30 minutes with James Rooney to discuss your team, workflow and first scope.
For organisations building an AI application, adding a useful AI feature to a product, or bringing in an embedded team to move an agreed opportunity into delivery.
Turn the idea into a specific user task, a clear first release and evidence that would justify further investment. Give product owners a decision they can act on.
Keep the requirements, difficult examples and test results together. Engineers can work against the full context while users help decide whether the result is useful.
Build in your repositories and environment, with pairing, documented decisions and an agreed handover. Ownership of the deliverables transfers on payment under the engagement terms.
Use a 30-minute call to discuss the outcome your team needs.
Talk it throughExplore the user's task, the available data and the surrounding process. Use the readiness audit or a scoped proof of concept to test the most consequential assumptions.
Structure the source material, resolve gaps with the business and define acceptance examples. Use AI-assisted engineering with code review, security checks and automated, manual and user testing.
An embedded build team brings engineering and adoption into the work. Agree release decisions, support responsibilities and the capability your permanent team needs to take forward.
Ask how this approach would fit your data, systems and team on a 30-minute call.
Talk it throughTenhaw and a PPL engineer built a working document-to-business-intelligence proof of concept in two weeks. Requirements became structured markdown before the build, and the engineer practised the method throughout.
See how the PPL proof of concept was builtIn his role at Discovery, James made delivery risk visible through capacity-based forecasts. The team was one of six on the launch, which met its fixed date across Discovery+ and Eurosport.
Read James's Discovery+ delivery case studyBook 30 minutes to ask James about the work and how it relates to your situation.
Talk it throughAgree the workflow, deliverables and team before work starts. These are the existing engagements this use case can sit within; prices exclude VAT.
Fixed price · £44,000
4 weeks
Find the AI workflows worth funding. Test them with your team.
See scope, team and termsFixed price · £20k–£55k
2–4 weeks
Build and test one AI workflow in your own tenancy.
See scope, team and terms£70k–£85k / month
6–12 months
Build enterprise AI with an embedded team your people learn from.
See scope, team and termsUse a 30-minute call to decide which scope is a sensible starting point.
Talk it throughA 30-minute discovery call with James Rooney. Bring a workflow, product or team challenge and we will discuss a sensible first scope.
most start with a fixed-price AI Readiness Audit · £44,000 · 4 weeks · working prototypes
No time that suits? Email hello@tenhaw.com. Calendar not loading? Open it on cal.com.
Record the assumption, the user task and the evidence from each experiment. Product managers can then decide which outcome to pursue, what needs another test and what should leave the backlog. The build follows that decision with acceptance examples attached.
The input, the expected behaviour, the observed result and the requirement it failed, within the project's data-handling rules. That gives the team a repeatable test case and helps distinguish a requirement gap from a model, retrieval or application problem.
Test it against a task customers already need to complete. Look at the usefulness of the result, the effort of checking it and how people recover from an error. Those observations inform the product decision and the support it will need.