Evaluation before deployment
Golden datasets, regression suites and human review loops that catch drift early.
03 — Applied AI
AI & Intelligent Automation
AI that survives contact with production.
A demo that works on ten documents is not a system that works on ten million. The gap is retrieval quality, evaluation, latency budgets, cost per request and the unglamorous work of knowing when the model is wrong. That gap is where we spend our time.
We start every engagement with an evaluation harness built from your real data and your real failure cases. Without it, nobody can tell whether a change made the system better or simply different.
What you get
- AI opportunity assessment & feasibility study
- Evaluation harness and golden dataset
- Retrieval or model pipeline in production
- Guardrail, logging and review tooling
- Team enablement and operating playbook
How we approach it
The parts that usually go wrong.
And what we do differently so they do not.
Retrieval you can trust
Chunking, hybrid search, reranking and citation so every answer traces to a source.
Guardrails and fallbacks
Input filtering, output validation, deterministic escape hatches and audit trails.
Honest unit economics
Cost and latency per request modelled before you commit to a vendor or a tier.
Proof
All case studies
Where this work shipped.
Questions
Before you ask.
Your data stays in your tenancy. We deploy inside your cloud account or on infrastructure you control, and we can run entirely on self-hosted open-weight models where regulation or policy requires it.
Often it is not, and we will say so. The assessment phase compares an AI approach against rules, search and process change on cost, accuracy and time-to-value. Roughly one in four assessments ends with us recommending something simpler.
Have a problem in this area?
Bring us the messy version. We are more useful before the requirements document exists than after it has been signed off.