LLM Evaluation and Observability Services

Make AI behavior measurable before and after release. Ampity designs evaluation, guardrail and observability systems for LLM, RAG and agentic workflows operating...

What this service is for

This service is for teams that have moved beyond a convincing demo and now need evidence that an AI system is useful, safe enough for its context, cost-aware and observable in production. Evaluation is designed around the task and failure modes, not a single universal score.

Typical situations

• A prototype looks good, but release criteria are subjective.
• RAG answers vary and the team cannot separate retrieval failures from generation failures.
• Model, prompt or data changes ship without a regression test.
• Production teams need visibility into quality, latency, cost, safety and human escalations.

Included

• Task, risk and failure-mode definition
• Representative evaluation dataset design
• Automated and human evaluation workflows
• RAG retrieval and answer-quality evaluation
• Guardrails, escalation paths and release gates
• Production traces, quality signals, latency and cost monitoring

What you receive

• Evaluation strategy and failure taxonomy
• Versioned evaluation dataset and scoring rubric
• Regression and release-gate workflow
• RAG and agent trace design
• Guardrail and human-escalation design
• Production quality, latency and cost dashboard specification