AI Observability, LLM Monitoring & Governance
LLMOps consulting for AI observability, LLM monitoring, evaluation and guardrails. Review production answer quality, operating failures and cost evidence.
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
Frequently asked questions
Can LLM evaluation be fully automated?
Some checks can. Production evaluation usually combines deterministic tests, sampled human review and model-assisted scoring. The mix depends on consequence, ambiguity and review cost.
What should be monitored for a RAG system?
At minimum, retrieval relevance, source coverage, groundedness, answer usefulness, latency, cost and failure patterns. The right thresholds depend on the task and user risk.
Is observability the same as logging prompts?
No. Useful observability connects inputs, retrieval, model configuration, tool calls, outputs, feedback, cost and release version while applying the required privacy and access controls.
What are LLM guardrails?
Controls that check and constrain model inputs and outputs, such as blocking unsafe content, enforcing formats, limiting topics and protecting sensitive data.
How do you reduce LLM costs in production?
By monitoring usage, choosing the right model per task, caching, shortening prompts, limiting retrieval and routing simple requests to smaller models.