Your team shipped an LLM feature on demo-quality prompts. Now hallucinations, drift, and edge-case failures show up in production, and you have no systematic way to catch them before release.
Contracting
LLM Evaluation Consultant: Gate AI Output Before It Reaches Production
I build eval frameworks, AI agent orchestration pipelines, and Production LLM QA gates before models ship to users.
LLM evaluation consultantAI agent orchestrationProduction LLM QALLM eval frameworksAI engineering consultant
01
The Problem
02
What's at Stake
Every bad model response erodes user trust. Manual spot-checking does not scale. Regressions slip through because evals live in spreadsheets, not in your CI pipeline.
03
The Solution
I build Production LLM QA into your delivery flow: structured eval suites, AI agent orchestration with guardrails, and release gates that block regressions. You ship AI features with the same rigor you apply to backend services.
What You Get
- 01Eval framework design: datasets, scoring rubrics, and regression baselines
- 02AI agent orchestration workflows with tool use and human handoff points
- 03CI-integrated eval gates for pre-merge and pre-release checks
- 04Production monitoring hooks for drift detection and quality alerts
- 05Documentation and runbooks for your team to extend eval coverage
04
Stack
PythonTypeScriptLLM APIsEval harnessesCI/CDAWS
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