What's in-between an AI Wrapper and Deep Tech?
How the NVIDIA Inception program brings clarity to pre-seed High-Tech startups (a Narrio story)
[Startup name] is the Cursor of [industry].
Hundreds of startups on Product Hunt now use this very statement as their UVP (unique value proposition) in an attempt to catch the Cursor trend wave. Individually, each startup is unique and that’s incontestable, but value added is unclear and most of the time not there.
Why does every startup want to be like Cursor?
And why wouldn’t they? Cursor’s story is the dream bootstrap story that we got to witness real time. A bunch of students who created a unicorn startup in under 2 years.
That kind of success cannot be replicated and most definitely not hundreds of times.
We’ve failed collectively with 5 startups so far (not that we’re looking to fail more) and we’ve learnt a few tough lessons.
We’ve partnered up with Narrio, who recently joined the NVIDIA Inception program, to bring you resources and help you build your startup the right way.
Narrio is an early-stage startup building an AI system that helps teams turn expert knowledge into high-quality content, quickly and consistently.
It combines structured knowledge graphs with small language models (SLM) to capture, organize, and reuse what experts know—so content stays clear, on-message, and scalable.
And what better way to describe what the NVIDIA Inception program is if not directly from their website?
“NVIDIA Inception is a program designed to help startups accelerate technical innovation and business growth at all stages. Inception is free and supports members of its global community with valuable benefits from NVIDIA and partners”1
This article will hopefully prepare you for the real world of startups outside of the inspirational Ted Talks and the industry hype.
Key takeaways
Deep Tech is a mindset before it’s a product category;
In early stages, clarity and financial leverage matter the most;
Programs like NVIDIA Inception can de-risk key technical and architectural decisions early on by filtering noise.
Table of contents
Building actual Deep Tech differentiators
Why early-stage startups need more than just good ideas
Financial barriers kill promising startups before they scale
Case study: Narrio's pre-seed reality
The NVIDIA Inception program
Conclusions
1. Building actual Deep Tech differentiators
The advancements in LLMs in the past year make it possible (and viable financially) to create a product in just 10 days. About a quarter of Y Combinator startups have generated with AI 95% of their code.2
Building tech solutions has just gotten incredibly easy and VCs (venture capitals) know this. Unless you have product-market fit or existing traction built over time, deep tech is what investors look for when considering investing in a startup.
So how to build tech that solves a real problem?
We’ve found that it takes a good mix of 3 things: understanding the problem to be solved, research of the latest technologies and trends and a big chunk of creativity when designing the solution.
“In true deep tech, teams spend years in the lab chasing fundamental breakthroughs. In commercial tech, they move fast on well-known rails. But there’s a space in between where the tech feels familiar, almost ordinary, yet there’s something quietly ambitious under the hood. That’s high tech and in this space, the pressure to monetize comes quicker, but the mindset (the patience, the obsession with getting it right) can still come from deeper ground. We pursue that with Narrio and believe it is that mix where the real edge often hides.” - Alexandra Botezatu, CEO @ Narrio
Narrio lives in that in-between. Its graph architecture, voice persistence layer, and multi-role orchestration system aren't market standards (yet!) - they are a calculated route towards a better content outcome.
And like any early bet, they require time, context, and calibration to get right.
For NVIDIA Inception startups, the benefits stack up in the deep tech sector. Not only do they receive preferential pricing for both software and hardware products, but the accepted startups also gain access to a catalog of edge and cloud platforms that allow heavy ML infrastructure deployments.
2. Why early-stage startups need more than just good ideas
Good ideas solve problems.
Good ideas are solutions that speak to the user directly.
Good ideas are built over time with market validation.
How do we know this? Because Facebook started as a rating website before it became the giant it is now. Netflix was a DVD-by-mail rental service before it switch out to being the streaming platform and movie studio it is now. Instagram started out as a check-in app with multiple features before realising people only used its photo-sharing feature and pivoted.
Every iconic company began with a good idea, but became great by adapting and compounding insights.
Good ideas need friction to become great. Friction can have a negative connotation, but in our world it actually means repeated contact with a real-world problem. Early-stage teams need friction rooted in reality, otherwise their ideas are just beliefs.
Our founding experience at Hyperplane has been greatly accelerated by mentors. Rubber ducking ideas and new angles, along with industry knowledge and expert advice allowed us to move the needle forward with speed.
Which is why the mentoring offer for startups in the NVIDIA Inception program sounds so exciting to us. Imagine some of the best tech minds training you. Imagine gaining access to a top network of professionals. The opportunities are endless!
3. Financial barriers kill promising startups before they scale
Too many good teams never make it past MVP because they can’t afford the tools or infrastructure to test properly.
Even in AI (where open models are widespread) running real workloads on GPUs, managing latency, testing new toolchains add up fast. What looks like free R&D can quickly turn into a $10k burn before a single pilot runs.
“You need to test, a lot, and that means GPU time, orchestration costs, architectural tradeoffs. You don’t just build. You experiment, discard, rebuild. And that gets expensive” - Alexandra Botezatu, CEO @ Narrio
In AI especially, the myth of "move fast and break things" breaks down. Hallucinations, misuse, and technical sprawl become real risks. The more your architecture blends research and product, the higher the cost of mistakes.
That’s why clarity early on matters and not just for speed, but for survival.
4. Case study: Narrio's pre-seed reality
Like any early-stage startup, Narrio faced the pre-seed paradox: the tension between needing proof to get funding and needing funding to get proof.
But let’s hear directly from Alexandra!
“Our challenge is layered. We are not building a wrapper around someone else’s tech but designing a system meant to remember how experts think.
That meant two types of proof are needed: technical, because the architecture isn’t battle-tested; and commercial, because we solve a real problem but still validating market pull.
We knew we couldn’t treat foundational architecture as throwaway. Not in a system designed to capture and reuse expert thinking across people, content artefacts, and platforms.
We also know that early technical decisions can snowball into costly debt later. That’s why moving from best guess to informed choice is critical and without support, expensive. Information is abundant and the challenge now is not to generate information but to find the right one in the moment of need.
Besides credits (which everyone needs), one particular surprise we had in NVIDIA Inception was the Resources recommendations tailored to our profile and product description. We saved valuable time by having a bridge of access to both information and real tools.
They bring clarity to our hardware and software decisions, and help make financial tradeoffs more grounded (less theoretical, more doable). We knew we wanted to dive deeper into NVIDIA NeMo™️ and this made it frictionless.
These weren’t answers we could pull from a search bar. They came from access, curation, and knowing what to ignore as much as what to pursue.
We barely scratched the surface of the resources we have available here and already see opportunities to leverage at every growth stage we have planned.”
5. The NVIDIA Inception program
It’s not a cash grant or a promise of investment. But for teams building high-tech products with real technical depth, it can shift the odds in your favor.
It includes:
Cloud and GPU credits
Early access to SDKs and engineering guides
Technical check-ins and access to best practices
A peer network and visibility boost across NVIDIA’s ecosystem
For pre-seed teams, like Narrio, trying to turn theory into architecture it makes a real difference in access and speed.
6. Conclusions
Many “AI startups” today are thin wrappers around APIs. And that’s fine for distribution plays, rapid monetization, and short-term utility.
The moment you try to build something persistent, with memory, adaptation, and user-specific logic, you’re dealing with edge cases, abstraction layers, and system design that has no blueprint.
Narrio chose that route. Not because it’s harder, but because it’s needed.
Support from programs like NVIDIA Inception doesn’t just lower costs. It gives clarity to the teams who are ready to earn it.
If you’re in that middle space, neither pure research nor lightweight wrapper, you’re not alone. That’s where a lot of successful companies started.
👉 For financed startups who struggle to ship before the competition does, we provide free 1:1 consulting services with our CTO
. Book an intro call here.👉 For teams who struggle to cut through sales noise with high-quality content in high-trust industries book a chat with Alexandra here.
https://www.nvidia.com/en-us/startups/?deeplink=expertise-faq--3#expertise-faq-item-d2cb7e4daf
https://www.cnbc.com/2025/03/15/y-combinator-startups-are-fastest-growing-in-fund-history-because-of-ai.html
blindlessly hit like even tho it’s a clear NVIDIA inception’s PR post due to gud knowledge!
Thanks for the link. You've given us something to think about.