AI breaks where professional work gets hard.
We teach it how experts decide: evaluation sets and training data, written and graded by practising professionals.
Each dot is a model’s answer. One expert review. About one in six passes.
How a task is graded
It starts with real work.
A practising professional writes the task the way a managing director would actually ask for it.
The model answers.
Fluent, confident and plausible. On a first read, it looks finished.
A second expert sets the bar.
They check the task, then write the rubric: what earns credit, and the shortcut that loses it.
Every point has to be earned.
Each criterion is checked against the answer. It misses the one-off gain, and the shortcut costs it.
4 / 10
Not sendable as-is.
That gap, written down criterion by criterion, is what we measure, and what our data teaches a model to close.
On general benchmarks, models look ready.On professional work, they are not.
16%
The gap is rarely knowledge. It is judgement: the check an expert always runs, the shortcut they never take. That is what general data does not capture, and what we write down.
Start small. See the work before you pay for it.
Four stages, each ending with something you can use, whether or not you go further.
Scope
We agree the workflows, the failure modes and the fields that matter for your model, and what a good answer looks like.
You get A written scope and a sample rubric
Sample
Practising experts write a small set of tasks and rubrics for you, free, so you can judge the quality first-hand.
You get A free sample of graded tasks
Pilot
We build the pilot at the size you need. Every task is reviewed by a second expert before it reaches you.
You get A pilot evaluation set or training batch
Measure
We run the set against the models you name and show where each one fails, criterion by criterion.
You get A failure report you can re-run
Three kinds of data. One standard: an expert would sign it.
Expert evaluation sets
Tasks taken from real professional workflows, each with a weighted rubric written by a practitioner. Re‑runnable, so you can hold every release to the same bar.
Reasoning‑trace training data
Worked solutions that show how an expert reaches the answer: the checks they run, the shortcuts they refuse, the calls they make and why.
Rubric and preference data
Weighted rubrics, penalty criteria and expert judgements between model answers, ready for reward models and reinforcement learning.
Four things we will not compromise on.
- Isolated by design
- Each client’s tasks, data and expert team are kept separate, and nothing we make for you is reused for anyone else.
- Fair expert terms
- Experts are paid professional rates for skilled work and told plainly how that work will be used.
- Neutral by structure
- We do not build a competing model, so our only interest is making yours better.
- Measured, not asserted
- Every claim about quality comes with the rubric, the reviewer notes and the results behind it.
Who we work with
We work with AI product teams building agents for knowledge‑work professions, enterprise AI groups adapting models to their own workflows, and research teams who need a benchmark they can re‑run and publish on.
What we are learning, written down.
More notes on rubric design and failure analysis are in progress.
Questions teams ask first.
Something else? hello@priberry.com
What does it cost?
We price per task, after scoping, because cost depends on the field and how hard the tasks are. The first small sample is free, so you can judge the work before you pay for any of it.
How do you handle our data?
Anything you share is used only for your project and seen only by the experts assigned to it, under confidentiality agreements. It is returned or deleted when the work ends, and we are glad to sign your NDA before you send anything.
How do you verify experts?
We check each expert’s professional background and credentials before they work on a project. Every task is then reviewed by a second expert before delivery, so no single person’s judgement goes unchecked.
Which fields do you cover?
We scope fields per pilot and only take on work we can staff with practising professionals. Tell us the workflow and we will say plainly whether we can do it well.
How long does a pilot take?
We agree the timeline during scoping, once we know the field and the volume. The free sample comes first, then the pilot, with dates set before any work starts.
Do you build your own model?
No. We do not train or sell a competing model. Our work goes into yours.
Show us where your model breaks.
Tell us the field, the workflow and what your model gets wrong. A practitioner reads every request.
What happens next
We reply within two working days
From a person on our team, not an autoresponder.
A short scoping call
The workflow, the failure you care about, and whether we can staff it well.
A free sample
A few graded tasks in your field, before you commit to anything.
Prefer email? hello@priberry.com