b. Bala ChandraIndependent in spirit. Engineer by craft.
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Case study 02 — AI-assisted product

LunaCalorie.

A food journal and habit tracker that pairs a calm mobile experience with AI-assisted nutrition estimates and cost-aware sync.

Role
Product direction · UX architecture
Platform
Mobile
Status
Google Play closed beta
Stack
Flutter · Firebase · Firestore
AI
Gemini-assisted estimation
Fig. 02 / Data flowClosed beta
01Meal input
02Gemini assist
03Estimate
04Offline journal
05Trends
01Intent

Make daily nutrition easier to see.

Luna Calorie explores a lower-friction way to journal food, understand meal estimates, and follow daily and weekly patterns without turning tracking into a chore.

Meal entries can be enriched with Gemini-assisted calorie and macro estimates, then rolled up into trend views that show the shape of a week rather than a single number.

02Decisions

Calm on the surface, deliberate underneath.

01

UX architecture

Directed navigation and information structure around journaling, habits and trends.

02

Design tokens

Built a reusable token system so visual decisions stay consistent as the product grows.

03

Offline first

Shaped Firestore sync for resilient use and multi-device continuity rather than assuming connectivity.

04

Cost awareness

Treated backend and sync cost as an architecture constraint from the start, not a scaling problem for later.

03Flow

Assistance first, then visibility.

  1. Record a meal
  2. Request a Gemini estimate
  3. Review calories and macros
  4. Store it in the journal
  5. Read daily and weekly trends

Scope note: the closed-beta URL, screenshots, measured estimation accuracy and usage metrics were not available, so this page leaves those claims open rather than filling them in.

04Takeaway

Product thinking includes operational thinking.

Luna shows mobile engineering as a whole: visual consistency, navigation, offline behaviour, sync economics and AI integration are all the same decision viewed from different angles.