AI Transformation
Most AI pilots stall somewhere between a promising demo and something a business can depend on. We start with your workflows, find the places AI genuinely earns its keep, and build those into systems your team can run and your auditors can accept.
01
We map the real workflows and the data behind them, then rank opportunities by payoff against effort. Some honestly come back as “not yet”.
02
One narrow use case, built properly, with evals and a cost model — so what you get is a decision, not an impression.
03
Into the systems people already use, with the access control and audit trail that keep it there. Your team is trained to run it.
What we build
New · Ecommerce platform
Commerce, without the plugin sprawl.
Catalog, pages, pricing, promotions, search, orders, loyalty, gift cards and returns on one connected core — with APIs, scripts and webhooks when your business needs something special. Built by the team behind some of the UK’s largest ecommerce platforms.
Flagship · AI agent platform
One agent. Every part of your enterprise.
Embedded in your product, or standalone across your org — handling support, operations and transactions with the isolation, audit trail and access control enterprise deployments demand.
Industries
Sectors
Company size
Same engineering standard at every size — from one team shipping weekly to a rollout through procurement, security review, and systems that predate us.
Expertise
Production LLM features — retrieval, evals, guardrails, and a cost model that survives real traffic. Built to be measured, not demoed.
Dashboards people actually open, on numbers that reconcile.
Ingest, model, and serve — from source of truth to query in seconds.
Single sign-on, federation, and least-privilege access that survives an audit.
Threat modelling, hardening, and monitoring — designed in, not bolted on.
How we ship
Agree what the problem is, what constrains it, and what “working” actually means.
A working build in days. Seeing something real settles questions a document can't.
Types, tests, evals, threat model. Where AI-assisted output earns production trust.
Small changes released daily behind feature flags, instead of one risky launch day.
Observability, cost, and drift. AI systems fail quietly, so we measure rather than assume.
AI Governance
Some clients need the Act as their reference point, and plenty do not. When you do, we settle who carries which duty, build the controls in as we go, and hand over the evidence at the end. When it is not, we build to whatever standard does apply.
Academy
Taught in-house by the engineers who ship our products, on real systems rather than exercises. Every track is traditional software engineering with a focus on AI: the fundamentals first, then the half of the job now done alongside a model — building with it, and verifying what it produces. It's also how we hire.
Testing that catches what matters — plus evals for LLM features and review of AI-written code.
Ship a feature end to end — schema, API, interface, deploy — with AI in the toolchain and in the product.
Pipelines, observability, and the on-call habits that go with them, models in production included.