A complete process map
Include the preparation, checking, rework and follow-through, not only the generated output.
A useful answer is only
part of the workflow.
A focused engagement to map, test and improve one AI-assisted workflow, with the people, information and handoffs considered together.
Discuss your workflowConnect an idea to the work.
The demo works. The real task is more complicated. Information arrives in different forms, exceptions need judgement, and the answer still has to reach the next person. The sprint starts there.
For a team with a specific task to improve and someone accountable for the process after the demonstration.
Capability you can
put to work.
A test with a purpose
Agree what the pilot needs to establish and what would make the idea worth continuing.
An honest handover
Document what works, what remains uncertain and who would own a live workflow.
A clear structure.
Room for your context.
We agree the specific work and its boundaries before we begin. Here is the method that shapes the engagement.
01Map the real task
Follow the work from input to final use. Identify sources, permissions, review effort and exceptions.
What stays with youA workflow map and measured starting point.
02Design a bounded test
Choose the smallest useful scope. Agree success, revision and stop criteria before testing.
What stays with youA test plan and acceptance criteria.
03Test the difficult cases
Review outputs and handoffs with the people responsible for using them. Record what fails as carefully as what works.
What stays with youAn evidence log and design recommendations.
04Decide and hand over
Choose whether to expand, revise or stop. Make ongoing ownership and remaining work explicit.
What stays with youA handover document and next-step decision.
The work should leave
something behind.
Clear artefacts, shared understanding and a practical next step. Not a folder of slides that nobody opens again.
- A discovery session around one workflow
- A map of inputs, reviews and handoffs
- A defined pilot scope and exclusions
- A practical evaluation approach
- Documentation of the observed findings
- Recommendations for implementation or a simpler alternative
Before we begin.
Do we have to know exactly what to build?
You need a meaningful task, not a finished technical specification. Discovery helps distinguish a useful problem from a solution looking for one.
Will you work with our existing systems?
We begin with your current workflow and constraints. Specific integrations depend on access, permissions and the agreed scope.
What happens if AI is not the best answer?
We will say so. A simpler process, better source information or conventional automation may be the more useful outcome.
A clear scope, from the start.
Integrations, production hosting, data migration, licences, maintenance and security approvals are included only when explicitly scoped. A prototype is not represented as a production deployment. We agree the boundaries before work begins.
Start with your work.
Tell us who is involved, what matters and what you would like to make better.