01
How we approach it
Most AI programmes stall in the same place. A model demonstrates well, then meets real data, real permissions and real cost ceilings. We start at that end of the problem: what the system must do reliably, on which data, under whose governance, at what unit economics.
That means the data platform is part of the AI engagement, not a later phase, and it means you get a number to hold us to before we write production code.
All five practices →Evaluate before we build
Offline evaluation sets and acceptance thresholds agreed in week one.
Own the data path
Lineage, quality tests and access control shipped alongside the model.
Hand over something operable
Runbooks, dashboards, cost budgets and a team trained to run it.
02
AI & Data Solutions
Three offers, one engagement model. Each one assumes the others exist.
Systems that write, summarise and answer
Retrieval-grounded assistants, document processing and content pipelines built on your own corpus, with citations and refusal behaviour you can audit.
Read more →Agentic AISoftware that takes the next step
Tool-using agents scoped to a single business process, with explicit permissions, human checkpoints and a full trace of every action taken.
Read more →Data PlatformsThe layer everything else depends on
Lakehouse and warehouse builds, pipelines, semantic models and business intelligence, governed, tested and documented.
Read more →03
Supporting practices
Where AI work needs infrastructure, control, an application around it or a team to run it, these are the same people.
AWS, Google Cloud, Azure
Landing zones, migration, Kubernetes platforms, infrastructure as code and cost control on all three hyperscalers.
CybersecurityRisk, identity and AI safety
Cloud security posture, identity and access, assessment and response, extended to cover model and agent risk.
Custom SoftwareFully bespoke engineering
Web and mobile products, integration work, ERP and CRM extension, and the interfaces your AI systems are used through.
Managed ITConsulting and capacity
IT management consulting, support operations and staff augmentation for teams that need senior hands quickly.
04
Why clients keep us
One team, whole stack
Data engineers, ML engineers, cloud and security people in the same delivery group. No handoff tax between vendors.
Commercially honest
Fixed-scope framing phase, a written acceptance threshold, and a recommendation to stop if the numbers do not clear it.
Built to be audited
Evaluation results, traces, lineage and access controls are deliverables, the things procurement and risk actually ask for.
Sized for you
Enterprise programmes, single automations for mid-market operators, and embedded build teams for founders.
Organisations we build and run systems for, across banking, healthcare, retail, telecom and the public sector.
Get in touch
Tell us what the system has to do.
We will tell you whether it is an AI problem, a data problem or neither. That conversation is free and usually short.