AI Evaluation & Auditing
Measure your AI before you trust it
Systematic evaluation of models and agents: benchmarks on your data, automated judges and regression tests that turn confidence into evidence.
Model Benchmarking & Selection
We compare models — commercial APIs and open source — on your cases and your data, not public benchmarks. You get an evidence-backed recommendation: quality, latency and cost per query for your real scenario.
Golden Sets & Automated Judges
We build reference sets validated by your team and automated evaluators (LLM-as-judge) calibrated against human criteria, to measure hundreds of cases in minutes.
AI Regression Testing
Every prompt, model or version change runs against the full eval suite before deployment. Nothing reaches production if it degrades quality — just like tests in software.
Compliance & Bias Auditing
Explainability, bias monitoring, drift and traceability for regulated industries. Integrates with watsonx.governance for enterprise-grade auditing.
🔗 Related services: Evaluation is the quality control of AI Agents and the heart of Harness Engineering. Our AI Strategy Consulting defines what to measure; we measure it.
Process
How We Work
We Listen
Your context and objectives.
We Design
Clear scope and costs.
We Execute
Short sprints, frequent demos.
We Support
Continuous support and evolution.
FAQ
Frequently Asked Questions
Why isn't manual testing enough?
Manual testing doesn't scale and is never repeated the same way twice. An eval suite runs hundreds of cases in minutes, is reproducible, and catches regressions the human eye misses.
What is a golden set?
A curated set of cases with the expected correct answer, validated by your team. It's the yardstick every version of your AI system is measured against.
Is this useful if we already have AI in production?
It's the best time: we audit its real performance, build the eval suite from real cases, and leave it running in your deployment pipeline.
What tools do you use?
Langfuse for traces and evaluations, promptfoo and our own frameworks; in enterprise environments, watsonx.governance for lineage, bias monitoring and compliance.