AI Data & Model Intelligence
LLM Evaluation
Automated benchmarks rarely capture how a model performs for real users. We design evaluation rubrics, run blinded human review and report results in a form your team can act on.
Challenges
What we help solve
- Benchmarks that do not reflect product use
- Subjective judgments with low agreement
- Slow feedback loops between releases
- Comparing models across versions fairly
Capabilities
Structured human evaluation of language model outputs.
Rubric design
Criteria for helpfulness, accuracy, instruction-following, tone and safety.
Side-by-side comparison
Blinded pairwise evaluation across models, prompts or system versions.
Domain expert review
Evaluation by contributors with relevant subject knowledge.
Actionable reporting
Error taxonomies and examples, not just aggregate scores.
Delivery approach
How the work runs
- 1Discover
- 2Define
- 3Design
- 4Build
- 5Validate
- 6Deliver
- 7Improve
Deliverables
What you receive
- Evaluation rubric
- Scored evaluation dataset
- Findings report with error taxonomy