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An illustrative engagement, not client work. Select a stage to replay it.

Illustrative example, not client work: a clinic booking assistant taken through the six Stuck-to-Scale stages. Ideate, is there a real problem worth solving here: missed appointments are mentioned, complained about and worked around by hand, with staff phoning every patient the day before, but nothing is paid for or switched for yet, so the problem is real enough to build for. Define, what exactly are we building, for whom, and what are we not building: of six ideas, two make the first release. Build first: booking over WhatsApp with reminders, because customers are already there, and payment at the time of booking, which cuts no-shows with no AI at all. Don't build a custom-trained model, because an off-the-shelf model does this job. Cut the admin dashboard with 14 reports to 2, because nobody reads twelve of them. Later: no-show prediction, which needs three months of real data first, and a voice assistant, until the text flow is proven. Build, does the thing exist and does it work: the two ideas are built as one flow in the chat customers already use. City Dental Clinic's booking assistant offers Thursday slots at 5:00, 5:30 and 6:15, holds 5:30 and asks the customer to confirm and pay. Conversation tests pass on real booking requests, security checks cover prompt-injection paths, every step the assistant takes is traced, and code and data live in the client's own accounts. PMF, do the right people want it badly enough to keep using it: the rollout reaches internal use and beta clinics, measuring whether patients book in chat without being told to, whether beta clinics keep it switched on after the pilot, and whether paid bookings turn up for their slots. It goes to market only if the beta clinics keep using it. Market, can we reach them repeatably and will they pay: a soft launch, where the same setup has to work for every clinic, payment is taken at booking, and it sells on fewer no-shows rather than on the AI. Grow, does it compound without us pushing it: live for every clinic, with new clinics using the same setup, staff seeing every booking instantly, and code and data staying in the client's own accounts.