Domain-Expert AI Evaluation and Validation
Realistic scenarios, synthetic data and expert review, from people who do revenue cycle and credentialing work every day.
How the Work Runs
1. Scenario Design
We build realistic scenarios and synthetic data for the domain you are training on.
2. Model Run
The scenarios go through your model, in your environment.
3. Expert Review
Practicing specialists review the responses against how the work is actually done.
4. Errors Found and Corrected
Errors, hallucinations, omissions and weak reasoning are identified, classified and answered with the ideal response, not only a score.
5. Structured Feedback
Everything returns in a structure your training pipeline can consume.
Who It Is For
Teams Training Domain Models
Where general-purpose data does not cover the domain, and the gap shows up as confident, fluent errors.Teams Running Evaluations
Where you need a specialist judgment on whether an answer is correct, not only whether it reads well.Teams Building Synthetic Data
Where the real data cannot leave its environment and a realistic substitute is needed.
Reviewed by People Who Do the Work
Our reviewers are billers, coders and credentialing specialists who run this work for US practices. In this domain the difference between a fluent answer and a correct one is usually a payer rule, a documentation requirement or the order steps have to happen in — and that is a judgment a generalist reviewer cannot make.
- Reviewers are practicing specialists, not generalist annotators.
- Feedback is written as a corrected response, not a rating.
- Scope, structure and output format are agreed before work starts.
Tell Us What You Are Building
Talk to Our Domain Team
Tell us the domain, the model and what a correct answer looks like in your context.
- Scenario Design
- Expert Review
- Synthetic Data
- Error Classification
- Structured Feedback