AI assistant that cites its sources
Streams answers drawn from the practitioner's own documents and cites them, so members can see where every answer comes from. It stays within the practitioner's guidance.
Case study · Health & wellness
A health membership platform, led by a practitioner, that turns a member's lab report into a personalized, plain-language explanation. It comes with an AI assistant that answers from the practitioner's own material, structured courses, a member community and subscription billing.
A practitioner with a large, engaged audience wanted to turn free education into a paid membership. The membership had to offer something a generic app couldn't: each member's own lab results, interpreted through the practitioner's methodology.
It had to feel calm and trustworthy, work well on a phone and stay within the practitioner's guidance. Lab reports arrive as PDFs from many different labs, each with its own layout, marker names and units. Most people get those numbers back with no explanation at all.
Streams answers drawn from the practitioner's own documents and cites them, so members can see where every answer comes from. It stays within the practitioner's guidance.
Structured lessons in order, with progress tracking and a discussion thread on each lesson.
A members-only feed with posts, comments, reactions and shares, open to active subscribers.
Monthly, annual and early-bird tiers with secure checkout, a paywall, billing webhooks and MRR/ARR reporting.
Members can submit written, before-and-after or video stories through a guided form. An admin reviews each one before it appears in the public gallery.
Users, content, subscriptions and moderation on one page, plus live brand theming you can preview before publishing.
In health, an AI that sounds confident but is wrong does real harm. We designed the system so the model always works from facts the platform has already checked.
Marker matching and range classification follow fixed rules against a curated library. The ranges come from the library, never from the model.
One model is used for extraction and another for interpretation, each chosen for what it does best. Either can be swapped out without rebuilding the product.
Synthetic lab reports with known results test the analyzer from end to end, so a change that breaks the analysis is caught before members see it.
Built to WCAG 2.1 AA with a calm, clinical look, and designed for the phone where most members will open their results.
Health, wellness & memberships
We build membership platforms and AI features that keep your methodology at the center and keep members coming back.