured Tableside Pantry this week.

P.S. One last thing…

If Tableside Pantry fits your thesis, reply to this email and we will introduce you to the founders. The intro is on us.

If this issue was useful, share it with one investor in your network. We are pushing for 1,000 subscribers in 60 days.

P.P.S One last thing…

If this was useful, forwarding it to one person in your network is the single best way to support what we’re building.

500+ investors already read this every week, including firms like Accel, WTFund, and Ice VC, alongside family offices from Dubai, Singapore, and Europe.

Our only promise is this: we will keep finding India’s best founders at the earliest stage, so you never miss a deal worth taking.

See you next week

If you want me to make a warm intro with this startup founder, feel free to reply to this email!

Email: jaylee@globalventureplay.com

Skyroot Aerospace changed that. $60M. $1.1B valuation. The message to every frontier engineering founder in India: the capital is now here.

ISRO spent decades building India's space foundation. Skyroot is just the first to turn it into a business.

At the geopolitical moment where countries want launch independence, and the timing couldn't be better.

When Skyroot hits unicorn status in that category, the world should stop seeing India as a back office software economy. They start seeing a frontier engineering economy.

That's the real significance.
Not the valuation.
The reclassification.

Korean founders struggle to reach Indian corporates. Indian enterprises don't have a structured way to find Korean technology partners. That gap has been expensive for both sides.

Suneel Bandhu brings years of cross-border business development between India and Korea, connecting Korean IT companies with major Indian enterprises, with roots in the Tata Group network. He understands how large organizations make decisions.

Global Venture Play works the other side: early-stage founders, investors, and venture relationships across Asia.

Our strengths don't overlap. They connect.

Startup-to-corporate and corporate-to-startup are different motions.

Together, we're building a more direct path between the Korean and Indian innovation ecosystems.

MoU Signed: Global Venture Play × Luxavera Ventures

If you’re new here, Welcome.

Welcome back to GVP. Every week we meet one Indian founder, verify the traction, and write the story so you can make faster investment decisions. We added 32 new subscribers last week — now at 550 investors reading weekly. If someone forwarded this to you, subscribe here.

“They call it market fluctuation but it’s not market fluctuation. It’s an organized loot that’s happening on the citizens of this country.”

— Shafi Parambil, Member of Parliament, Lok Sabha

Hey there!

Three months into building his startup, Aditya got the kind of email most 21-year-olds dream about.

The vice president of one of the largest travel companies in the world wanted to talk. Not about a partnership. Not about a mentorship. They wanted to buy his company.

After two meetings, they made him a real offer for the idea he launched just three months ago.

He had an opportunity to walk away with a serious payday in his final year of college.

But He said no.

Today I’m going to walk you through why a college student turned down the acquisition offer from a Fortune 500 company.

What he’s seen in the Indian flight booking market that nobody else has, and the early traction that’s caught the attention of investors and acquirers alike.

The problem that reached parliament.

In July 2024, a Kerala MP walked into the Lok Sabha holding a single screenshot.

A Kochi to Dubai flight on Air India. ₹19,062 ($228) for July 27. ₹77,573 ($930) for August 31.

Same airline, same route, four times the price one month apart.

What Shafi Parambil called this on the floor of parliament is the line you cannot unhear:

“They call it market fluctuation but it’s not market fluctuation. It’s an organized loot that’s happening on the travelers, that’s happening on the citizens of this country.”

Think about who actually pays for this loot:

• Gulf migrant workers. Around 8.9 million Indians work in the Gulf, most earning ₹25,000 to ₹40,000 ($300 to $480) a month. A ₹77,000 ($925) vacation ticket home costs them two months of salary.
  

• People caught in crises. After the Pahalgam terror attack in April 2025, Srinagar-Delhi fares hit ₹62,000 ($745) before the DGCA stepped in. Trivandrum-Delhi during another crisis touched ₹1,00,000 ($1,200)
  .

• Regular Indian travelers. International fliers lose ₹5,000 to ₹15,000 ($60 to $180) per ticket on average. 70 percent of Indians book a month in advance, locking themselves in before prices drop.

Parambil asked the government for a quasi-judicial body to regulate airfares.

They acknowledged the problem and promised an inquiry. A year later, fares still move 100 to 500 times a day.

MakeMyTrip, Cleartrip, and Skyscanner let you compare or set alerts. None of them book the cheaper fare for you when it drops.

A 21-year-old college student decided to do that himself.

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How this 21-year old solving this problem?

Aditya was a final-year BBA student in Mumbai who could not afford to instantly book flights.

He kept missing the price drops. So he built the tool he wished existed.

Here is how Nomadiq works:

• You enter your route, dates, and preferences (timing, airline, cabin class).

• The AI looks at one year of fare data (21 million data points and predicts how much the price will drop and when.

• You pay the current price upfront. The system then tracks your route 24/7.

• The moment the price drops, Nomadiq books the cheaper ticket and refunds the difference to your bank account.

Their current prediction accuracy is around 93 to 95 percent.

The biggest single drop the team has tracked so far was a Switzerland to Chennai business class flight that fell ₹5 lakh ($6,000).

Before writing a line of code, Aditya did something most founders skip.

He walked into the pricing teams at Air India and IndiGo and asked the analysts who set fares whether prices actually drop.

They confirmed it. Five months of research and 21 crore data points later, the model was ready.

The team

• Aditya Aryan — Co-founder, 21, final-year BBA student.

• Atindra — Co-founder & CEO.

• Nisarg — COO.

Eleven members in total, six in the core team.

Traction and market

The numbers from a team operating without paid marketing:

• 500+ paying customers in the first few months.

• ₹2 lakh+ ($2,400) in savings delivered to users.

• ₹1.5 lakh ($1,800) in revenue from a flat ₹350 ($4) platform fee per domestic booking.

• 300+ Smart Bookings completed.

• 2,500+ international waitlist users.

• 90% of users came from organic LinkedIn posts.

One Reddit post hit 330K views. A Mumbai auto poster campaign got picked up by news channels.

The Indian online flight booking market sits at $9.5B today and is projected to reach $15.8B by FY26 at 18.5% CAGR.

International bookings are where the money is.

Nomadiq takes 25% of the savings delivered to users on international fares, which means a single booking with a $100 saving generates $25 in revenue, the same as 5 to 10 domestic bookings.

The team is currently raising ₹2 Cr ($240K), with ₹50 lakh ($60K) already committed by the co-founder of Wernos, a corporate travel company.

GVP take:

A few things stood out to me about Nomadiq:

• They are solving a problem that an entire nation is facing, if this problem has been addressed in Lok Sabha’s parliament, there is a high chance the government will support for startups who can solve this issue.
  

• The acquisition offer is the strongest validation. A fortune 500 company does not approach a three-month-old company unless they see something the market does not.
  

• The data moat is real. 21 crore historical fare data points trained on a self-learning model is not something a competitor replicates in a weekend.
  

• The business model favors quality, not volume. One international booking earns what 5 to 10 domestic bookings earn elsewhere.
  

They’re solving a problem a nation is facing, but existing players chose not to solve. That gives them an high advantage.

P.S. One last thing…

If this was useful, forwarding it to one person in your network is the single best way to support what we’re building.

500+ investors already read this every week, including firms like Accel, WTFund, and Ice VC, alongside family offices from Dubai, Singapore, and Europe.

Our only promise is this: we will keep finding India’s best founders at the earliest stage, so you never miss a deal worth taking.

See you next week

If you want me to make a warm intro with this startup founder, feel free to reply to this email!

Email: jaylee@globalventureplay.com

If you’re new here, Welcome.

Welcome back to GVP. Every week we meet one Indian founder, verify the traction, and write the story so you can make faster investment decisions. We added 32 new subscribers last week — now at 529 investors reading weekly. If someone forwarded this to you, subscribe here.

This week we’re covering the following:

• Why 240 million Indian vehicles still wait 2 to 5 hours when they break down

• How one founder got India’s two biggest battery brands to admit what they couldn’t fix in over a decade

• What roadside assistance actually means in India today, and why less than 15% of breakdowns get organized help

• Why this 43-year-old founder spent 4 years and ₹30 lakhs of his own money before opening a single store

• How one customer becomes 4 revenue events spread over 5 years

• What to watch for when evaluating early-stage mobility infrastructure startups in India

“"Software is eating the world, but infrastructure is what feeds it." — Marc Andreessen

Uber wiped him out at 40. He started over with ₹30 lakhs.

In 2018, Sheikh Siraj had a problem.

For 26 years he had been running cabs and fleet management in Bangalore. Then Ola and Uber entered India. His business couldn’t compete and he had to shut it down.

He was 40 years old, looking for what to do next.

Everyone he spoke to kept telling him the same thing. EVs are the future. Batteries are where the money will go.

So in 2019 he started a battery business. Almost by accident, in his own words. He didn’t know what else to start.

Within six months, he saw something more interesting than batteries.

Six months in, he saw the bigger problem.

Vehicles in India weren’t dying because the batteries were bad. They were dying because nobody could fix them when they did.

The numbers tell the story.

- There are 240 million vehicles on Indian roads.
- Every year, 50 million of them break down.
- Less than 15% of those breakdowns get help from any organized service.
- When help arrives at all, the wait runs 2 to 5 hours.

That’s a service infrastructure gap, not a battery quality gap.

So Siraj went to the two largest battery makers in India to ask why nobody had fixed it.

The conversation that became a startup.

He met with Exide and Amaron, the two biggest battery brands in India.

He asked them: why has nobody fixed this?

Their answer surprised him. They had been trying for years.

Every dealer in their distribution network operates as an island, and they could not bring them onto one track.

Imagine buying a battery from a dealer in Bangalore.

You drive 50 kilometres out for a weekend, and the battery dies on the highway.

The nearest dealer to you won’t help because you didn’t buy from him. The brand can refer you to him, but he still charges you.

The dealers compete with each other for every customer. None of them have any incentive to share.

Exide and Amaron told Siraj that no centralized service operator existed in India. After years of trying to build one through their dealer network, they had given up.

That answer became his startup.

He spent 4 years quietly building the answer.

His company is called Erina Assistance.

It’s a network of physical service stores across India in four formats:

• Metro stores: in cities, serving cars, bikes, and home power systems

• Transit stores: on highways, one every 50 km, so no truck is more than 25 km from help

• Mini stores: in rural towns, for local battery service and home UPS systems

• Franchise stores: where partners put up the capital and Erina runs the operation

Each store holds inventory and dispatches in-house trained technicians. The customer opens BatterySOS, the consumer app, and a technician arrives in 30 to 45 minutes with the part needed and finishes the job on the spot.

The business runs three revenue streams from one footprint:

• Product sales: 60-65% of revenue (battery sales across automotive, UPS, industrial, solar)

• Service revenue: 30-35% (RSA calls, annual maintenance contracts, fleet support)

• Scrap recovery: 3-5% (end-of-life batteries collected and resold to recyclers)

The same hubs, technicians, and dispatch system serve all three revenue lines.

One customer becomes four revenue events.

This is what makes the model interesting.

Existing players in India each do one thing. A garage fixes breakdowns, a dealer sells batteries, a scrap collector buys old ones. Nobody owns all three steps.

Erina is built differently. Every customer becomes a multi-touchpoint relationship over 5 years:

1. Sale: customer buys a battery from Erina

2. Install: Erina installs it on the spot

3. Service: AMC visits every 6 months

4. Replace: new battery sold when the old one dies

5. Scrap: dead battery collected and resold to recyclers

That’s 4 revenue events from one customer, plus the data captured at every interaction. That data feeds a predictive engine that tells them when to reach out before the next failure.

That data layer is what investors should care about most.

Every breakdown, every service call, every battery health check builds a proprietary dataset that becomes useful to OEMs and insurers over time.

The kind of infrastructure aggregators can’t disrupt.

Most existing competitors fall into one of two camps.

Gig-based RSA apps can dispatch fast but the technician quality is a coin toss. OEM helplines guarantee quality but their dispatch is slow, often 2 to 3 hours.

Erina sits in the gap with three things competitors don’t combine:

• In-house certified technicians: not gig workers, trained and salaried

• Physical hubs: owned or franchised, holding inventory close to the customer

• A technology platform on top: dispatch, predictive engine, channel partner dashboard

The moat compounds with time. Building 500 physical stores takes years, not months. In-house trained technicians can’t be spun up in a weekend. Once Erina owns the lifecycle of a customer, switching costs are real.

This is the kind of physical infrastructure that took FedEx and DHL decades to build. Once it exists, no aggregator can wipe it out the way Uber wiped out Siraj’s first business.

GVP Take

Here’s what stood out to me from our conversation with Siraj.

1. A 26-year operator who has been disrupted before. He survived the Ola/Uber wipeout, lost his first business to it, and learned the lesson. He’s not a first-time founder learning what India’s transportation sector looks like, since he’s spent his career inside it
   .

2. Direct validation from Exide and Amaron. The two largest battery brands in India told him both that the gap exists and that they had failed to fix it after years of trying. That kind of supply-side market signal is rare.
   
   

3. Capital discipline. He’s spent over ₹30 lakhs of his own savings across 4 years and burned no external capital figuring out the business model. By the time he’s raising seed, the platform is already built.
   
   

4. EV transition optionality. The hub network he’s building today for lead-acid batteries is the same network that will service EV batteries and host charging points tomorrow. With India’s EV penetration still under 6%, he’s positioning ahead of that wave.
   
   

5. Capex intensity is the biggest risk. Each company-owned hub costs ₹30 to 50 lakhs to set up. Even franchised hubs need ₹8 to 12 lakhs from Erina for inventory and tech. The model compounds slowly, and investors should expect multiple funding rounds before EBITDA break-even in Year 3.
   
   

6. The first metro store opens days from now. There is no meaningful revenue history yet. The bet here is on the operator, the insight, and the discipline behind the four years of work.

P.S. One last thing…

If this was useful, forwarding it to one person in your network is the single best way to support what we’re building.

500+ investors already read this every week, including firms like Accel, WTFund, and Ice VC, alongside family offices from Dubai, Singapore, and Europe.

Our only promise is this: we will keep finding India’s best founders at the earliest stage, so you never miss a deal worth taking.

See you next week

If you want me to make a warm intro with this startup founder, feel free to reply to this email!

Email: jaylee@globalventureplay.com

If you’re new here, Welcome.

Welcome back to GVP. Every week we meet one Indian founder, verify the traction, and write the story so you can make faster investment decisions. We added 32 new subscribers last week — now at 529 investors reading weekly. If someone forwarded this to you, subscribe here.

This week we’re covering the following:

• Why a $50 billion market still runs on phone calls and emails in 2026

• How one founder landed India’s biggest carbon registry with a website that didn’t actually work

• What carbon credits are, and why thousands of Indian companies are about to buy them for the first time

• How HP ended up paying his engineers for free through a climate accelerator

• Why the EU’s new carbon border rules make this relevant for global investors

• What to watch for when evaluating early-stage climate infrastructure startups

“The best way to validate is when customers are willing to fund development or buy the product based on a vision or mock-up” - Atlanta ventures

Basil Paulose was studying sustainability at the University of Edinburgh when he stumbled into a problem that didn’t make sense to him.

The carbon credit market, it is worth tens of billions of dollars and was still being run on phone calls, email chains, and broker relationships.

Buyers were spending four to six months trying to purchase credits.

The same carbon credit could cost $1 from one seller and $10 from another.

And nobody had built an infrastructure layer to fix it.

Not because the problem was invisible. Everyone in the market knew it was broken. But the people

building solutions were ESG consultants, not engineers.

The market didn’t need better policy. It needed someone who could write code.

So Basil quit his insurance job in London, flew back to India with barely any budget, and started building.

His startup is Reoxide.

He convinced India’s largest carbon registry to partner with him before the product existed.

This is the part that caught our attention.

Basil approached Universal Carbon Registry, the largest carbon credit registry in India with a framework website. Not a working product. Not a demo. A mockup.

The registry signed the deal anyway.

In Basil’s words: “We cracked Universal Carbon Registry with just a framework website. We didn’t even have the product. The market was so backwards that they were looking for new innovations.”

Then he had to actually deliver. Then Basil had to scramble, coding the actual platform himself with one

front-end developer, building custom APIs and webhooks to deliver on a promise he’d already made.

The market told him yes before he asked for money.

What are carbon credits, and why is this market still stuck in the 1990s?

For readers who don’t know about ESG, here’s the short version.

When a company pollutes like (textile manufacturer, an agriculture exporter, an apparel brand.. etc) they can buy carbon credits to offset their emissions.

One credit equals one ton of CO2 reduced or removed. Companies buy them to meet regulatory requirements or to position their products as carbon neutral.

The market for these credits is massive.

But it operates like a bazaar.

There’s no central marketplace. Buyers find sellers through brokers, emails, and phone calls.

• A Nasdaq study found that 94% of carbon market participants still rely on phone and email to manage critical activities.

• New credit listings take months to onboard. One in four market participants experience significant inefficiencies.

• Price differences are extreme. The same credit might cost $1 from one seller and $10 from another, depending on how urgently the buyer needs it.

And there’s a trust problem on top of it all. Scams, double-counted credits, and weak verification have caused the voluntary carbon market to shrink by over 60% between 2022 and 2024.

Major companies like Nestlé and EasyJet have pulled back their credit purchases.

What Reoxide built, and how it works.

A corporate buyer uploads their ESG report to the platform. The AI parses the document, calculates the company’s emissions, and recommends a custom basket of verified carbon credits from pre-vetted sellers on the marketplace.

The platform is integrated with Universal Carbon Registry.

Sellers can list and delist freely — no escrow lockup, no fees to exit. Both buyers and sellers get a dashboard for tracking pricing, timing, and offset planning.

Reoxide takes 1% to 5% from the seller and 1% to 5% from the buyer on each transaction.

Future plans include subscription tiers at $20/month for basic reporting and $300/month for advanced features.

Why now: India’s carbon regulations are creating forced demand?

India has launched its Carbon Credit Trading Scheme. Core sectors are now required to report their emissions and offset them.

Indian exporters selling to the EU and Western markets also need to comply with those countries’ carbon regulations.

This creates buyers who have no choice. A textile exporter facing a regulatory deadline must buy credits.

They can’t opt out, and they can’t wait six months.

Basil is building specifically for these compliance-driven buyers.

Small to mid-size exporters in textiles and agriculture with 1 to 50 employees and revenues under half a million.

Companies that need to show carbon neutrality to keep exporting.

The go-to-market plan is phased. India first (already live with Universal Carbon Registry). EU next

(targeting biochar carbon credit sellers). Then Singapore.

The team behind Reoxide.

Basil Paulose (founder) — Master’s from the University of Edinburgh. Worked on a data project with the Scottish government. Then moved to London and worked in insurance before leaving to build Reoxide.

Dinesh (co-founder, CMO) — Currently at a fintech startup, Handles marketing and sales. Joined initially as an advisor and moved to a co-founder role in the last three to six months.

Three technical interns funded by HP through the Climate Collective accelerator, one of the largest climate accelerators in Southeast Asia. HP pays their salaries through a sustainability grant. No equity was given. The interns work with Reoxide for three months.

Reoxide has also been through Draper’s founder program.

GVP’s take

Why did we pick this one?

India made carbon credits mandatory for around 490 companies in April 2025. The first actual trading hasn’t even started yet — that’s expected mid-2026. So the market is being built right now. Almost nobody is in it.

And there’s another layer. The EU just started pushing Indian exporters to prove their carbon footprint. If you’re a textile company or an agriculture exporter selling to Europe, carbon credits aren’t optional anymore. They’re part of the trade paperwork.

He closed his first partnership before he had a product. That tells you something about the founder. When the biggest registry in your country signs a deal based on a mockup website, it means the market is desperate for what you’re trying to build.

HP is paying three of his engineers through a climate accelerator grant. No equity given. No cash spent.

For a pre-revenue startup, that’s smart resource management.

And the core gap is real. A multi-billion dollar market where 94% of participants still rely on phone and email. That’s an infrastructure problem waiting for a technical founder.

P.S. One last thing…

If this was useful, forwarding it to one person in your network is the single best way to support what we’re building.

500+ investors already read this every week, including firms like Accel, WTFund, and Ice VC, alongside family offices from Dubai, Singapore, and Europe.

Our only promise is this: we will keep finding India’s best founders at the earliest stage, so you never miss a deal worth taking.

See you next week

If you want me to make a warm intro with this startup founder, feel free to reply to this email!

Email: jaylee@globalventureplay.com

This week we’re covering the following:

• Why it takes 14 years and $3 billion to discover a single drug (and why 95% of them still fail)

• How an AI platform compresses months of pre-clinical research into a single afternoon

• What drug repurposing is, and why the cure for rare diseases might already sit in your pharmacy

• How 4 founders with zero pharma connections landed their first pilot in Bangalore

• The rare disease data strategy that billion-dollar competitors can’t replicate

• What to watch for when evaluating early-stage AI pharma startups

If you're new here, Welcome.

Every week we feature one vetted Indian startup for global investors.

We meet the founders, verify the traction, and write the story so you can make faster investment decisions. 35% of the startups we’ve featured have gone on to get investor meetings.

While the world watches the US, Asia’s best startups are building with real traction at a fraction of the valuation. That’s where we look.

If you find this valuable, subscribing and sharing this with your investor network is the best thing you can do to support us. It helps us keep finding the best founders and deals for you.

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“Someone you love will get sick, and the medicine won’t exist yet.” What will you do then?

Hey there!

Since 1985, the world has faced seven major pandemics. Swine flu. Influenza. COVID-19. Each time, the same question echoed across hospitals, governments, and living rooms: when will the medicine come?

The answer has always been the same.

Years. Sometimes a decade.

The traditional drug discovery takes 12 to 14 years and costs $2 to $3 billion.

After all that time and money, only 5 out of every 100 drug candidates reach patients. The other 95 fail somewhere in the pipeline.

Four young founders from India believe that timeline is unacceptable.

So they quit their jobs, shut down their companies, and bootstrapped an AI platform that could compress years of pharmaceutical research into 3-5 years.

Their startup is PathoscribeAI.

The $3 billion coin flip that kills 95% of new medicines

To understand why these four founders quit everything, you need to understand how broken drug discovery really is.

Imagine spending $3 billion and 14 years searching for a single key that opens a single lock.

But there are billions of possible keys scattered across the planet. You have to test each one for safety, effectiveness, and side effects. And after all that work, there’s a 95% chance your key doesn’t fit.

That’s pharmaceutical R&D.

But here’s what worsens it. Thousands of drugs already exist in pharmacies. They’ve been approved. We know they’re safe. Some of those existing drugs could treat diseases they were never designed for.

During COVID, researchers scrambled to test whether existing medications could fight the virus. Some worked. But they found them slowly, almost by accident, because no system existed to check all possibilities at once.

This process of finding new uses for existing drugs is called Drug repurposing.

It’s faster and cheaper than building new drugs from scratch. But very few people are doing it systematically at scale.

Samarth put it bluntly during our interview:

“In India, the big pharma companies don’t discover drugs themselves. They just buy the patent and sell. That’s the easy money thing going on.”

The actual research gets delegated to mid-sized companies that most investors have never heard of. Those companies are the ones struggling inside the broken system.

PathoscribeAI compresses months of research into one afternoon.

Here’s how the platform works in plain terms.

A researcher types in a disease name and its symptoms.

Behind the screen, a team of AI agents kicks into action.

One agent pulls molecular data. Another runs toxicity simulations. Another scans patent databases. Another cross-references published research to ground every finding in real science.

Within two to three hours, the platform produces a complete report. Candidate molecules, binding scores, toxicity predictions, tablet formulation details, and IP landscape analysis.

To be clear: this does not replace clinical trials. Drugs still need to pass through Phase 1, 2, and 3 testing with real patients. What PathoscribeAI does is cut the R&D phase that comes before trials.

Ashutosh explained it simply during our meeting: “We basically cut up that R&D part. We give pharma companies the research and documents so they can skip straight to Phase 1.”

His estimate: the full drug development timeline could shrink from 10-15 years to 3-5 years, and costs could drop by roughly 60%.

Most AI drug discovery companies globally focus on either finding new drugs or repurposing old ones.

PathoscribeAI does both and adds tablet formulation on top, so a pharma company receives a complete, actionable package from a single platform.

They landed their first pilot by reaching the people everyone else ignores.

PathoscribeAI didn’t chase big pharma for their first customer. They went after the hidden layer of India’s pharma ecosystem.

Samarth explained the strategy: before starting any pilot outreach, they studied how India’s pharmaceutical industry actually works.

Big pharma companies delegate discovery work to mid-sized and smaller companies. Those smaller firms are the ones drowning in slow, expensive R&D processes.

PathoscribeAI found those companies. They reached out to the researchers directly. Not the executives. Not the procurement teams. The scientists in the labs. They convinced them to try a demo.

That demo became their first pilot partnership with a pharmaceutical company in Bangalore. They’ve also partnered with a research institute connected to the Institute of Life Sciences, one of India’s top government biotech research bodies.

This is the part that caught my attention.

There’s a disease called amyloidosis. It’s so rare that only about 40 people in all of India have it. Forty patients out of 1.4 billion people.

No pharma company will spend $3 billion developing a cure for a market that small. Those 40 patients are invisible to the industry.

But an NGO in India tracks them. Volunteers check on these patients. Record their symptoms. Monitor how they’re doing, day by day.

PathoscribeAI went to that NGO and placed one of their own people inside it. That person collects real clinical data from those 40 patients every day. That data feeds directly into their AI system.

Their AI is now learning from real, living patient data for a disease that doesn’t exist in any commercial database. Not in Recursion’s $100M lab. Not in Insilico Medicine’s pipeline. Nowhere.

If they scale this model to more NGOs tracking more rare diseases, they build a data advantage that billions of dollars can’t replicate. Because the data doesn’t live in a database. It lives with volunteers sitting next to patients in their homes.

The team behind PathoscribeAI

Pratap Singha (CTO/Founder) is the one the team calls “Captain America.” He pulled all four founders together. Four years of experience building technology for healthcare with AI. He met Ashutosh at a previous startup called Mark 18, met Samarth on Discord, and convinced both of them to leave everything and join.

Ashutosh Rout (CEO/Co-Founder) worked as a research assistant at the Institute of Life Sciences, one of India’s top government biotech research bodies. Three years watching researchers struggle with broken R&D processes from the inside. When Pratap called with the idea, he quit his job and called his college friend to join too.

Soumya Acharya (COO/Co-Founder) had run an agency with Ashutosh during their college years. Three years of operations experience. When Ashutosh called, he came onboard.

Samarth Vaishnav (CMO) studied law. Pivoted to marketing. Built his own agency serving NGOs and small businesses. Met Pratap on Discord. Heard the pitch, shut down his entire company, and joined full-time. Four years in brand strategy and marketing.

Where they stand today?

Live with Bajaj General Insurance since Q3 2025. Product branded as Bajaj Allianz ClimateSafe across West Bengal, Jharkhand, Chhattisgarh, Assam, and Tamil Nadu.

• 34,937+ policies issued

• Thousands of claims with automated payout in same day

• $120K revenue last financial year

• 99.8% platform uptime

Gig workers are 59% of policies. Bootstrapped by founders plus ₹1 crore ($120K) from a Sweden-based family office.

Now raising a seed round. In active expansion talks in the UK and Japan.

Where they stand today.

• Beta platform live (Pathoscribe-v1, BioPATH model)

• First pharma pilot secured in Bangalore

• Institute partnership connected to the Institute of Life Sciences

• NGO collaborations for rare disease patient data (amyloidosis)

• Provisional IP filed on AI orchestration and formulation engine

• Bootstrapped, no external funding raised yet

• Raising $360K (₹3 crore) at pre-seed for 8% equity

• Fund allocation: 35-40% R&D, 20% marketing, remainder operations

GVP’s take.

I wanted to feature PathoscribeAI for a specific reason.

What stands out:

• They’re one of the only Indian platforms combining drug discovery, drug repurposing, and tablet formulation in a single AI pipeline. Most global competitors focus on one piece. PathoscribeAI delivers all three in one report.
  
  

• The NGO rare disease data pipeline is a genuine moat. Real-time patient data from ultra-rare disease populations is something billion-dollar competitors don’t have. If they scale this model to more diseases, that advantage compounds.
  
  

• They understand India’s pharma structure. Instead of chasing big pharma they can’t reach, they targeted the mid-sized companies that actually do the research. Smart go-to-market for a bootstrapped team.

PathoscribeAI is at the stage where one meaningful pilot can change everything. If you’re in pharma or biotech or connected to a CRO, this team is ready to prove their platform on a real drug pipeline. That pilot could be worth more than any check.

P.S. One last thing…

If this was useful, forwarding it to one person in your network is the single best way to support what we’re building.

500+ investors already read this every week, including firms like Accel, WTFund, and Ice VC, alongside family offices from Dubai, Singapore, and Europe.

Our only promise is this: we will keep finding India’s best founders at the earliest stage, so you never miss a deal worth taking.

See you next week

If you want me to make a warm intro with this startup founder, feel free to reply to this email!

Email: jaylee@globalventureplay.com

This week we’re covering the following:

• The insurance model where claims get paid in one day (and why it’s a $56B opportunity)

• How a $6 (₹600) policy pays $53 (₹5,000) the next day with zero paperwork

• Why insurance companies actually make more money when they pay claims faster

• Why Bajaj General Insurance signed up as their first customer within five months

• What to watch for when evaluating early-stage insurtech startups

If you're new here, Welcome.

Every week we feature one vetted Indian startup for global investors.

We meet the founders, verify the traction, and write the story so you can make faster investment decisions. 35% of the startups we’ve featured have gone on to get investor meetings.

While the world watches the US, Asia’s best startups are building with real traction at a fraction of the valuation. That’s where we look.

If you find this valuable, subscribing and sharing this with your investor network is the best thing you can do to support us. It helps us keep finding the best founders and deals for you.

Share

He couldn’t watch farmers & workers struggle anymore. Now his platform settles insurance claims in one day.

For 20 years, Anupam Shrey worked inside India’s insurance industry. First at ICICI Lombard. Then at Bajaj Allianz.

His job was selling climate protection to farmers, gig workers, and small businesses.

He did his job well. But he couldn’t control what happened after the sale.

Every monsoon, the same cycle repeated. A farmer would buy a policy. The rains would destroy the crop. The farmer would file a claim.

And then wait one to two months. Sometimes the money never came.

It wasn’t just farmers.
Delivery drivers during heatwaves.
Construction workers when sites were flooded.
Retail shop owners after unseasonal rain.

These are everyday workers who earn ₹500 to ₹1,000 a day. Losing three days of income with no safety net is devastating.

In Anupam’s words: “At the time of need, we are not providing the settlement to the farmers or to the workers.”

Eventually, he left. He pulled his co-founder Suman out of the same industry.

He called his engineering college friend Ankur, who is a cofounder too and had 15 years in banking tech. The three of them put in their own money, took personal debt, and started building.

Five months later, they had a live product. Their first customer was Bajaj General Insurance.

The same company Anupam had just left. Their startup is Plutas.ai.

What is parametric insurance, and why should investors care?

I had never heard this before, that’s why I thought I should explain this in simple terms to you.

Between 2019 and 2023, weather disasters caused $56 billion in losses across India.

Most of it was uninsured. Traditional insurance does not work for a $32 loss (₹3,000). The paperwork and adjuster process costs more in time than the payout is worth.

Parametric insurance flips this.

There is no claim form, no adjustments, no waiting.

The payout is triggered automatically by data.

If rainfall at your pincode crosses 128 mm for three consecutive days, you get paid the next day. If the temperature crosses 42 degrees, you get paid.

The weather station confirms it happened, and money hits your account within 24 hours.

A delivery driver buys a policy for $6 (₹600). The trigger hits. He gets $53 (₹5,000) the next day. He didn’t call anyone or fill out a form. That is parametric insurance.

Plutas built the platform that makes all of this possible.

A customer selects their location and a date range.

Plutas’s AI engine pulls 30 years of historical weather data from the India Meteorological Department and ERA5 satellite data.

It calculates the exact trigger threshold for that pincode.

The customer sees the premium, the coverage, and the exact payout conditions. They pay, and the policy is generated instantly.

If the weather event crosses the threshold, the claim is paid out automatically within one day.

The platform is cloud-based and API-driven, so any insurer can plug into it.

Current products with Bajaj include Daily HeatSafe, HeatSafe, Daily RainSafe, and Continuous RainSafe.

Plutas does not carry risk. Insurance companies do. Every new insurer on the platform multiplies the business without Plutas taking on risk.

If claims get paid faster, don’t insurers lose money?

I had this doubt in my mind: in traditional insurance, fewer claims mean more profit. But parametric insurance runs on completely different economics.

Premiums are tiny, usually $6 to $13 (₹600 to ₹1,200). Payouts are small, usually $53 to $527 (₹5,000 to ₹50,000).

The AI pricing engine uses 30 years of weather data to calculate the exact probability of a trigger event at each location.

Expected claims are already baked into the price. The insurer profits on volume.

Fast claims are actually what makes the product sellable. Parametric insurance never scaled before because customers didn’t trust it.

By settling every claim within one day, Plutas removes the trust barrier. More trust means more customers, which means more revenue.

Plutus has generated ₹25 lakh ( $30K) in additional revenue for Bajaj in the last financial year.

This is revenue that Bajaj would never have earned without the platform. The insurer didn’t lose money. They made new money from a customer segment they could never reach before.

The team behind Plutas.

1. Anupam Shrey started at ICICI Lombard and worked at Bajaj Allianz for years, selling parametric insurance across India. He translated two decades of on-the-ground insurance experience into Plutas’s product logic and underwriting rules.
   

2. Suman worked alongside Anupam in insurance for nearly 20 years. They sold the same products, hit the same walls, and eventually left together to build the solution. He leads insurance operations and distribution.
   

3. Ankur was Anupam’s classmate from engineering college. 15 years building technology in banking. Anupam convinced him to leave his corporate career and join as co-founder. He built the entire platform, from AI underwriting to automated claims, in under five months.

Together, roughly 55 years of combined experience across insurance and banking technology.

They bootstrapped with personal funds and debt before raising any external capital.

When we asked Anupam about his proudest moment, he didn’t mention the Bajaj partnership or the Tokyo award. He said:

Converting 20 years of theoretical parametric insurance knowledge into a working technology platform. The theory became the product.

Where they stand today?

Live with Bajaj General Insurance since Q3 2025. Product branded as Bajaj Allianz ClimateSafe across West Bengal, Jharkhand, Chhattisgarh, Assam, and Tamil Nadu.

• 34,937+ policies issued

• Thousands of claims with automated payout in same day

• $120K revenue last financial year

• 99.8% platform uptime

Gig workers are 59% of policies. Bootstrapped by founders plus ₹1 crore ($120K) from a Sweden-based family office.

Now raising a seed round. In active expansion talks in the UK and Japan.

GVP Take

I wanted to feature Plutas for a specific reason. The founder-market fit is unusually deep.

1. Anupam sold parametric insurance for 20 years, watched it fail, and built the fix.
His former employer became his first customer.

2. The business model works for everyone. Insurers get new revenue from an unreachable customer segment. Customers get affordable protection with payouts they trust.

3. Plutas earns a SaaS fee without carrying risk. Every new insurer on the platform multiplies the business.

The main risk is the early stage with one insurance partner.

But the core thesis is simple: climate losses are growing, the people affected most have the least protection, and insurers need the technology to serve them. Plutas built it, and the numbers show it works.

P.S. One last thing…

If this was useful, forwarding it to one person in your network is the single best way to support what we’re building.

500+ investors already read this every week, including firms like Accel, WTFund, and Ice VC, alongside family offices from Dubai, Singapore, and Europe.

Our only promise is this: we will keep finding India’s best founders at the earliest stage, so you never miss a deal worth taking.

See you next week

If you want me to make a warm intro with this startup founder, feel free to reply to this email!

Email: jaylee@globalventureplay.com

Quick Summary

• Sector: Defense Tech

• Stage: Pre-seed

• Founders: Naman Porwal (CEO, IIT Delhi ‘23, ex-HKUST, ex-Goldman Sachs) & Tanish Porwal (CTO, IIT Delhi, current student, AI & CUDA developer)

• Product: Nex Ecosystem, an AI-powered autonomy and intelligence layer for the future battlefield. Starting with automating the aerial battlefield

• Business Model: Software licensing to drone OEMs + subsystem sales + co-development partnerships

• Market: the global drone market is $73B in 2024 and is projected to reach $164B by 2030. India’s addressable market exceeds $1.3B.

Welcome to Global Venture Play (GVP)

Every week, we feature one high-potential Indian startup, carefully vetted for traction, team strength, and scalability. Helping our investors to find undervalued Indian AI startups.

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The country that automates the battlefield first wins the next decade

In 2017, two teenage brothers in India watched YouTube videos about drones being used in the Iraq and Syria conflict.

The footage of American B-2 bombers caught their attention. The internet had just become cheap across the country.

They wanted to build a plane.

They found a Vietnamese YouTube channel called Creative Channel.

Followed along step by step. A build that should have taken two hours took them two months. They didn’t know how a motor worked.

Didn’t know what servo motors were. Learned everything from scratch, failing at every step.

When the plane was ready, they took it to their family farmhouse for the first flight.

Everything was working exactly as it should.

But it crashed.

But instead of walking away, they had a thought. The machine worked perfectly. Every part did its job. It crashed because of the humans controlling it.

So they asked a question that sounded silly at the time: what if it could fly itself?

That question is now Crebain Aerospace.

Naman and Tanish believe soldiers on the front lines will soon be history. Future wars will be fought from closed command rooms.

Autonomous machines will handle air, water, land, and space.
Humans will make decisions. Machines will carry them out.

They’re starting with the sky.

Why military drones today are still "dumb"?

Most military drones in 2026 still work like remote-controlled cars with wings.

One trained pilot per drone and GPS are required for navigation.

If the enemy jams GPS, the drone gets lost. If communication drops, the drone is useless.

Ukraine produces 8 million FPV drones per year.

Their defence force is 800,000 people. That’s 10 drones per soldier.

Most get destroyed almost immediately because they can’t think for themselves in electronic warfare zones.

In the first week of the Iran conflict, the US and Israel burnt through 800 Patriot interceptor missiles.

Each Patriot costs $3-4 million. Each Iranian drone it targeted costs $20,000.

The math is unsustainable for every country trying to defend itself.

Earlier this year, India’s Army had to emergency-purchase autonomous drones from Shield AI, a US company valued at $5.3 billion.

India, one of the world’s largest defense spenders, didn’t have a domestic company that could build what they needed.

Crebain is building the brain, not the body

They aren’t building drone hardware. They’re building the intelligence that makes drones think for themselves.

They’re starting with air because it’s the most mature battlefield technology and the most urgent gap to fill.

They watched the Indian market carefully and saw that after 2021, when India restricted Chinese imports, dozens of companies rushed into drone manufacturing.

Hardware is becoming commoditized.

So they took the opposite approach. They build intelligence software that plugs into other companies’ drones. Think of it as “Intel Inside” for military drones.

Their product is called Nex Ecosystem.

It gives any UAV three capabilities:

1. The ability to see its environment using cameras and sensors without GPS (they call this Vigil)
   

2. The ability to navigate and avoid obstacles autonomously (that’s the Nex Autopilot)
   

3. And a secure data network that gives commanders a full picture of the battlefield, i.e., COP (Common Operating Picture)

They’ve already tested Version 1.0. In flight tests, their drone navigated a 10 km path without GPS and maintained a position error of less than 8 metres.

It handled dust clouds, visual occlusion, and dynamic obstacles.

Version 2.0, the mission-aware layer where operators can give high level mission objectives to the UAV and trained behaviours execute it. This stack is currently under development.

The team Behind Crebain Aerospace

Naman Porwal (CEO): IIT Delhi 2019-23. Worked at HKUST on autonomous vehicle perception and as a software developer at Goldman Sachs & Finmechanics. Leads business, Product and strategy.

Tanish Porwal (CTO): IIT Delhi, current student. Only first-years were selected to represent IIT Delhi at Inter-IIT Tech Meet 2024. AI and CUDA developer. Leads technology development.

Both come from a family that has run a fertiliser manufacturing business for 50 years.

When asked about his timeline, Naman said plainly, “We are not building a company that runs for four years. It has to be a legacy thing.”

Where they stand today.

Nex Autopilot V1.0 (GPS-denied navigational autonomy) is built and flight-tested.

They are already in conversations with Indian drone Original Equipment Manufacturers (OEMs) to integrate the software.

After Operation Sindoor (India-Pakistan, May 2025), GPS-denied capability became a top priority for the Indian Army. The founders report strong inbound interest from defense stakeholders.

They are pre-revenue. Raising $1M pre-seed for team building ($400K).

GVP Take

I wanted to feature Crebain for a specific reason.

1. India’s army buying American drones through emergency procurement is the clearest market signal. The government is actively pushing indigenous manufacturing. A domestic software company that can plug autonomy into Indian-made hardware has a structural advantage.
   

2. Hardware manufacturing in India is crowded. Software licensing to OEMs means lower risk, higher margins, and the ability to become the autonomy layer across many hardware platforms.
   

3. The founders carry a 3D printer everywhere. When parts don’t exist in the market, they design and print them on the spot. They 3D-printed a power distribution board for their prototype because the right one wasn’t available. That’s the kind of resourcefulness that matters at this stage.
   

4. The risks are real. They’re pre-revenue. Defense sales cycles run 18-36 months. The mission-aware autonomy layer (their core differentiator) is still in development. And they’re competing in a space where Shield AI has raised over $1 billion.
   

5. But the timing is perfect. After the India-Pakistan conflict, after the Iran drone warfare crisis, after India’s emergency purchase of American drones, every signal points to India needing this capability built domestically. These two brothers from IIT Delhi might be the ones to build it.

P.S. Reply to this email for a warm intro to the founders. They’re raising their $1M pre-seed now.

Cerbain Pitchdeck

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Email: jaylee@globalventureplay.com

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Quick Summary

• Startup: Ofofo

• Sector: Cybersecurity / Agentic AI / Enterprise SaaS

• Stage: Seed

• Founders: Post-exit founding team with 42 years combined experience in cybersecurity, enterprise sales, and product

• What they do: AI platform that answers enterprise security questionnaires in 30 minutes instead of 30 days, with 99% accuracy

• Business Model: Per-questionnaire pricing + compliance SaaS + procurement marketplace | 85%+ gross margins

• Market: $23B lost annually to security delays in enterprise sales

• Traction: $600K ARR in 7 months, zero churn, 83 NPS, $3M+ pipeline

Welcome to Global Venture Play (GVP)

Every week, we feature one high-potential Indian startup, carefully vetted for traction, team strength, and scalability. Helping our investors to find undervalued Indian AI startups.

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Two founders who already sold companies built the wrong product for 4 years.

Angad was part of a company that lost a major enterprise contract because they couldn’t get their SOC 2 Type 2 certification done in time.

The client liked the product, the pricing was agreed, but the security compliance process dragged on so long that the deal fell apart.

That experience stuck with him. And once you understand how enterprise software sales work, you’ll see why.

When a SaaS company tries to sell its software to a large corporation, say a bank or an insurance company, the buyer’s security team sends over a questionnaire before signing the contract.

Sometimes it’s 200+ questions about
- How do you store data?
- Who has access to your servers?
- What happens if there’s a breach?

Every answer needs documentation and proof.

And the person who has to fill it out:

He is the CISO (Chief Information Security Officer).

This process takes about 30 days on average. In many cases, 6 to 8 weeks.

And the cost of that delay are:

• 70% of enterprise deals require a security review before signing.

• Each delayed deal costs an average of $180,000 on a $2.2M contract.

• Win rates drop by 23% when deals get stuck at this stage.

• Across the industry, $23 billion is lost every year to these delays.

The problem is, your CISO is already busy protecting the company. Now they also have to fill out paperwork for the sales team. For every single deal.

So what do sales teams actually do when the questionnaire lands?

They fake it. Mark every answer “yes.”

Angad, Mohan, and Anshika watched this happen for four years while running a cybersecurity marketplace.

They started Ofofo in 2020 to help small companies buy security products.

They hit $1M in GMV, but margins were thin, about $100K a year in actual profit.

Most companies coming to them weren’t there to buy security products. They were panicking because a bigger company had thrown a questionnaire at them, and they had no idea what to do.

That’s when they started Ofofo

Ofofo compresses 30 days of security paperwork into 30 minutes.

Ofofo works like this. A company uploads all of its security documents into the platform. Audit reports, certificates, policies, server data.

The AI reads everything and understands the company’s security setup.

When a questionnaire comes in, the AI already knows the answers because it has the company’s actual data. 178 questions answered in under 30 minutes.

A team of 72 real cybersecurity experts then checks every answer before it goes out. The AI does the heavy lifting.

The humans make sure nothing is wrong. 97% AI accuracy, 100% after human review.

The platform also helps companies get security certifications like ISO 27001 and SOC 2, and source cybersecurity products from a global marketplace.

Their gross margins are above 85%.

The team behind Ofofo.

Mohan Gandhi Ponnaganti (CEO) started ethical hacking at age 12 and worked with the Indian government’s cybersecurity unit. MBA from IIM Ahmedabad. Spent years at Yahoo, Oracle, and American Express. In 2012, he built Entersoft, an application security company, grew it to 70-80 people, and sold it to an Australian PE fund. Ofofo is his second cybersecurity company.

Angad Singh Gill (Co-founder) — Took a cloud cost optimisation company from $2M to $10M ARR before it was acquired by Apptio, which was later acquired by IBM. Leads sales and go-to-market at Ofofo.

Anshika Srivastava (Co-founder) — 13 years in product consulting at Deloitte and Infosys. Leads product development.

42 years of combined experience. Two previous exits.

Where they stand today. (Their customer become an investor)

$600K ARR in seven months. Zero churn. NPS of 83. Over $3M in pipeline proposals were sent to companies.

Current customers include Darwinbox, Teachmint, Locus, CredStack, and Lending Labs.

The Lending Labs story is worth highlighting. Their CEO had 100 security questions piling up every month for six months with no resolution.

His 100-person team didn’t have the bandwidth to handle it. Two months after deploying Ofofo, the entire backlog was cleared. Questions that come in now just get forwarded straight to the platform.

He was so impressed that he visited Ofofo’s office, brought his entire dev team for a workshop to study how the product was built, and then invested as an angel investor.

GVP’s take:

This has been one of my favourite startups we’ve featured in GVP. Here’s why:

1. Two founders with exit experience building in a domain they already know deeply.

The CEO Mohan built and sold a cybersecurity company called Entersoft to an Australian PE fund. Angad ran a marketing agency and then helped scale a cloud optimization company from $2M to $10M ARR before it was acquired by Apptio, which IBM later bought.

2. Early customers didn’t just renew. Some became angel investors.

3. The before-and-after impact is clear in the numbers.

$600K ARR in seven months. Zero churn. NPS of 83. Over $3M in pipeline. What used to take 30 days and cost $180,000 per delayed deal now takes 30 minutes.

4. They are raising $2 to $4 million at a $15 million valuation.

About $500K is already committed from angels and customers who became investors.

I’ll be honest. It’s been a while since I’ve come across a startup that I’d genuinely consider investing in. This is one of them.

If you want me to make a warm intro with this startup founder, feel free to reply to this email!

Email: jaylee@globalventureplay.com

Whatsapp: +821068181518

Quick Summary

• Sector: Beauty & Wellness / Marketplace Tech

• Stage: Pre-seed | App live (beta), entering market

• Founders: Dr Sreelakshmi (CEO) & Chandramouli (COO)

• Product: All-in-one beauty and personal care booking app (salons, spas, skin clinics, dermatologists, hair care, and personal care products)

• Business Model: 12–18% booking commission + salon SaaS subscriptions (₹299–₹20,000/mo) + premium listing + data analytics

• Market: India’s beauty salon market is valued at $10.8B. There are 500M+ annual personal care appointments.

• Opportunity: The only major Indian service category without a dominant

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He lost a business deal because a salon made him wait two hours.

Hey there!

Chandramouli had an important meeting. A company pitch. The kind you prepare for weeks.

Before the meeting, he needed a quick shave. Thirty minutes, max. He walked into a salon. There was no way to check if they were busy. No way to book a time slot. He just showed up and waited.

One hour passed. Then two.

By the time the barber was done, the meeting was over. He’d missed it.

That moment stuck with him. He didn’t just lose a meeting — he found a gap.

After COVID, he and Dr Sreelakshmi decided to build the solution. They’ve spent the last four years on it.

Their startup is R Saloons & Spa.

India digitized every service category except the salon visit.

Think about how services work in India today.

Food has Zomato and Swiggy. Rides have Ola and Uber. Healthcare has Practo. Hotels have OYO. Even billboards have platforms now.

But if you want to book a haircut, a facial, or a spa treatment at a new salon? You’re calling. You’re texting on WhatsApp. You’re walking in and hoping for the best.

• India’s beauty salon market is valued at $10.8 billion.

• Over 500 million personal care appointments happen every year.

• The market is growing at 8%+ annually. And 90% of it is still offline.

• There are 6.5 lakh salons across the country.

Most have no digital presence. No way to show real-time availability. No way to collect payments online. No customer retention tools.

On the consumer side, booking takes an average of 3+ calls. Pricing is unclear, quality is unknown until you’re already sitting in the chair.

Smartphone penetration in India has crossed 750 million. UPI has 800 million+ users. Post-COVID, consumers expect to book everything from their phone.

Salons just don’t have the infrastructure to offer it yet.

There is no clear market leader in India’s beauty tech space. The early-mover advantage is still available.

R Saloons is building the booking layer this market is missing.

R Saloons isn’t just a listing app. It’s a full-stack platform built for both sides of the marketplace.

For consumers: Discover verified salons with ratings, compare pricing, see real-time availability, and book a slot in under two minutes. Payments happen inside the app via UPI, cards, or wallet. No phone calls.

For salons: A free CRM dashboard to manage bookings, track pending and completed orders, view weekly and monthly reports, and retain customers with offers and notifications. Basic onboarding is free for the first three months — no registration cost, no setup fee. After that, subscriptions start at ₹299/month.

The revenue model has four layers: 12–18% commission on every booking, SaaS subscriptions (₹299–₹20,000/month depending on tier), premium listings for visibility, and data analytics for customer behavior insights.

The app covers salons, spas, skin clinics, dermatologists, trichologists, hair extensions, and personal care products. At-home services are planned for a future phase.

Where they stand today.

R Saloons is pre-revenue. The app is live in beta. Market entry is underway in South India, where Urban Company hasn’t established a strong presence. Urban Company only does at-home services — not in-salon booking.

60+ salons have signed ahead of launch. Zero marketing spend. All onboarded through founder-led, relationship-driven outreach. The strategy: free CRM, free registration for three months, then monetize once value is proven. It mirrors what Zomato did with restaurants in the early days — sign up the supply side for free, build density, then turn on revenue.

18-month targets: 30,000 bookings/month, 3 cities, 200+ salon partners, Series A readiness.

Raising $500K–$2M pre-seed. Minimum covers 2 cities, 6 months runway. Stretch covers 5 cities, 24 months

The team behind R Saloons.

Dr Sreelakshmi (Founder & CEO) — 10 years in beauty and wellness operations. Managed 50+ salon chain locations. Deep expertise in salon economics, staffing, and customer experience. She didn’t study the salon market from a report. She ran it.

Chandramouli (Co-Founder & COO) — Leads operations and go-to-market. 8 years in consumer growth. Previously scaled B2C operations at

The team also has advisory support from a former beauty chain executive and an early-stage tech investor (in discussions).

GVP Take

1. Competitors to watch
   
   Fresha (global, not India-focused), V Urban (luxury-only in India), and Urban Company (at-home services, different model but strong brand).

   
   None of them currently own in-salon booking for the mass Indian market. That gap is real.
   

2. Dr Sreelakshmi’s background is the strongest signal.

   
   She operated 50+ salon locations over a decade. She understands unit economics, staffing patterns, and customer behavior at ground level. That kind of supply-side knowledge is difficult to replicate and rare among marketplace founders.
   

3. I’ve seen this exact model work in South Korea.
   

   Salon booking platforms there scaled fast once smartphone adoption hit critical mass. South India has the same conditions — high smartphone penetration, digital payment infrastructure already in place — but no dominant player in the category yet.
   

That’s why I chose to feature R Saloons at this stage.

1 R Saloons And Spa (2)

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Email: jaylee@globalventureplay.com

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Quick Summary

• Sector: Drone Autonomy Infrastructure

• Stage: Early-stage commercial deployment started

• Founders: Kunal & Kevin, ETH Zurich, robotics autonomy background

• Product: Bumblebee (agri sprayer) + Wasp (multipurpose inspection/ISR platform)

• Market Focus: India first (orchards and complex farms), then SEA, South America, Africa

• Opportunity: Solving the biggest unsolved problem in drones

What happens when a drone flies below 10 metres?

In 2016, Kunal Shrivastava and Kevin Kleber spent four years at ETH Zurich’s Robotics and Perception Group. This lab was among the first in the world to fly a drone indoors using only cameras.

No GPS. No motion capture. No human pilot.

Kevin’s research earned 300+ citations. Referenced by MIT and NASA.

Their professor, Davide Scaramuzza, pioneered the entire field. His lab recently beat world champion human pilots in autonomous drone racing.

By 2020, Kunal and Kevin had a question that wouldn’t leave them alone.

If drones can see and navigate inside a lab, why do commercial drones still crash into trees?

They tried selling their vision software to two drone companies. One in agriculture. One in offshore logistics. The tech worked. Then COVID killed both projects.

So they made a bigger bet. They moved to Bangalore and built the entire drone themselves.

That company is SUIND.

Why do drones work well at 200 m but fails at 20 m?

Most drones crash because they can’t see.

That sounds strange. Drones fly with GPS, radars, and sensors. But here’s what happens when a drone operates three metres above the ground, weaving through mango trees and rubber plantations: GPS stops working.

Radar can’t detect thin branches. And one wrong move means the entire platform crashes and needs expensive repairs.

This is the biggest problem holding back the global drone industry from scaling into agriculture, infrastructure inspection, and defence surveillance. The high-value use cases all happen close to terrain.

And close to terrain is where every existing drone system falls apart.

SUIND built perception first, then the drone around it.

SUIND’s approach is the reverse of what most drone companies do.

Instead of building a drone and then bolting on sensors, they built the vision-based autonomy system first and designed the hardware around it.

Their system uses low-resolution greyscale cameras to build a real-time 3D map of the environment at 20 frames per second.

It processes 18 million data points every second. The same cameras handle obstacle avoidance, terrain following, precision landing, and even targeted spraying.

The technical term is Visual SLAM (Simultaneous Localisation and Mapping). The drone is mapping its environment and locating itself within that map at the same time. No GPS required.

This is hard to do on a drone because compute power is limited.

You can’t carry a heavy computer on a flying machine. SUIND runs all of this on lightweight, low-power embedded processors that they’ve optimised from first principles.

Bumblebee: proven in 25+ crops with zero incidents.

SUIND’s first commercial product is Bumblebee, a type-certified agricultural spraying drone built specifically for orchards and complex terrain.

Why agriculture first? Two reasons:

1. Indian farmland gave them unrestricted airspace to accumulate thousands of flight hours under real commercial conditions, not just lab tests.

2. Orchards are among the most difficult drone environments globally: uneven slopes, tall trees, narrow takeoff spaces, and chemical fumes interacting with the hardware.

Bumblebee has now operated across 25+ crop types, including mango, rubber, pomegranate, apple, sugarcane, and banana. And it has completed 2,500+ commercial flight hours with zero crashes.

At $15,000 per unit, Bumblebee costs half of what a fully equipped DJI agricultural drone costs. And it operates where DJI systems struggle.

Here’s their product demo:

The team behind SUIND.

1. Kunal Shrivastava (Co-founder/CEO) — IIT Roorkee (Electrical Engineering) + ETH Zurich (MSc Robotics). Specialises in visual SLAM, sensor fusion, and computer vision. Previously recognized at Rio Tinto for improving operational safety.
   

2. Kevin Kleber (Co-founder/CTO) — ETH Zurich. Leads path planning, mapping, and advanced autonomy algorithms. 300+ citations for his foldable drone research (referenced by MIT and NASA). NASA Tech Briefs “Create the Future” Award. Drone Hero Europe Award (2019).

Early traction…

32 Bumblebee units on order.

Eight delivered. Marketing started only a month ago.

Their target is 200 units by the end of 2026, roughly $2.9–3.0 million in revenue.

Revenue model: hardware (upfront) + software (subscription).

GVP Take

Both founders spent years doing fundamental drone autonomy research at ETH Zurich in Switzerland. Their advisor, Professor Davide Scaramuzza, pioneered the field. His lab recently beat world champion human pilots in autonomous drone racing.

Most drones depend on GPS, radar, or LiDAR to navigate. SUIND uses only cameras. Same approach Tesla took with self-driving cars. No radar. No LiDAR. Just vision.

That matters for two reasons:

- Cameras are 10x lighter and cheaper than radar or LiDAR.

- And they capture far more detail, like thin branches, individual trees, and terrain changes that radar misses.

Very few drones in the world can operate autonomously close to surfaces.
That’s where the most valuable use cases such as orchards, bridges, borders, buildings.

They already have paying customers. DGCA type certified.

Worth watching: manufacturing capacity is still early. WASP is pre-commercial. And Skydio in the US uses a similar vision-based approach.

They’ve raised ~$1M so far. Seed round planned.

SUIND Pitchdeck

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Email: jaylee@globalventureplay.com

Whatsapp: +821068181518

Over the last 6 months, we featured 34 early-stage startups inside GVP.

Foue of them have now raised venture capital.

That got me thinking: what patterns do these five share?

What did early backers see that made them write cheques?

And what does this tell us about where capital is actually flowing?

Today, I’m walking through all five — the rounds they raised and the signals that made them fundable.

1. Onfinance AI

They raised $4.2M pre-series A from Peak XV Partners.

Since 2016, global banks have paid over $400 billion in regulatory fines. That’s roughly $116 million lost per day.

OnFinance built ComplianceOS, a finance-trained language model that reads new regulations, assigns tasks, and turns 14-page policy updates into workflows within hours.

The pre-Series A was led by Peak XV Partners (formerly Sequoia India & SEA), with participation from Groww Founders’ Fund, Mars Shot Ventures (Razorpay Founders’ Fund), and Shyamal Hitesh Anadkat from OpenAI.

When we featured them, they had pilots. Now they have enterprise contracts and $4.2M in funding.

2. ThirdAI Automation

Raised $3M in seed funding co-led by Endiya Partners and Capria Ventures.

Semiconductor machines cost millions. When one breaks, companies lose up to $5M per hour. Finding the root cause still takes 10 to 12 days.

ThirdAI built software that records every machine breakdown and stores every past fix. When a new issue hits, the system matches it against historical data and pinpoints the root cause — cutting diagnosis from 10-12 days to hours.

They’re now focusing on scale deployments across India, Japan, and the US.

3. BridgeAI

They raised $500k+ in seed funding with Amunra Capital.

Over 63% of web traffic now comes from AI agents. Most websites are still built for human eyes.

Arunima Valluvakandy Jayadevan (IIT Guwahati, ex-AI Talent & Culture Amp) founded BridgeAI to help businesses optimise how AI agents experience their digital infrastructure.

As agents move from assistants to operators, agent readiness will define who wins in the next phase of the internet.

4. HelpingAI

They got selected into Antler.

Three teenagers are building a reasoning model 87% cheaper than GPT-4.

They’ve built Dhanishtha-2.0, a model that reasons only when needed. Simple queries get instant answers.

But the real story was unit economics:

• 87% cheaper than GPT-4

• 10x faster than DeepSeek-style reasoning

• 1.4M downloads

• 1,300+ active developers

After we featured them in GVP, everything changed for them.

The LinkedIn post hit 3,700+ likes and 400K+ impressions in a single day. The co-founder who had stepped away

rejoined. VCs who’d been ignoring them started reaching out.

Then they got selected into Antler and relocated to Bengaluru full-time.

What these four have in common:

1. Everyone is an AI company. But that alone doesn’t explain why they got funded.

2. Each one uses AI to solve a painful, expensive, slow process.

3. They all had traction before they raised. Paying clients, active users, pilot projects. Proof of PMF.

And they were all featured in GVP before the rounds closed.

We’ll keep looking for these patterns early. And we’ll keep sharing them before the market spot them.

Thanks for reading and being part of this journey.

— Jay Lee

Global Venture Play

Quick Summary

• Sector: Semiconductor Infrastructure

• Stage: Early-stage

• Founder: Vivek – 8 years at Intel

• Product: Software for semiconductor manufacturing

• Market Focus: Japan, Taiwan, Malaysia, Singapore, South Korea, and SoutheastAsia

• Opportunity: Automating one of the most expensive manual workflows left inside semiconductor fabs

When the best engineer quits, the machine gets dumber..

Vivek spent eight years at Intel working inside semiconductor fabs. He saw the same problem again and again.

When a single machine fails, production on that line can stop immediately. Engineers dig through logs, sensor data, and old reports to figure out what changed.

It’s slow.

In some cases, diagnosis took 10 to 12 days.

Not because the engineers lacked skill — but because most of the real knowledge lived in someone’s head.

When that engineer left, the factory had to start from zero.

A small delay can cause $5 million per hour…

A single advanced lithography tool can cost over $200 million.

When it goes down, the production line tied to it often stops.

For large fabs, downtime can cost anywhere between $1 million to $5 million per hour, depending on capacity and output.

Why is it taking too long and costly to solve this?

1. The machine generates huge amounts of data, but it’s spread across different systems and screens.

2. Engineers have to manually compare what’s happening now with what happened in the past.

3. If they’re unsure, they often have to call the company that built the machine to help diagnose it.

None of this is automated. It depends on people connecting the dots.

Building memory for machines

ThirdAI is starting with one clear goal: help factories figure out why a machine failed.

Instead of letting every failure start from zero, ThirdAI builds a structured memory for each machine.

Their software connects to the tool’s data and records what happens during every incident — what signals changed, what tests were run, and what fix worked.

So when a similar failure appears, engineers don’t have to dig through logs or rely on someone who “has seen this before”.

The system analyses past cases and surfaces the most likely causes within minutes.

It doesn’t replace engineers.
It reduces the time it takes to diagnose the problem.

The Economics That Actually Work

Team

1. Vivek (Founder & CEO): He spent eight years at Intel and saw firsthand how long it takes to diagnose machine failures.

2. Sainyam (Co-Founder, AI & Engineering): He previously co-founded a B2B SaaS startup and has built AI systems across semiconductor, battery, and industrial domains. He has worked at companies like Intel and Roche, combining AI development with real industrial use cases.

Early Traction…

The software is currently being tested in a semiconductor facility in Japan, where it has been running for more than six months.

The team is starting with a narrow focus: they are testing it on one machine first. They want to make sure it works before using it on more machines.

Beyond Japan, they are targeting semiconductor hubs across Taiwan, Malaysia, Singapore, South Korea, and Southeast Asia.

GVP Take

Semiconductor manufacturing is one of the most advanced industries in the world. Yet one of its most expensive workflows, like root cause analysis, but it still depends heavily on people.

That creates a bottleneck.

When a machine fails, millions can be lost every hour. The faster a team identifies the real cause, the faster production resumes. Today, that process relies on experience, memory, and coordination across teams and vendors.

If ThirdAI can reliably shorten diagnosis time without sacrificing accuracy, this becomes infrastructure-level software inside fabs.

Not a dashboard. Not a reporting tool.

A system that factories depend on.

If you want me to make a warm intro with this startup, feel free to reply to this email!

Email: jaylee@globalventureplay.com

Whatsapp: +821068181518