Prepared for Dr. Gabriela Sheets. This spec translates the adoption framework into screens, flows, and a data model — the scope needed to build and demo Lumbiota's V1.
Lumbiota attaches to behavior a family already has — planning meals, cooking dinner, feeding a household — and makes each moment slightly better. The phone is the whole product. Three rules govern every decision:
| Life stage | What they need from the app |
|---|---|
| Expecting | Trimester-aware nutrition that quietly builds maternal microbial diversity ahead of birth. |
| New parent, infant 0–12 mo | Allergen-introduction guidance and purée-stage recipes timed to pediatric protocol windows. |
| Household running multiple protocols | One shared meal that serves two or more tracks at once, without cooking twice. |
Home surfaces one action (Plan). Cook Mode executes it (Cook). Protocol Alignment updates in response (Track). The First 1,000 Days timeline contextualizes why it mattered (Learn). No step requires leaving the kitchen.
| Screen | Purpose |
|---|---|
| Onboarding | Life-stage picker → 3 recipes, grounded in developmental microbiome science. No account required. |
| Home | Protocol Alignment (hero metric) + tonight's recipe + journey preview. |
| First 1,000 Days | Horizontal journey from conception to toddler; current position, active protocol, what's next. |
| Recipe detail | Ingredients, swap options, protocol rationale, "start cooking." |
| Cook Mode | One step per screen, embedded timers, cast entry point. |
| Family Table | Multi-protocol households: swipeable or split-view tracks, live ingredient-swap recalculation. |
| Depth / Diagnostics | Diversity scores, taxa breakdowns, longitudinal trends — one tap below Home. |
| Cast picker | AirPlay / Google Cast / StandBy — surfaced from the share icon, not a tutorial. |
No account wall — the first three recipes need nothing but a life stage. Sequencing-based personalization is available immediately for families who already have a practitioner's result, and encouraged once the app has proven itself.
Phone-first, cast-optional. Cook Mode is built once as a single responsive layout; casting renders that same view on a remote screen. There is no voice-skill certification and no smart-speaker platform risk.
| Entity | Key attributes | Relates to |
|---|---|---|
| Protocol | life stage, active window, target nutrients | Household, Milestone |
| Life Stage | trimester / infant month / toddler | Protocol |
| Recipe | ingredients, prep time, protocol tags, swap map | Protocol, Track |
| Household | members, active Tracks | Track, Protocol |
| Track | member, current Protocol, texture/stage | Household, Recipe |
| Diagnostic Result | diversity score, taxa breakdown, date | Protocol, Milestone |
| Milestone | stage marker, status (past/now/upcoming) | Protocol |
| Cast Session | target type, active recipe, step index | Recipe |
| In V1 | Deferred |
|---|---|
| Hero metric, First 1,000 Days timeline, recipe engine | Smart-appliance APIs (multi-cookers, ovens) |
| Cook Mode + AirPlay / Cast / StandBy | Voice control (Siri Shortcuts) |
| Family Table, ingredient-swap recalculation | Full longitudinal diagnostics dashboard |
Why defer smart appliances: manual temperature check-ins plus Live Activity timers cover ~95% of the value at 5% of the engineering cost. Appliance APIs get built when demand justifies the maintenance burden.
Dr. Sheets' practice today is bottlenecked on her own hours: sequence a client's microbiome, interpret it, build a custom plan, then manually stay in the loop between sessions. Her own site already lists "Healthcare Practitioners" as a service line — consultations, lectures, training other clinicians. The app is the natural product wrapper for that: it turns a one-to-one consulting practice into a licensable one, run from anywhere.
The app absorbs the daily-touch work — recipes, Cook Mode, Family Table, Protocol Alignment tracking — so clients get value between sessions without her being physically present. Her role shifts from "delivers the daily plan" to "reviews sequencing data and adjusts protocols remotely." That's a job doable from a laptop anywhere, with occasional video check-ins standing in for a physical practice.
Everything specified above (sections 1–8) is consumer-facing: a family managing its own protocols. None of it lets a practitioner manage a caseload. That's a distinct surface, built on the same design tokens, not a repurposing of existing screens.
| Screen | Purpose |
|---|---|
| Caseload roster | Every active client/household at a glance, sorted by who needs attention (alignment drop, unread sequencing result, protocol due for review). |
| Sequencing intake | Upload or connect a client's microbiome sequencing result; the practitioner reads and annotates it directly — this is where her actual diagnostic expertise lives in the product. |
| Protocol assignment | Turn a sequencing read into an active Protocol for that client's Track — the same Protocol entity the consumer app already runs on (see §7). |
| Cross-client trends | Alignment and adherence across the whole caseload, not just one household — spot who's drifting before they churn. |
| Async check-in | Lightweight messaging tied to a specific Protocol or Milestone, replacing ad-hoc email/text between sessions. |
| Tier | Who it's for | Revenue |
|---|---|---|
| Sheets-run instance | Her own direct clients | Existing consulting fee, now with a lower cost-to-serve per client |
| Certified Practitioner license | Midwives, doulas, integrative nutritionists she trains and certifies on her methodology | Per-seat or per-active-client license fee, or a revenue share on each practitioner's client billing |
Why licensing beats training alone: she already trains practitioners informally (lectures, consultations). Wrapping that training in a licensed app means every certified practitioner runs on her actual protocols — not their memory of a workshop — so quality holds as the network grows, and she earns recurring revenue from a caseload she never personally touches.
The same daily companion a solo family gets — a recipe, a cook, a journey — but now backed by a practitioner who reviews their real sequencing data periodically instead of guessing from symptoms. Fewer "what do I eat tonight" messages land in the practitioner's inbox, because the app already answered it.
This is explicitly out of V1 scope (§8) — it depends on the consumer app proving the adoption thesis first. Sequenced correctly: consumer V1 → Deep Data unlock (§5, Flow C) validates that families actually connect sequencing results → Practitioner Console productizes the review side of that same data.
| Stage | What proves it worked |
|---|---|
| Download → value | % of new users who view a recipe within 2 minutes, no account created |
| First cook → hook | % who complete a full Cook Mode session; % who discover casting unprompted |
| Deep data → commitment | % who connect a sequencing result after ≥3 cooks (opt-in rate, not prompt-driven) |
| Family Table → moat | Household retention at 90 days vs. single-profile retention |
| Practitioner network | Certified practitioners active at 6 months; average caseload size per practitioner without quality drop-off |