LLM Observability, Evals & Guardrails
We treat evaluation as one part of observability, alongside traces of every request and production monitoring, and build evaluation frameworks with offline golden sets, automated regression runs and LLM-as-judge scoring calibrated against human review. Guardrails cover prompt injection, PII, scope and tone, with dashboards for structured human review.
Deliverables you can hold us to
- Golden test sets with human-validated labels
- Automated regression harness and CI gates
- LLM-as-judge rubrics calibrated to human reviewers
- Input and output guardrails with monitoring
Typical situations
- 01A model upgrade quietly changed answer quality
- 02Ground-truth labels you suspect are wrong
- 03Compliance needs evidence that outputs are controlled
Part of Generative AI & LLM Engineering
RAG, fine-tuning, evals and LLM apps built for production.
Explore the full offeringHow a typical engagement runs
Scoped up front, shipped weekly, measured against the metric we agreed.
Discover
A 60-minute call and a deep-dive sprint that end in a scored opportunity map and a baseline.
Design
Architecture and stack chosen with a decision matrix and a model bake-off on your data.
Build
Weekly releases into your environment with evals, guardrails and traces from sprint one.
Scale
Hardening, staged rollout, cost controls and handover so your team runs it.
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OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, Mistral, DeepSeek and other open-weight models. We pick per task based on measured quality, cost and latency on your data, and we build so you can switch later.
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