Service Line 01
AI
Take control of your AI agenda. We build, govern and operationalise AI inside your stack: owned-weights models, auditable evaluations and real production traffic, not slideware.
Overview
AI only earns its place when it runs in production, clears an audit, and keeps working after the consultants leave. That is the bar we build to. We treat AI as an engineering discipline, not a science experiment, and we hand you a system your own team can operate and extend.
The problem
Most enterprise AI never leaves the pilot.
Proofs of concept demo well and die quietly. The gap is rarely the model. It is governance, evaluation, observability and the operational practice that lets a regulated business trust a model in front of customers. We close that gap, and we hand the keys back.
Signs it's time
You might recognise these.
If two or three of these feel familiar, this is the conversation to have.
You have impressive demos but nothing live in front of customers.
Risk and compliance cannot sign off because nothing is auditable.
Your data is too sensitive to send to a third-party API.
Models that did ship have quietly drifted and nobody noticed.
What we deliver
AI that delivers measurable business value.
From owned-weights model development to enterprise agents, we ship AI you can audit, defend to a regulator, and run without us.
AI & ML Model Development
Owned-weights models trained and fine-tuned on your data, with reproducible pipelines and evaluation baked in from day one.
AI Security and Governance
Model cards, audit trails, red-teaming and policy guardrails so AI clears risk, compliance and the board.
Enterprise AI Agents
Task-scoped agents wired into your systems of record, with human-in-the-loop controls and full traceability.
AI/ML Ops
Evaluation, observability, drift detection and rollback: the production plumbing that keeps models honest after go-live.
What you keep
What you walk away with.
- 01A working, owned-weights model deployed in your cloud
- 02An evaluation harness your team can run and extend
- 03Model cards, audit logs and governance documentation
- 04Runbooks, observability dashboards and rollback procedures
- 05A trained internal team that owns the system
How it runs
Discovery to handover.
Every engagement follows the same path, written down, so you always know where it is, and where it ends.
- 01
Discovery
A senior engineer maps the use case, data reality and risk surface. You leave week two with a written architecture and a costed roadmap.
- 02
Reference build
We build a working slice against real data and a real evaluation harness. Not a demo, a foundation you can extend.
- 03
Production
Hardened, governed and observable. Deployed into your cloud, behind your controls, with the eval gates that let it ship.
- 04
Handover
Your team runs it. We document, train and transfer ownership. The success metric is that you no longer need us.
Outcomes
Built to move a number that matters.
5
models in production in under 8 weeks
62%
fewer fraud incidents post go-live
100%
auditable, owned-weights by default
FAQ
Common questions.
- Do you use owned-weights models or third-party APIs?
- Owned-weights by default for regulated workloads, so you keep control of data, cost and auditability. Where a hosted model is the right call, we make that choice explicit and reversible.
- How do you handle AI governance and compliance?
- Every model ships with model cards, evaluation baselines, audit logging and policy guardrails designed to clear internal risk and external regulator scrutiny.
- Can you work inside our existing cloud?
- Yes. We deploy into your cloud footprint and your controls. You own the weights, the pipelines and the infrastructure.
Keep exploring
The other service lines.
Let's talk
Ready to talk about AI?
A senior engineer on the call from day one. Two weeks to a written architecture and a working reference build.