METHOD / REASONING
Make judgment measurable.
We turn professional workflows into expert-built datasets, evaluations, and repeatable feedback systems.
WHAT WE BUILD
A complete system for seeing where a model’s work fails.
- 01Expert demonstrations and gold-standard revisions
- 02Preference and critique data
- 03Rubric-scored evaluation suites
- 04Adversarial and ambiguity-rich scenarios
- 05Multi-document and multi-turn tasks
- 06Failure analysis and targeted follow-on sets
The output is not the product. The evaluation system is.
Each task combines a realistic record, an answer worth challenging, explicit standards, practitioner review, and an adjudicated artifact that can be reused and improved.
MODEL RESPONSE / EXCERPT
Polished. Plausible. Incomplete.
- 01Cites the general cap
- 02States a confident conclusion
- 03Misses the later document
- 04Does not preserve uncertainty
Synthetic example. No client information or assessment material.
Design. Calibrate. Build. Verify.
Every engagement should improve both the model being tested and the system used to evaluate the next one.
- 01
Define the work
Start with a real workflow, participant role, source record, constraints, and consequence.
- 02
Specify quality
Set the criteria before seeing model output. Separate material error from stylistic preference.
- 03
Calibrate experts
Match practitioners by domain and test agreement on representative cases.
- 04
Generate and evaluate
Collect demonstrations, critiques, scores, revisions, and structured rationales.
- 05
Adjudicate
Investigate disagreement rather than forcing artificial consensus.
- 06
Compound
Feed recurring failures, task difficulty, and expert reliability into the next program.
THE COMPOUNDING LAYER
Each program should make the next standard stronger.
FOR AI TEAMS
Bring us a workflow.
We will help make it measurable.
Tell us what the system needs to do, what the record looks like, and where quality is hardest to judge.
Discuss a program Reasoning is being developed by Lawtrades.