Methods
How we evaluate non-deterministic systems — what to test, how to score it, and what the result actually proves.
What Labs does
The gap model evals leave behind.
Model evals are necessary but not sufficient. Benchmarking a model in isolation doesn’t catch adversarial manipulation, bias, leakage, unsafe agency, or production drift — those live in the system of work around the model. Labs exists to close that gap: to keep building evaluation methods that hold up against how AI actually fails, in real conditions, in regulated and high-stakes settings.
Not a side project
What Labs makes is what the rest of Qapitol runs on.
Labs isn’t a side project. What it produces is what the rest of Qapitol runs on — the evaluation methods inside our engagements, the assurance approach behind sign-off, and the research that informs both.
The way AI fails keeps moving — so the way we evaluate it has to keep being rebuilt. That rebuild is the work.
What comes out of it
The methods behind every program.
See how the methods become a delivered outcome in How it works, and the platforms that carry them in Technology.
In the open
The published research.
Some of what Labs learns is for our clients alone. Some of it should be in the open, because the whole market is figuring out AI assurance at the same time. Our flagship is the State of AI Assurance report — written for the people who carry sign-off responsibility, not for an academic audience.