Service · EVALUATIONS
Measurably more accurate, reliable AI.
AI Quality & Ops (Evaluations). Evaluation harnesses, golden datasets and regression gates that turn accuracy into a managed number.
Who this is for
Built for teams like yours.
- Enterprises running AI in production without systematic evaluation
- Teams seeing accuracy drift after model or prompt changes
- Leaders who need AI quality reported like any other KPI
What the FDE team does
Inside your stack, not around it.
- Evaluation harnesses and golden datasets
- F1, precision, recall, groundedness and hallucination-rate tracking
- LLM-as-judge plus human review
- Regression gates in CI/CD
- Cross-model benchmarking on your tasks: Claude, GPT, Gemini, Llama, Groq-hosted models
- Cost and latency optimisation
- Observability on Vertex AI, Bedrock and Azure AI Foundry
The outcomes you get
Scored, not promised.
[0.XX]→[0.XX]
F1 score on production tasks
Illustrative · placeholder
−[X]%
Inference cost via model routing
Illustrative · placeholder
0
Regressions shipped after gating (target)
Illustrative · placeholder
Frameworks & tools we bring
Model-agnostic by design.
Vertex AIAWS BedrockAzure AI FoundryLangSmithOpenTelemetryGitHub Actions
Related case study
Raising AI accuracy for an enterprise running AI in production
ReadPut an FDE pod on ai quality & ops (evaluations).
Start with an outcome audit. We’ll map the systems, measures and FDE pod needed to move from AI ambition to production impact.