Benchmark frameworks for enterprise AI workloads
Evaluation harnesses measuring retrieval relevance, hallucination rate and latency, so AI changes ship on evidence rather than impressions.
Capability example. This describes the kind of work we are equipped to do. It is not a specific client project.
- Services involved
- AI developmentTechnology consulting
The problem
Teams ship prompt and model changes with no way to tell whether quality moved, and regressions surface only after users complain.
Where it applies
AI reliability & evaluation
How we approach it
- 01
Build a labelled evaluation set representative of real production queries
- 02
Score retrieval relevance, answer accuracy and hallucination rate per change
- 03
Track latency and cost alongside quality so trade-offs stay visible
- 04
Wire the harness into CI so regressions block the release
What this is designed to change
- Quality regressions caught before release
- Model and prompt changes compared on the same benchmark
- Latency and accuracy trade-offs made explicitly
Typical stack
- Ragas
- DeepEval
- TruLens
- Python
- GitHub Actions
Next
All work
Data platforms & decision intelligence
Ingestion pipelines powering real-time executive analytics
High-volume data ingestion, modelled into analysis-ready datasets behind live executive dashboards.
See how we approach it