Evaluation & Reliability
Eval harnesses, regression suites, and monitoring so your AI behaves tomorrow the way it did in the demo: BFCL, TAU-bench, and DeepEval harnesses, multi-LLM comparison, hallucination reduction. Built into everything we ship; available standalone.
- PRACTICE NO.
- 09
- PRINCIPALS
- PA · NB
- CASES ON FILE
- 00
- FIELD
- AI / ML INFRASTRUCTURE · SAAS & DEVELOPER TOOLS · CONVERSATIONAL AI · NLP & DOCUMENT AI
PROBLEM SPACE
The discipline that makes AI safe to run in production.
Eval harnesses, regression suites, and monitoring so your AI behaves tomorrow the way it did in the demo: BFCL, TAU-bench, and DeepEval harnesses, multi-LLM comparison, hallucination reduction. Built into everything we ship; available standalone.
WHAT WE DELIVER
- 2.1eval harnesses
- 2.2multi-LLM benchmarking
- 2.3hallucination reduction
- 2.4monitoring & alerting
- 2.5CI gates for AI behavior
HOW IT SHIPS
Systems in this practice run as a loop, not a launch. Inputs are evaluated, routine outcomes execute automatically, and anything consequential holds at a review gate where a named person clears it with context attached. Every decision (human or automatic) lands in the audit trail.
PROOF
NO PUBLIC CASE FILE · FIGURES SOURCED FROM ENGAGEMENT RECORDS
WHO BUILDS IT
ADJACENT
Tell us the use case. One call is enough to scope whether there is a fit, and what it takes to ship.
Start a brief