Build & Prove
Build a bounded AI-native product journey or business workflow. Integrate the real constraints, test the result and make the next decision with evidence.
Discuss this engagementScope, timing, fees, team effort and support responsibilities are agreed before work starts. Existing capability is an input to the plan.
Build & Prove
Before the work starts
Success is agreed before the build. We assess correct task completion, output quality and the effort needed to review, correct and run the workflow. A polished demonstration is one input; the acceptance decision rests on the evidence.
Illustrative application
For a recurring internal request, the workflow gathers current context, proposes the next action, checks its authority and either completes an allowed step or brings the exception to an owner. We evaluate that complete loop.
Measures agreed for the workflow
Your team
Define what good looks like, review domain-specific results and decide which changes can enter real work.
Spell.fm
Build and integrate the system, implement the controls and run the evaluations alongside your reviewers.
The engineering loop behind a product journey or workflow. Each handoff carries context, an artifact and a decision.
Observe the actual work, its frequency and its constraints. Agree a baseline, the decision to be made and the people who need to be involved.
What moves forward
Connect product thinking, experience design and architecture. Explore a working prototype and agree on the direction before the build.
What moves forward
Coordinate engineering agents around a shared plan. Give each task the context, tools and isolated workspace it needs, then bring the changes together.
What moves forward
Use representative tasks, failure cases and permission tests. Compare with the agreed baseline, including the effort needed to review, correct and operate the result.
What moves forward
Tie the release to what was reviewed. Use controlled rollout, observability and a recovery path to bring the work into production.
What moves forward
Carry the implementation, operating knowledge and evaluation history into the next cycle. Give changes a named owner, and recheck assumptions as the work evolves.
What moves forward
The delivery state is explicit. A prototype explores feasibility; a controlled pilot tests a bounded use case. Production use requires the agreed access, quality, security, monitoring and recovery checks to pass.
No. Domain experts define the work and evaluate results. Engineers implement the integrations and controls. Enablement focuses on the tasks each role will actually own.
We examine the actual action path: identities, permissions, tool boundaries and available alternatives. Tests include disallowed actions and failure behavior. Instructions to the model support these controls but do not replace them.
Where should we invest first?
Find where AI can create worthwhile change, with a plan grounded in your actual workflows, existing systems and available capacity.
How do we run and expand it reliably?
Bring proven AI workflows into dependable operation. Connect the platform, controls, people and support needed to make the capability last.
