AI / GenAI Engineering
Eval harnesses and continuous evaluation
Domain-specific evals that gate every deploy — no vibes-based shipping, no silent regressions.
The problem
Sound familiar?
- 01Nobody knows when the model regresses; manual spot-checking is the norm.
- 02Public benchmarks aren’t your use case; scores look great, behaviour ships poorly.
- 03A new model from the provider should help — but you can’t prove it.
What we deliver
Concrete outputs.
Golden dataset curated against your real questions and expected answers
Scoring rubric (binary, rubric, LLM-as-judge) sized to your task
Automated runners using Ragas, DeepEval, or a custom harness
CI integration: a regression blocks merge to main
Eval-drift dashboards and weekly review
Human-in-the-loop review queue for novel cases
Methodology
How we run it.
Phase 1
Define
Task, success criteria, scoring approach.
Phase 2
Curate
Golden dataset, edge cases, refresh cadence.
Phase 3
Automate
CI integration, dashboards, drift alerts.
Related capabilities
What pairs well with this.
- AI / GenAI Engineering
LLM applications and RAG systems
Retrieval-augmented generation pipelines that ground LLMs in your data with citations, audit trails, and a private deployment option.
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ML pipelines and MLOps
Model lifecycle done properly — versioned, evaluated, monitored, retrained on a schedule.
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AI product development
End-to-end AI product builds — UX, model, retrieval, eval, and ship. Available on the partnership model.
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Get started
Ready to scope eval harnesses and continuous evaluation?
Book 30 minutes — we’ll tell you honestly whether the partnership model fits or whether an SOW is the better path.