Improving AI in production
Expert data, reinforced learning, and evaluation from one team
- Expert data
- Human feedback
- Evaluation
Data and evaluation
We run the full post-training loop for AI labs and enterprises.
Every stage of
post-training.
- 01 Generate: experts write the data
- 02 Label: the rubric is applied
- 03 Review: checked against the rubric
- Training-ready data
Golden datasets, SFT, and the fine-tuning runs that use it. We measure the improvement against a baseline.
Expert data →RLHF, reward models and RL environments that teach agents to finish real tasks, not just answer questions.
RL →Benchmarks and red teaming show where a model or agent fails before users find out.
Evaluation →What we deliver
Our process
- 01Scope
We agree the task, the rubric and the pass and fail criteria with you before any work begins.
- 02Build the data
Domain experts generate, label and review the data your model trains on.
- 03Tune on judgement
Experts compare, rank and correct model outputs. Their rankings and corrections go back into training.
- 04Test
Benchmarks and red teaming show where the model still fails.
↺What testing finds shapes the next round of data.
Why one team for all three
Each line is stronger when the same people see the whole loop.
One pool of experts
The same vetted engineers and domain experts write the data, judge the outputs and design the tests.
One definition of correct
The rubric that labels the data also ranks the outputs and grades the evaluation.
No hand-offs
Failures go straight back into data and feedback, with no vendor in between.