Applied AI
Evaluation harness & guardrails
The test suite for a probabilistic system, covering correctness, safety, regression and drift.
4–8 weeksTypical duration
The problem this solves
You cannot change the prompt, the model or the retrieval without a quiet fear that something else got worse.
What you receive
Artefacts you can hold, and that you can accept or refuse — never a list of activities.
- An evaluation set built from your real traffic, with the grading criteria written down
- An automated evaluation run on every change, reporting per-category deltas
- Guardrails for the failure modes that actually matter to you, tested rather than assumed
- A regression gate, so a change that degrades a category cannot ship silently
Also in applied ai
Private LLM platform
Model serving on your own accelerated hardware, with routing, quotas, caching and observability, so the data never leaves.
Retrieval-augmented knowledge systems
A retrieval layer over your corpus, with ingestion, chunking and evaluation treated as engineering rather than as configuration.
Agentic workflow engineering
Multi-step agents with tools, bounded autonomy, human checkpoints and a full audit trail of what the agent did and why.