Think. Build. Scale. Assure.
What we actually do at each stage of the AI lifecycle. Start at any of them.
01 · THINK
Find where AI will actually pay off
AI Opportunity Sprint
Best when
You know AI matters but not where to invest first.
What we do
- Discover and rank use cases by value, readiness and risk
- Assess whether your data and systems can support them
- Build the business case and a sequenced roadmap
- Set AI policy, governance and the operating model
You receive
A prioritised use-case portfolio, business case and roadmap
02 · BUILD
Turn the priority use case into a working system
Pilot to Production
Best when
You have a priority use case and need a working system.
What we do
- Design workflow, data, integrations and approvals together
- Data pipelines, document intelligence and retrieval (RAG)
- AI agents and copilots wired into core systems through APIs
- IoT and protocol integration where AI meets equipment
- Evaluation sets and test data from the first sprint
You receive
An integrated pilot connected to your systems, with success measures agreed upfront
03 · SCALE
Run it reliably, at a cost that makes sense
Managed AI Operations
Best when
You have AI in use that needs to run reliably at lower cost.
What we do
- Monitoring and observability across models and workflows
- Model and cost management as usage grows
- Guardrails and human-in-the-loop controls
- Adoption support so people actually use it
- Operate under an SLA, with regular review
You receive
A monitored production service with SLAs and continuous improvement
04 · ASSURE
Prove it can be trusted
Independent AI Assurance
Best when
You need evidence that an AI system is safe, secure and compliant.
What we do
- Structured evaluation of each AI component
- Red-teaming, tested independently of the build
- Auditability and traceability of decisions
- Security and privacy evidence, backed by ISO/IEC 27001 and 27701
- A clear remediation plan for anything that falls short
You receive
An evidence-based assurance report: evaluation, red-team findings and a remediation plan
Capability
AI capability across the stack
The same foundations support client work across industries and use cases.
| Layer | What it covers | Typical deliverables |
|---|---|---|
| Strategy | Opportunity discovery, roadmap, governance | Use-case prioritisation, business cases, AI policy and operating model |
| Knowledge | Data foundations, retrieval, evaluation sets | Data pipelines, document intelligence, retrieval-augmented generation, test datasets |
| Applications | Agents, workflow automation, enterprise integration | AI agents, copilots, IoT and protocol integration, APIs into core systems |
| Operations | Observability, cost, safety, human oversight | Monitoring, model and cost management, guardrails, human-in-the-loop controls |
See it in practice: Energy & e-mobility · Healthcare · eGovernance