Here are two numbers that don’t usually move together. According to Tracxn’s India Tech 9M 2026 report, as covered by MediaBrief, Indian tech companies raised $10.3 billion in the first nine months of 2026. That’s up 7% on last year. Over the same period, the number of funding rounds fell 38%, to 1,134. The year also produced six new unicorns.
Put another way, the total went up while roughly 700 fewer rounds got done. Divide $10.3 billion across 1,134 rounds and you get an average of about $9 million each. Averages hide a lot, and a handful of big late-stage cheques can pull that figure up. The direction is still clear, though. Investors aren’t sitting on their money. They’re putting more of it into fewer companies.
We build software for early-stage teams, so we care less about the headline than about who got cut. When the number of rounds drops by more than a third, the companies that lose out are rarely the ones with paying users and a stable product. They tend to be the ones still pitching a demo.
What “fewer, bigger rounds” actually filters for
For most of the last cycle, a polished prototype could raise a seed round. You had a Figma file, a clickable build, and a backend held together with a no-code tool and a few serverless functions. The pitch was that the money would pay to build the real product. Investors accepted it because there was a lot of capital around and speed seemed to matter more than soundness.
A 38% drop in deal count tells you that deal no longer works as often. When investors write fewer cheques, each one gets more diligence, and diligence now goes past the pitch deck. We keep seeing the same questions come up in technical due diligence:
- Is the app in the stores, and how does it behave on a mid-range Android phone on a patchy 4G connection?
- Is there a real backend with authentication, role-based access, and an admin console that ops staff can use without calling an engineer?
- If we brought in someone to read the codebase for a day, would they find something that forces a rewrite?
- Where does the AI feature send user data, and what stops a prompt injection from leaking it?
None of these are unusual questions. The change is that they now come before the term sheet instead of after it.
The engineering bar, concretely
“Build a production product” is vague advice, so here is what we think it means for a team trying to be one of the 1,134.
A backend you’d be comfortable showing someone
That means a real data model with migrations, not a spreadsheet you’ve outgrown. It means auth done properly: hashed credentials, token expiry, and permissions enforced on the server, not just hidden in the UI. It means logs you can actually search when something breaks at 11pm. And it means an admin console, because the first operations hire will need to refund an order or unlock an account without a developer running SQL by hand. It isn’t exciting work. It’s the work a technical reviewer checks first.
Mobile that feels native, because users can tell
In India especially, your users mostly carry budget and mid-range Android devices, and they’ll uninstall an app that drops frames or drains the battery. Cross-platform is the right call for most early teams. We build in Flutter for that reason. But the parts that users actually notice, such as camera, background tasks, notifications, payments, and health or sensor data, often need real Swift and Kotlin underneath. A team that can only work at the framework layer gets stuck the first time a platform API misbehaves. The Flutter apps we’ve shipped, Crumb Count and Kompete, both needed native work in places a prototype would never have reached.
Code that survives an audit
This is where many funded-looking startups turn out to be fragile. We do code audits and architecture troubleshooting on existing codebases, and the same problems come up again and again. There are API keys committed to the repo. Business logic is copied across three services. Nothing is tested. One engineer who left six months ago is the only person who understood the payments flow. Any one of these might be survivable. Put three together and an investor’s technical advisor will recommend a rewrite, and a rewrite line item in diligence changes the valuation conversation.
AI features with a threat model
Almost every pitch this year has an AI component, and reviewers are getting sharper about it. They want to know what data reaches the model provider, whether user-supplied text can override your system instructions, and what happens when the model is wrong in a way that costs money. A chat feature that works in the demo and leaks another customer’s records under an adversarial prompt is a liability, not a feature. Security in AI integrations belongs in the architecture from the start. It can’t be added later.
What we’d tell a founder reading these numbers
The six new unicorns will get the headlines, but most founders aren’t raising at that level. What matters for them is that the funnel got narrower while the money inside it grew. That’s good news if you can show a working product, and bad news if you were planning to raise on a prototype and build afterwards.
The practical takeaway is to spend on engineering that makes diligence boring. Ship to the stores early. Put real auth and a real admin tool behind the app. Have someone outside the team read your code before an investor’s reviewer does. Treat your AI feature as an attack surface. None of this needs a big team. It needs engineers who have shipped production software before and will tell you plainly which parts aren’t ready yet.
That’s the kind of work we do at orithLabs. We’re two founders who build mobile apps, backends, and AI integrations meant to go to production, and we audit codebases that other people built. If you’re getting ready to raise and want an honest view of where your product stands, you’ll be talking directly to the engineers. We’ll tell you what holds up and what doesn’t.