Overview
Applied AI with an evaluation harness, a cost model and a person in the loop.
We begin by killing the use cases that will not pay back. What survives goes through a short proof of value on real data, with a measured baseline and an honest cost per transaction before anyone commits to building.
Production means evaluation sets, drift detection, versioned prompts and retrieval, and a documented escalation path to a human when confidence drops.
What the engagement covers
Use-case triage
Scored on value, feasibility, data readiness and unit economics.
Document intelligence
Extraction and classification across contracts, claims and invoices.
Grounded assistants
Retrieval over your own corpus with citation and confidence thresholds.
Forecasting and optimisation
Demand, pricing and scheduling models in the operational loop.
MLOps
Reproducible training, model registry and automated evaluation gates.
AI governance
Model cards, bias testing, audit trails and human review by design.
Typical results
How we run it
Survey
Two to four weeks measuring the real baseline: cost, reliability, delivery speed and where the friction actually sits.
Plot
A target architecture and a sequenced roadmap where every stage carries its own business case and can stand alone.
Launch
Two-week iterations with working software at the end of each, shipped through the pipeline we are building with you.
Sustain
Managed running against published targets, handed back to your team whenever they are ready to hold it.
Questions we get
All services
Not sure which one?
Describe the problem in three sentences. A practice lead replies the same day with an honest read on whether we are the right call.
Talk to a leadNext step
Ready to plot the trajectory?
A ninety-minute working session with our practice leads. No deck — we look at your actual constraints and tell you where we would start.