A pre-launch dental clinical-AI startup, founded by a practicing oral surgeon, came to Mindbowser with a clinical problem and more than 25 open technical questions. In four weeks we settled the AI architecture, intake approach, data-source strategy, product design and development scope, before development started.
Book a Design Sprint CallPre-launch dental clinical-AI startup, founded by a practicing oral surgeon
4-week design sprint: AI architecture, intake approach, rules engine, product UI, data sources and development scope
Vertex AI, Google Cloud, JSON rules engine, Figma
Sprint complete, fixed-scope SOW signed
Every major architecture and product decision was made during the sprint, so development starts against a fixed scope instead of open questions.
Technical questions resolved before development started
Sprint deliverables, including wireframes, prototypes, user stories and design documentation
Medical and professional data sources mapped and evaluated for API availability
From first call to signed fixed-scope SOW
The founder understood the clinical need. The architecture, data strategy and scope still had to be decided, and every one of those calls needed to land before development so nothing would be reworked later.
Patients can arrive with fragmented or incomplete medical histories, while dentists need to identify relevant risks quickly during clinical workflows.
General-purpose LLM or specialized dental SLM, fine-tuning or prompt engineering: none of it was decided.
Licensing, data-source API availability, hosting and model ownership were all open among the 25+ questions.
The engagement needed clear boundaries on what the platform would and would not include, with potential scope-creep areas identified before development began.
Vertex AI inference on Google Cloud, with a JSON rules engine deciding what gets checked and the LLM writing the explanation.
We evaluated specialized dental SLM approaches against a general-purpose LLM governed by structured clinical rules, then selected Vertex AI for cloud inference.
We mapped approximately 12 medical and professional data sources and verified API availability for each, which set the MVP data priorities.
We designed the practice portal, patient intake form, clinical output dashboard and admin experience in Figma, and set how intake handles incomplete submissions.
We set what the platform would not include before development began, so the fixed-scope SOW had clear edges.
We run design sprints that settle model choice, data sources and scope before development starts.
Partner with us to design, build, and scale digital solutions that drive better outcomes.
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