Engagements
Focused work. Clear deliverables.
A review of one AI use case, a model choice or a deployment decision gives your team a practical place to start. Each engagement has an agreed scope, fee and handover.
AI governance readiness review
Bring clarity to the responsibilities, controls and evidence around one AI system or use case.
The work
Review the intended use, ownership, data flows, existing controls and supplier evidence. Identify the governance questions that need attention and the people who need to resolve them.
Your handover
A written assessment, a prioritised gap list and an action plan with suggested owners and dependencies.
AI model evaluation
Compare a proposed model with your current approach against the work it needs to do.
The work
Agree representative tasks and acceptance criteria. Evaluate the proposed configurations, data boundaries, licence terms, deployment requirements and operating costs.
Your handover
An evaluation record, a comparison of the options and a recommendation with clear assumptions and any further testing required.
Safe adoption of global AI models
Embedded AI feasibility
Establish what an AI workload needs from its hardware and operating environment before committing to deployment.
The work
Review the task, sensors, data and hardware constraints. Assess performance, connectivity, power, thermal limits and maintenance needs on the proposed architecture.
Your handover
A feasibility assessment, deployment requirements and a test plan for the next stage.
Embedded AI and computer vision
Continuing the work
Where implementation or continuing support is useful, we agree a separate scope. The first review gives you the findings needed to make that decision.
A useful place to start
What decision is ahead of you?
Tell us what you are building, adopting or trying to understand.