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innercode

Innercode is a health intelligence product built for health-aware women who receive blood test results with no useful interpretive context: just a table of numbers measured against reference ranges calibrated to population averages, typically a 30-year-old man. A 40-year-old perimenopausal vegan who takes five supplements is being read against a baseline that erases her entirely. The capstone centred on designing and shipping the Results Hub: a feature that lets users upload their blood test results and receive personalised, science-backed interpretation calibrated not to a population average, but to who they actually are. Before reading a single result, the product collects four active inputs (life stage, diet, current supplements, and family history) that feed directly into every insight generated. Before any file is processed, users see an explicit consent screen: plain language, a clear data policy, and a path to permanent deletion. No buried checkboxes. The project ran a complete PM cycle: discovery interviews with target users, problem framing, feature scoping, a working prototype built in Lovable, and a remote usability test with real participants uploading their own blood test results. Every design decision had a research origin: the consent moment came from users who described considered privacy positions, not vague anxiety; the supplement input came from a participant who said "in no app, none, do you know that I supplement." The usability test validated the core hypothesis. One participant described the interpretation as "the first time I received professional-quality feedback on my results." Innercode doesn't replace the doctor. It makes the user a better-informed patient by the time she walks into the appointment.

Case Study

Design and validate a feature that solves a structural gap in how people receive health data: results arrive without interpretation, context, or a clear path to action. The Results Hub was scoped to prove that a product could bridge this gap by personalising interpretation to the individual (not the population), while maintaining user trust through sourced recommendations and explicit data consent. Success was defined as a participant independently completing the full flow, understanding her results, and being motivated to act, both on her own and in preparation for her next doctor visit.

The Team

  • Camila Klein Rinaldi

    Journalist turned Product Manager | Digital Health & Wearables | Bridging Content, Data & User Needs

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We AI-proof
your career

studienberatung@neuefische.de+49 30 9173 9346
Mo - Fr 09:00 - 17:00