Define the outcome
We identify the decision, document, analysis, or workflow you need to produce, then define what good looks like and when it is needed. Only then do we decide whether AI has a useful role.
Illustrative example: A one-page update: decisions, open questions, owners, and next dates.
Result: A clear aim and quality standard
Workflow diagnosis
Together, we map the current steps, skill level, approved tools and licences, data sensitivity, quality risks, and how often the task repeats. This reveals what should stay human and what can be supported safely.
Illustrative example: Use approved, non-sensitive notes. Keep decisions and commitments under human control.
Result: Useful opportunities and hard constraints mapped
Guided design
We choose only useful AI touchpoints and connect them with prompts, human checks, and documentation. A repeatable design beats a collection of disconnected chats.
Illustrative example: Group the supplied notes into decisions, questions, and actions. Mark missing owners; do not invent them.
Result: A workable workflow draft
Verification routine
Outputs are tested against sources, assumptions, privacy requirements, and relevant rules. Reliable use depends on knowing when the model should not be trusted.
Illustrative example: Match each action, date, and owner to its source. Resolve missing information before sharing.
Result: A clear verification routine
Human-owned plan
You should be able to explain, run, adapt, and stop the workflow yourself. The tool may assist, but judgment, accountability, and final decisions remain human.
Illustrative example: Save the brief, prompt, and checklist. Review the final update yourself each week.
Result: A repeatable workflow you control