Opportunity & readiness
Review recurring tasks, accessible data, system permissions and the capacity to adopt a new workflow. Prioritise opportunities by value, feasibility and consequences of failure.
Decide / Direction before development
AI consulting & strategy.
Turn AI ideas into a practical implementation plan. We help UK businesses choose worthwhile use cases, understand their data and systems, and define what a successful first release should do.
Discuss this with usThe opportunity
Choose the right problem. Build a clear plan.
You may have a long list of ideas, a promising demonstration or pressure to adopt AI. The missing piece is often a decision: which change is useful enough to fund, feasible enough to deliver and clear enough to evaluate?
We work with the people who own the process and the people who use it. Together we map the current work, identify the expensive handoffs and check the evidence behind each opportunity. The result connects a business decision to a delivery scope.
What we can do.
Business case & roadmap
Compare configuration, existing products, conventional automation and custom AI. Make assumptions about effort, operating cost, review time and adoption visible before committing.
Technical AI due diligence
Assess an existing prototype, vendor proposal or AI product. Examine architecture, data rights, evaluations, dependencies and operating requirements, then identify the next decision and supporting evidence.
Tangible outputs
What you receive.
The exact scope is agreed around your workflow. A typical engagement can include:
- A prioritised opportunity map with selection criteria
- A workflow and data-readiness assessment
- Build-versus-buy options and cost drivers
- A scoped first release with acceptance measures
- A delivery roadmap and clear ownership
Illustrative workflow
An operations team with too many AI ideas
Map recurring work → compare opportunities → test the highest-value assumption → agree a first implementation.
An example of how this capability can fit a business process.Define what better looks like.
We agree useful measures before implementation and compare them with the current workflow.
- ↗A measurable baseline for the chosen process
- ↗A feasible scope with accessible data and systems
- ↗A clear investment decision with known dependencies
Your questions, answered.
Do we need a technical specification?
No. Start with the task, who performs it and a few examples of what makes it difficult. Discovery turns that context into a usable specification.
Will the recommendation always be custom AI?
No. A process change, an existing platform or a straightforward integration may be the better answer. We compare the options against the work that needs doing.
Can you assess a project already in progress?
Yes. We can review the implementation, test its assumptions and define the work needed to improve, release or stop it.
Useful before you start
Questions worth exploring.
What does AI implementation really cost?
A useful budget covers the whole workflow: data, integrations, evaluation, people and the cost of keeping it running.
How to choose your first AI project
Start with work you can observe, data you can access and an outcome you can measure.
A practical checklist for choosing an AI partner
Ask for evidence about delivery, integration and operation—not just a compelling demonstration.
A useful next step
Bring us the work
that should work better.
One process, one product idea, or a question about where to start.