How do you get past the week-one retention cliff?

Retention is a design problem before it’s a notification problem. We instrument the activation moment, the point where a user first gets value, and build onboarding to reach it fast, then layer streaks, milestones, and behaviour-triggered nudges on top. We wire product analytics (Amplitude, Mixpanel, or PostHog) from day one so you can test changes against D1/D7/D30 instead of guessing. We won’t promise a retention number: that depends on your product and market. But we’ll build the loops and the measurement that let you move it.

How “real” is the AI personalization, or is it a wrapper on a static plan?

The difference that matters is whether the app adapts to what the user actually does. We build a profile that learns from logged meals, completed workouts, sleep, and outcomes, so a missed week re-plans the program and a plateau changes the recommendation. The LLM handles the conversational coaching and explanations; the personalization logic decides what to coach toward. We’ll tell you where a rules-based engine is the right tool and where a model earns its keep.

Which wearables and health platforms can you sync with?

Apple HealthKit and Google Fit / Health Connect cover most of the phone-native activity, sleep, and heart-rate data. For dedicated devices we integrate Fitbit, Garmin, Oura, and Whoop through their APIs. We normalise all of it into one health data model so your coaching logic reads a single, consistent signal rather than a different shape per device, and we handle the gaps and duplicate-source problems that come with syncing several trackers at once.

Do consumer wellness apps need to be HIPAA compliant?

Usually not on their own: a direct-to-consumer nutrition, fitness, or sleep app typically isn’t a HIPAA covered entity. But that doesn’t mean unregulated: consumer-privacy laws like GDPR, CCPA, and Washington’s My Health My Data Act treat this data as sensitive, and the app stores enforce strict rules on HealthKit and Health Connect data. The picture changes the moment you integrate with a provider, payer, or employer plan. We architect a clean PHI boundary so you can add those integrations later without re-platforming, and flag exactly where a HIPAA obligation would attach.

Can you build offline-first so tracking still works without signal?

Yes, and for a tracking app it matters: people log a workout in a basement gym or a meal on a plane. We build local-first storage with background sync and conflict resolution so logging never blocks on connectivity, then reconcile with the server and wearable data when the device is back online. For the user, the app works. The engineering underneath handles the sync correctness.

AI Wellness App Development

We build consumer nutrition, fitness, and sleep apps where the AI personalization adapts to what each user does, and the engagement design keeps users past the week-one drop-off. Wearable data feeds the coaching, and retention is something we design for from day one.

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What we build

Nutrition tracking & meal AI

Photo-to-macros logging, barcode and food-database lookups, and LLM meal suggestions that respect a user’s goals, allergies, and cuisine preferences.

Adaptive fitness coaching

Workout plans that re-plan around missed sessions, recovery, and progression, adjusting to what the user actually did instead of following a fixed script.

Sleep tracking & wind-down

Sleep-stage and consistency insights from wearables or phone sensors, plus evening routines and smart nudges tuned to each user’s chronotype.

Personalization & recommendation engines

A profile that learns from logged behaviour, goals, and outcomes, so nutrition, training, and sleep guidance get more specific the longer someone uses the app.

Engagement, streaks & habit loops

Streaks, milestones, smart notifications, and re-engagement flows built to lift D1/D7/D30 retention rather than spam users into churn.

Wearable & health-platform sync

Two-way sync with Apple Health, Google Fit / Health Connect, Fitbit, Garmin, Oura, and Whoop, normalised into one activity, sleep, and heart-rate model.

Doctor using a tablet in a clinical environment

Clinical tools that fit into real workflows

Reference architecture

Mobile App (iOS / Android)
Wearable & Health-Platform Sync
Unified Health Data Model
Personalization Engine
AI Coaching & Nudges
Engagement & Retention Loops

A typical AI wellness app: the mobile app and connected wearables feed a unified health data model, the personalization engine learns each user’s goals and behaviour, and AI coaching plus engagement loops turn that signal into nutrition, training, and sleep guidance that keeps people logging.

Integrations shipped across 23+ partners.

Wearables & health platforms

Apple HealthKit
Google Fit / Health Connect
Fitbit
Garmin
Oura
Whoop

Nutrition & food data

Nutritionix
Edamam
Open Food Facts
USDA FoodData Central

Engagement & messaging

Braze
OneSignal
Customer.io
Firebase Cloud Messaging

Subscriptions & billing

RevenueCat
Stripe
App Store / Play Billing

Product analytics

Amplitude
Mixpanel
PostHog

LLM providers

OpenAI
Anthropic
AWS Bedrock

Which engagement fits

Project Build

For a new wellness app: onboarding, tracking, wearable sync, AI coaching, and the paywall, built in a focused 10–14 week scope to a launch-ready iOS and Android build.

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Embedded Squad

For growing past launch: a dedicated team that ships alongside yours as you tune personalization, run retention experiments, and add new tracking verticals.

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Tech Audit

For assessing an existing app: a 5-day diagnostic of architecture, wearable-sync reliability, retention instrumentation, and privacy readiness.

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Privacy & compliance considerations

Privacy posture for consumer AI wellness apps. Status reflects how we engineer the controls into your product. Most wellness apps live under consumer-privacy and platform rules rather than HIPAA, and where that line sits stays your legal team’s call.
Standard	Status	What we ship
Consumer health data privacy (GDPR / CCPA / WA My Health My Data)	Compliant	Most direct-to-consumer wellness apps sit outside HIPAA but squarely inside consumer-privacy law. We build lawful-basis consent, data-subject and deletion flows, and granular controls for the sensitive health signals these laws now cover. See /compliance/gdpr.
HIPAA (only if you connect to covered entities)	In progress	A pure consumer app usually isn’t a covered entity, but the moment you integrate with a provider, payer, or employer health plan, HIPAA can attach. We architect PHI isolation so that boundary is clean, and flag where a BAA becomes your call.
App Store & Play health-data policies	Compliant	HealthKit and Health Connect data carry hard platform rules: no advertising use, no unrelated third-party sharing, explicit purpose strings. We build to those policies so your submission clears review the first time.
COPPA / age-appropriate design	In progress	If under-13s (or under-18s in the UK) can use the app, children’s-privacy and age-appropriate-design rules apply. We build age gating and reduced-collection modes, but which markets and ages you serve stays a product and legal decision.

Free

HIPAA Stance Builder

A guided walkthrough that maps your wellness app’s health-data flows, including wearable sync, coaching, and any provider or employer integrations, against HIPAA and consumer-privacy rules, and hands you a defensible stance on which apply.

Build your privacy stance

Medical imaging displays in a clinical setting

Engineered for clinical delivery

Patient data, provider workflow, regulatory guardrails

Clinical systems are measured by uptime and auditability, not marketing slides. We build for the reality of busy practices, compliance deadlines, and the patients who depend on both.

Frequently asked questions

Retention is a design problem before it’s a notification problem. We instrument the activation moment, the point where a user first gets value, and build onboarding to reach it fast, then layer streaks, milestones, and behaviour-triggered nudges on top. We wire product analytics (Amplitude, Mixpanel, or PostHog) from day one so you can test changes against D1/D7/D30 instead of guessing. We won’t promise a retention number: that depends on your product and market. But we’ll build the loops and the measurement that let you move it.
The difference that matters is whether the app adapts to what the user actually does. We build a profile that learns from logged meals, completed workouts, sleep, and outcomes, so a missed week re-plans the program and a plateau changes the recommendation. The LLM handles the conversational coaching and explanations; the personalization logic decides what to coach toward. We’ll tell you where a rules-based engine is the right tool and where a model earns its keep.
Apple HealthKit and Google Fit / Health Connect cover most of the phone-native activity, sleep, and heart-rate data. For dedicated devices we integrate Fitbit, Garmin, Oura, and Whoop through their APIs. We normalise all of it into one health data model so your coaching logic reads a single, consistent signal rather than a different shape per device, and we handle the gaps and duplicate-source problems that come with syncing several trackers at once.
Usually not on their own: a direct-to-consumer nutrition, fitness, or sleep app typically isn’t a HIPAA covered entity. But that doesn’t mean unregulated: consumer-privacy laws like GDPR, CCPA, and Washington’s My Health My Data Act treat this data as sensitive, and the app stores enforce strict rules on HealthKit and Health Connect data. The picture changes the moment you integrate with a provider, payer, or employer plan. We architect a clean PHI boundary so you can add those integrations later without re-platforming, and flag exactly where a HIPAA obligation would attach.

Building an AI wellness app? Schedule a meeting.

We’ve shipped the wearable sync, adaptive coaching, and engagement loops that consumer nutrition, fitness, and sleep apps live or die on. Tell us what you’re building.

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