Case studies / Fitineary
Mobile AppClient product · FitnessAug – Sep 2026

Fitineary

A fitness diary where your AI coach and your human coach share one screen, and logging a meal takes a photo.

Built for
Indian fitness users, personal trainers and nutritionists
Platform
Android and iOS apps
Status
Live on Google Play and the App Store
The Today screen, where a single ring shows calories left alongside macros, water and sleep for the day.
38 daysfrom first commit to both app stores
13releases in five weeks
3ways to log: photo, voice, text
₹349Premium per month
Overview

The short version.

Fitineary is one diary for food, exercise, water, sleep and mood, with a coaching layer built in. Users log a meal by taking a photo, speaking, or typing, including in Malayalam and Manglish, and review the AI's estimate before anything is saved. A personal trainer or nutritionist can connect to a client, assign routines and see how closely they're being followed.

We took it from a clickable prototype to live apps on Google Play and the App Store in 38 days, then kept a weekly release rhythm driven by the client's own feedback.

The challenge

What was broken.

People log food in one app and workouts in another, then hand their phone to their trainer to scroll through both. Calorie apps don't recognise puttu, appam or a plate of curd rice, typing every meal is tedious, and none of them give the trainer a real place in the app.

  • Western food databases miss everyday Indian meals.
  • Manual logging is slow, so people stop after a week.
  • Trainers coach from screenshots and WhatsApp messages.
How it came together

From idea to production.

  1. 5–6 Aug

    Prototype signed off

    A complete clickable prototype defined every screen and design token before any app code was written.

  2. 22 Aug

    Version 1.0

    The first full release of the app on a real backend.

  3. 12 Sep

    Live on both stores

    Version 1.3 went live on Google Play and the App Store in India.

  4. 23 Sep

    Premium and feedback

    First paid subscriptions, and a 35-point feedback round from the client turned into the 1.8 release within days.

The solution

What we built.

One Coach tab shows either the AI coach or the human coach, and the AI steps back in exactly the areas the human coach covers: training, nutrition or both. A routine is the same thing whether the user built it, the AI suggested it or a coach assigned it. Logging is designed to take seconds: point the camera at the plate, check the estimate, save. The app records everything on the phone first and syncs later, so it works in a basement gym with no signal.

How it works
  1. Step 1Snap a photo or speak
  2. Step 2AI estimates food and portions
  3. Step 3User reviews and saves
  4. Step 4Coach sees adherence
  5. Step 5Routine adjusted
Key features

What we delivered.

Photo, voice and text logging

Snap a plate, say what you ate, or type it. Malayalam and Manglish are understood.

AI coach and human coach

One Coach tab. The AI yields the areas your human coach takes on.

Routines that travel

The same routine whether you built it, the AI suggested it, or a coach assigned it.

Works offline

Logs save on the phone and sync when a connection returns.

Coach chat

Photo and voice messages, exercise demo videos and reminders.

Sign in with WhatsApp

A one-time code on WhatsApp or email, no passwords.

Full diary

Food, exercise, water, sleep and mood in one timeline.

Premium

Monthly to yearly plans at the same price on Android and iOS.

Inside the product

A closer look.

Where the AI does the work

Intelligent, and trustworthy.

Model chosen by experiment

We tested 10 AI models on 28 real meal photos, over a third of them Indian dishes. The cheaper option over-counted by more than 200 calories, so we ruled it out.

Two cents per user

Photo and voice logging cost about $0.018 per user per month.

You stay in charge

Every AI estimate opens in the normal review screen. Nothing is saved until the user taps Save.

Private by default

The app never holds an AI key, requests are rate-limited, and the AI provider is required to keep no data.

Built with

The stack.

Apps
React Native (Expo) for Android and iOS
Backend
Supabase Postgres with row-level security, edge functions
AI
Gemini Flash via a secured Cloudflare Worker
Business
RevenueCat and Apple in-app purchase, Dodo Payments, Firebase crash reporting and push
Results

The numbers.

Build proof

38 days
prototype to both app stores
Verified
13
releases in five weeks
Verified
$0.018
AI cost per user per month
Verified
35
client feedback items actioned
Verified

Expected impact for users

8 sec
to log a full meal by photo
Projected
3×
more days logged per week than manual apps
Projected
61%
users still logging at day 30
Projected
2×
clients per coach
Projected

Verified from project repositories, test runs and release records. Projected modelled estimates of user impact, not measured client results.

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