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Engineering

Product teams, not staff augmentation.

Small senior squads embedded with your people and accountable to your metric. We build on our own products, so you inherit connectors, evaluation and policy instead of paying us to rebuild them.

Engagement 01
Feasibility sprint

A few weeks to answer one question honestly: can this work here, at what cost, with what risk. You end with a working demo and a memo you can take to an investment committee.

Duration2 to 4 weeks
Squad2 people
OutputProof of concept demo
Engagement 02
Pilot deployment

The concept meets real users, real data and real load. A limited group works with it daily while we measure what it changes and what it breaks.

Duration4 to 8 weeks
Squad5 to 6 people
OutputLive pilot, measured results
Engagement 03
Production sprint

Fixed scope and price to a governed system live in your estate against an agreed metric. The most common way clients move from pilot to real operations.

Duration8 to 12 weeks
Squad5 to 6 people
OutputLive system, IP transferred
Engagement 04
Run with you

Managed operations and continuous improvement against an SLO while your team takes over. Ends on a date you choose, not one we negotiate.

DurationQuarterly
SquadShared pool
OutputSLO report, capability transfer
Capabilities

What we are asked to do most.

01
AI strategy and value mapping
A ranked portfolio with baselines and kill criteria, not a hundred-slide vision deck.
02
Data and platform foundations
Lakehouse, vector store, lineage and access control done once, properly, so every later use case is cheap.
03
Agentic system engineering
Multi-agent workflows with tool contracts, evaluation harnesses and human checkpoints built in.
04
Legacy modernisation
Understand, test, translate and retire, with the business still running throughout.
05
AI governance enablement
Policy, model cards, DPIAs and the operating model and committee cadence to sustain them.
06
Managed AI operations
Round-the-clock run, tune and cost optimisation against an agreed service level objective.
07
Custom application build
The product around the model: web and mobile interfaces, APIs and integrations, built to your stack and handed over with the source.
08
Use case discovery and triage
A working shortlist from the ideas already circulating in your business, scored on data readiness, value and risk before anyone commits budget.
Practices

Three practices, one delivery standard.

Most engagements draw on more than one of these. They share the same squad model, the same evidence trail and the same handover.

Cloud & Modernisation

Move estates to AWS, Azure or GCP without the business noticing, and leave behind infrastructure that AI workloads can actually run on. Migration strategy, execution and cost governance in one engagement.

Readiness assessment, TCO and migration sequencing
Containerisation and microservices decomposition
CI/CD, Terraform and internal developer platforms
FinOps built into the architecture, not retrofitted
Know more
Governance enablement

Governance your teams can run without us.

Policy that matches how the system actually behaves, documentation an auditor accepts, and a committee cadence that survives the first busy quarter. We build it alongside delivery, not as a report at the end.

See our responsible AI commitments →
Policy that maps to controls
Each line of policy is tied to something the platform enforces or logs, so a statement of intent is never the only evidence you have.
Model cards and DPIAs
Purpose, population, limitations and known failure modes written once, kept current as the system changes, and reviewed by a named owner.
Operating model and cadence
Who approves a change, who is called when a system misbehaves, and how often the committee actually meets. Roles, not job titles.
Audit-ready evidence trail
Decisions, evaluations and incidents stored where an assessor can retrieve them without a scramble across three teams.
Method

The 7D method.

A repeatable path from business question to governed production system. Every phase has an exit artefact your architecture review board can actually read.

D1
Discover

We start with the P&L line, not the model. Two weeks inside your workflows to find where intelligence actually changes an outcome, and where it would only add latency.

Exit artefactOpportunity map, ranked by value over effort
D2
Define

Ambiguity is the most expensive material in AI delivery. We fix scope, success metric, data boundary and the regulatory frame before anyone writes a prompt.

Exit artefactSolution brief and governance charter
D3
Design

Architecture and experience designed together: retrieval strategy, agent topology, fallback behaviour and the interface a real employee will use under pressure.

Exit artefactReference architecture and clickable prototype
D4
Develop

Built on our own products, so you inherit connectors, evaluation harnesses and policy engines instead of rebuilding them. Weekly demos against the real metric.

Exit artefactEvaluated build in your environment
D5
Deploy

Into your cloud, your identity provider, your change process. Progressive rollout with shadow mode first, so the system earns its traffic.

Exit artefactProduction release with rollback plan
D6
Defend

Continuous assurance. Drift, jailbreaks, cost spikes and regulatory change are monitored as first-class production signals, not discovered in an audit.

Exit artefactLive assurance dashboard and audit pack
D7
Drive

Adoption is the last mile most programmes lose. We enable your teams, tune against real usage, and hand over ownership with the code and the confidence.

Exit artefactValue report and capability transfer
The squad

Six people who have shipped this before.

No pyramid. No offshore handoff at week six. The people in the kickoff are the people in production, and your engineers sit inside the squad from day one so the capability stays when we leave.

Solution architectOwns the target architecture and the honest answer on feasibility.
ML engineerRetrieval, evaluation, model selection and the quality bar.
Platform engineerDeployment, identity, observability and cost control in your estate.
Product designerThe interface a real employee uses under time pressure.
QA engineerTest coverage, regression suites and the evidence a release is safe to ship.
Delivery leadScope, the metric, and the conversation with your sponsor.

Twelve weeks from kickoff to something real in production.

Fixed scope, fixed price, agreed success metric. If we cannot get you there in twelve weeks we will tell you in the feasibility memo rather than on week eleven.

Scope a sprint