Evaluation & Benchmark Design
Measure model performance against the metrics that matter for your business — not just academic benchmarks.
A Complete Evaluation Framework
Academic benchmarks tell you how a model ranks. Business benchmarks tell you if it's ready for production.
What we deliver
Standard benchmarks tell you how a model performs on academic tasks. Business benchmarks tell you whether the model is ready for your production environment. We design evaluation frameworks that measure what actually matters: accuracy on your tasks, safety in your context, and reliability under your operating conditions.
Our evaluation systems are automated, repeatable, and integrated into your development workflow so every model change is tested before it reaches production.
Key deliverables
- Custom evaluation dataset construction for your domain and tasks
- Automated scoring pipelines with business-relevant metrics
- Regression test suites for model and prompt changes
- Human evaluation workflows and annotation guidelines
- Red-teaming and adversarial testing for safety
- Benchmark dashboards and performance trend reporting
Real-Life Use Cases
Evaluation frameworks that caught problems before they reached production.
Catching Prompt Regressions
A conversational AI company had no systematic evaluation. A prompt change that improved one use case silently broke three others. We built a 2,000-case regression suite. The next 15 prompt changes were all validated before deployment — catching 4 regressions that would have reached users.
Clinical AI Safety Evaluation
A clinical decision support tool needed safety validation before hospital deployment. We designed a red-teaming suite with 500 adversarial medical scenarios. The evaluation revealed 3 failure modes that required model changes before the tool was cleared for clinical use.
Content Quality Benchmarking
A media company was evaluating 5 LLMs for content generation. We designed a domain-specific benchmark with 300 test cases and human preference scoring. The evaluation identified the best model for their use case — which was not the most expensive one.
Product Description Accuracy
An e-commerce platform's AI product description generator had a 12% factual error rate on technical specifications. We built an automated fact-checking evaluation pipeline. After model improvements guided by the evals, the error rate dropped to 1.8%.
Know your model is ready before you ship it
We'll design the evaluation framework that gives you confidence in every model change.
Design Your Evaluation Framework