How do you keep our data safe in AI systems?

Data-boundary design comes first: what leaves your environment, what never can, and what must be redacted or tokenised in between. We deploy within your cloud where required, contract for zero-retention API tiers, and log every model interaction for audit.

What does an AI engagement cost and how long does it take?

A focused first phase — one workflow, production-grade — typically runs 8–16 weeks. Costs follow the blueprint from the Define phase. We deliberately start narrow: one workflow that works beats ten pilots that don't.

Practice

AI & Intelligent Systems

Strategy, custom LLM platforms, autonomous agents, and the MLOps backbone — AI engineered with the guardrails enterprises need and the outcomes boards expect.

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Services in this practice: 6Services in this practice
Same delivery method: 5 phasesSame delivery method
Where every engagement ends: ProductionWhere every engagement ends

The problem

Every enterprise now has AI ambitions; very few have AI in production doing consequential work. The gap isn't models — it's engineering: data plumbing, evaluation, guardrails, cost control, and integration into the systems where work actually happens. That engineering is our practice.

How we approach it

We treat AI like any other mission-critical system: discovery first, measurable success criteria, phased delivery, and a Prove phase your risk and compliance teams can interrogate. Demos are easy. We build the version that still works in month eighteen.

Capabilities

6 services in this practice

Each one is a full capability with its own page — the method, the stack, and the questions clients ask before they commit.

AI Strategy & Roadmap

A defensible plan for where AI creates value in your business — and where it doesn't.

Custom AI & LLM Development

RAG platforms, copilots, and document intelligence built on your data, inside your boundaries.

AI Agents & Autonomous Workflows

Agents that execute multi-step business processes — supervised, audited, and integrated with your systems.

ML & Predictive Intelligence

Forecasting, scoring, and anomaly detection embedded where decisions happen.

Computer Vision & Speech

Cameras and microphones turned into structured, decision-ready data streams.

MLOps & AI Infrastructure

The platform layer that keeps AI systems reliable, measurable, and affordable at scale.

Before you ask

Questions we hear about ai & intelligent systems

Whichever the problem and your constraints demand. Frontier APIs win on capability-per-effort; self-hosted open-source wins on data boundaries and unit economics at scale. Most enterprise architectures we ship blend both behind an abstraction layer, so you're never locked to one vendor's pricing.

Other practices

Next step

Bring us the problem. We will bring the architecture.

A discovery call takes forty-five minutes. You leave with our read on the problem, the shape of the system we would propose, and a straight answer on whether we are the right team for it.

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An engineer on the call, not an account manager
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