Every AI team I talk to tells the same story: the agent or model works in the lab, the demo goes great, and then the project quietly stalls somewhere between “this works” and “this is in production.”
It’s rarely because the agentic workflow or model is wrong. It’s because nobody can prove it’s right — not on the rare event that matters, not on the scenario that hasn’t happened yet, and not without hitting a wall of privacy and governance constraints. We started Rockfish because we kept running into that from every direction — as researchers, as operators, as people who had tried to ship products ourselves. The gap was never talent or ambition. It was data: enough of it, the right kind, in a form a team could actually use.
The real bottleneck in AI today
Most conversations about AI focus on models and agents. The harder problem sits one layer down. Models need evidence that they generalize — not just what usually happens, but what rarely happens and what hasn’t happened yet. That evidence is exactly what most enterprises are missing.
Historical data reflects the past. It under-represents rare events almost by definition. Meanwhile, the data that does exist is often the data a team is least free to use: customer records, protected health and financial information, operational data locked behind governance policies that exist for good reason.
Waiting for a rare failure to occur naturally in production isn’t a testing strategy — it’s a hope.
Our approach: earn trust in the data, not just the demo
Rockfish is a generative data platform built to close that gap. We start from a customer’s schema and a small sample, generate a synthetic baseline that preserves the real statistical, temporal, and structural properties of the data, then let teams inject the specific rare events, edge cases, or future scenarios they need — described in plain language, fully labeled.
Every dataset is validated against fidelity, privacy, and coverage bars before it reaches a model or agent. And because the whole process runs inside a customer’s own environment — their VPC, on-prem, or natively inside a platform like Snowflake — teams get the data they need without ever moving or exposing the data they’re protecting.
Generating data was never the hard part. Trusting it — and testing against it — is.
Why this isn’t just a prompt away
This is the question we hear most, so it’s worth answering head-on. Anyone can prompt a large language model — or wire up an open-source library — for “some” synthetic data in an afternoon. That gets you most of the way to something that looks right, and none of the way to something you can trust. And the entire point of this data is to prove a model is safe to ship — so the gap between the two is the whole game.
A prompt gives you data that looks right. It doesn’t give you data you can stake a production decision on.
In practice, a general-purpose model falls short in four specific ways:
- It breaks the structure. The output reads plausibly while quietly violating the statistical and temporal relationships a downstream model actually depends on.
- It covers the wrong cases. It reproduces the edge cases it already knows from public data — not the rare event buried in your last incident, which is exactly the one you need.
- It isn’t repeatable. Ask twice, get different data. That’s a poor foundation for a regression suite you have to trust over months.
- It offers no guarantees. No formal fidelity or privacy bar — which means you quietly inherit that judgment call and own it forever.
To be clear, we lean on LLMs heavily where they genuinely add value — turning a plain-English description of a scenario into a precise, structured generation request is exactly the kind of interface problem they’re built for. What we don’t do is treat an LLM, or a generic library, as the engine that generates and validates the data itself. That part is purpose-built generative modeling, grounded in a decade of peer-reviewed research out of Carnegie Mellon — published at NeurIPS, ICML, AAAI, SIGCOMM, and IMC. We didn’t back into synthetic data as a side effect of a broader product; it’s the problem our founding team set out to solve from the start.
What this looks like in practice
The best evidence is what it does for teams already using it:
- Conviva — reproduced the behavioral lift their NEXA agent needed to stress-test before shipping, roughly a 3.5× improvement over historical data alone.
- Rento Perú — synthetic booking scenarios drove a 2.4× lift on the priority segment, taking conversion from a ~0.65% organic baseline to 24% on the customers that mattered most.
- BIMCON — generated a dataset 5× larger than the team had access to, at the same take rates, while holding full regulatory compliance.
In each case the constraint wasn’t model quality — it was the data available to prove the model out. Once that was lifted, teams moved from prototype to production.
Where we’re headed
Our vision is simple to state and hard to achieve: every enterprise, data ready for AI. We get there by empowering teams to build and deploy AI reliably, on data grounded in reality rather than assumed to be good enough. Today we’re focused on the data that matters most for that mission — structured, temporal, and event data, the backbone of the operational systems enterprises depend on. That’s a choice, not a limitation: we’d rather go deep and be right than broad and generic.
The charge
None of this matters if it stays a slide or a research paper. Every decision we make should move the industry toward one standard: AI systems tested against reality before they ever have to prove themselves in front of a customer. That’s the bar we’re building Rockfish to meet.
Muckai Girish is Co-Founder & CEO of Rockfish Data. Connect on LinkedIn.