Ground-truth data for AI labs
Top AI labs use our tasks to train and test their models. Every task is checked by machine, so a passing score is always real.
The easy problems are already solved. The next gains come from problems that are genuinely hard, and impossible to fake.
The best models already pass the public tests. To get better, they need harder problems with answers that can be checked, not guessed.
Most data on the market is sloppy. It is vague, random, or easy to cheat, and it teaches a model the wrong thing. We build the opposite.
Every tile is real product, not a description. This is what we build for labs.
Real software problems an AI agent solves step by step, graded by hidden tests that fail on the broken code and pass only on the fix.
- def resolve(node): + def resolve(node, source=None): + return dispatch(node.value, source) # pytest -q 12/12 passed
A sealed world with a checker that only passes on a real win.
Problems that ship with a correct answer and an exact checker.
Messy, real-world data questions where the agent has to work out what actually matters, with plausible traps that look right but are not.
# underspecified prompt + 4 messy sources join: stripe ↔ postgres ↔ events signal hidden in 2-table overlap # red herrings present, skepticism scored
What we have shipped
Seven kinds of data work we have delivered. Each one shown as how it is built, with no client named.
Every task follows the same path, from a shared goal to a checked delivery.
We agree the goal, the scope, and what a good answer looks like.
We build the task on real code or data and write the checker.
It clears our checks: clean build, repeatable, backed by a real answer.
We test it against strong agents and tune it to the right difficulty.
You get the task in your format, with the solution and the checker.
How we check
Every task runs through a fixed set of checks. Only the ones that hold up reach you.
A task is only useful in a narrow range. It has to be hard enough that most agents fail, and just within reach for the best.
We test each task against strong agents and keep the ones that land in that range. Too easy or too hard, and we fix it or drop it.
Each dot is a task. We keep the ones in range, and fix or drop the rest.
Our tasks ship inside the public benchmarks labs use, and we have passed the top review tiers of leading data platforms.
Your prompts and data stay yours, and stay private. We work for you alone.
Only the hardest checkable tasks. That focus is why every task holds up.
We deliver in the formats your team already uses. Plug it straight into your training and eval pipelines.
Akash is an engineer and designer. He builds and reviews hard tasks across coding, agent environments, math sets, and open-ended analyst work. His tasks ship inside public AI benchmarks and have passed the top review tiers of leading data platforms. He also designed Topdeta's brand and built this site.
A small, vetted team of reviewers checks every task before it ships, with calibration and failure analysis on each one.
Work with us
If your models need harder, more trustworthy data, book a short call or send a few details. We reply within a day or two.
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