One Year In: The Problem Got Clearer, Not Easier
Timur here — founder of Grizzz.ai.
A year ago, I still thought the main opportunity in AI for VC diligence was speed.
Not speed in the shallow sense of “generate a memo faster.” Something a little more respectable than that. Faster analysis. Faster screening. Faster movement from raw documents to a first-pass view.
I still believe speed matters. But after a year of building, I no longer think speed is the real problem.
The real problem is whether a fund can trust the path from source material to conclusion when the pressure is real.
That may sound like a small change in emphasis. It is not. That shift changed how I think about the product, the category, and what serious AI in diligence actually requires.
As of March 23, 2026, the operating footprint behind Grizzz.ai included 544 startups in the production database, 3,256 startup documents with extracted content, and more than 4,200 commits across the workspace. Those numbers matter only in one sense: they represent enough repetition for the problem to become clearer. A year of building did not make the work look easier. It made the shortcuts look less credible.
What got clearer?
1. The bottleneck is not output. It is defensibility.
At the beginning, it was easy to imagine the value in terms the market already understands: generate reports faster, summarize more documents, give investors a quicker first look.
That framing is convenient because it maps to the visible part of AI. People can see a faster answer. They can compare before and after. They can say, “This would save analysts time.”
But once you work with real diligence material, the weak point is not obviousness of output. It is defensibility of conclusion.
A fund does not just need text on a screen. It needs to know what source material mattered, what the system inferred, what remains uncertain, and where judgment still belongs to the human reviewer. The problem becomes sharper as soon as the output has consequences. If a first-pass screen shapes what gets a second meeting, what gets partner attention, or what gets ruled out too early, then “good enough summary” stops being a serious standard.
That was one of the biggest lessons of the year. In high-stakes workflows, polished output can hide the absence of a reliable reasoning path. The real question is not, “Can the system say something plausible?” The real question is, “Can a reviewer inspect how the conclusion was formed without starting from zero?”
That is a different category of product problem.
2. Better prompts do not solve what shared structure solves.
Another belief that changed over the year: I used to think a lot of the product advantage would come from better prompting, better orchestration, and better model behavior.
Those things matter. But they are not the deepest layer.
The deeper layer is structure.
Once you have enough documents, enough startups, and enough repeated evaluations, the real challenge is not generating one good response. It is making the system legible across many responses, many reviewers, and many cycles. That is where shared frameworks start to matter more than isolated outputs.
This became especially clear around first-pass screening. Without a framework, AI tends to produce something that feels useful in the moment but is hard to compare later. One startup gets described one way, another gets described another way, and you end up with artifacts that sound thoughtful but do not compose into a system.
That is why my thinking moved away from prompts and toward schema, framework discipline, and explicit evidence expectations. The value is not that the model can say something interesting about a founder, a market, or an execution pattern. The value is that a fund can evaluate multiple companies through a shared decision language that stays coherent over time.
The old mental model was “AI helps you think faster.” The sharper mental model is “AI helps a firm preserve evaluation quality across repeated decisions.”
That is a much harder problem. It is also the one worth solving.
3. Institutional AI is a different problem from individual AI.
This was probably the most important shift of all.
A lot of AI tooling feels impressive at the individual level. One person can move faster. One analyst can review more material. One founder can produce more output. That is real leverage, and I felt it directly while building.
But institutional reliability is not the same thing as individual leverage.
An individual can work around gaps with memory, context, and intuition. Institutions cannot depend on that. As soon as a workflow has to survive handoffs, reviews, inconsistency across operators, and changing standards over time, the bar changes. What looked powerful as a personal tool starts to look fragile as a team system.
That distinction got clearer the more the product moved from isolated capabilities to connected workflows. You do not build institutional AI by stacking smart outputs on top of each other. You build it by making sure context survives, evidence remains attached, uncertainty is visible, and the system can be reviewed by someone other than the person who first touched it.
This changed my view of what the product is trying to become.
It is not enough for Grizzz.ai to help one smart person move faster. The system has to make a fund’s first-pass process more legible, more comparable, and more reusable. Otherwise the value stays local. It never compounds.
4. More capability is not always progress. Better boundaries often are.
The first year also changed how I think about shipping.
When you are building fast, it is easy to feel that more capability equals forward movement. More connectors, more ingestion paths, more reporting surfaces, more agent behaviors, more automation. Some of that is real progress. Some of it is just more surface area.
What got clearer over time is that system quality often improves not when the system does more, but when its boundaries get sharper.
What exactly counts as evidence? What belongs in a trace? What should stay out? What gets versioned? What is live, and what is still coming soon? What should a human reviewer see immediately, and what should stay in the background?
These questions are less glamorous than feature expansion, but they are more important. The longer I worked on the system, the more I saw that trustworthy AI is not defined by how many things it can do. It is defined by how clearly it exposes the things that matter and how consistently it refuses to pretend about the rest.
That has shaped not just product decisions, but also how I think the company should speak in public. Hype is cheap partly because it hides the boundary conditions. Serious systems do the opposite. They make the boundary visible.
5. A year of building made the category feel narrower, not broader.
At the beginning, it was tempting to imagine a wide future very quickly. Many domains. Many users. Many adjacent workflows. In one sense, the underlying infrastructure can support that ambition.
But the more specific the work became, the more I respected the cost of being vague.
VC diligence is not just “knowledge work.” It has its own operating pressure, its own pace, its own consequences for weak reasoning, and its own mix of structured and unstructured evidence. That is why the category has become more specific in my mind over time, not less.
The problem is narrower than “AI for finance” and deeper than “automate investment memos.”
It is about decision infrastructure for VC diligence: how to move from raw startup, market, and supporting material into a first-pass process that remains inspectable, comparable, and usable by a real firm.
That narrowing is useful. It keeps the product honest. It prevents the company from talking like a generic AI startup. It also makes the second year more demanding, because a narrow category forces sharper standards.
You cannot hide behind breadth when the claim is specific.
What I think now
A year in, the main lesson is not that AI can accelerate the work. That part is obvious now.
The more important lesson is that acceleration without legibility is not maturity. It is just faster ambiguity.
If the system cannot preserve evidence, expose uncertainty, support comparison, and survive team-level use, then it does not matter how impressive the first output looks. It is still fragile.
That is what became clearer over the first year.
The product question is therefore stricter than I thought in March 2025. Not “Can AI help produce analysis?” Not even “Can AI help a person make better first-pass judgments?” The harder question is:
Can an AI-assisted diligence system remain trustworthy when it becomes part of a firm’s actual operating rhythm?
That is the question I care about now. It is also the question I want the second year of Grizzz.ai to answer more concretely.
If the first year was about building enough to make the real problem legible, the second year should be about proving that the solution can hold up under repeated use, shared workflows, and institutional pressure.
That is a narrower ambition than I might have described a year ago.
It is also a more serious one.