Consumer / Pet Care Production / Real Users

Anabulku

AI-Powered Pet Care Mobile App

3 weeks

App Store Release

< 1 month

First Users

Monorepo

Repository

RAG

Architecture

Executive Summary

We built Anabulku, a cross-platform pet care mobile app that helps pet parents manage health, routines, and care history in one place. The product combines vaccine and medication reminders, grooming schedules, multi-pet profiles, AI-assisted consultation with RAG over pet records, tool-connected reminder creation, in-app subscriptions, and pet matchmaking — shipped to the Apple App Store within three weeks.

The Problem

The main challenge was connecting multiple product behaviors into one coherent system: context-aware AI that retrieves pet-specific data rather than responding generically, RAG over structured health and care records, dynamic reminder logic across vaccines, medication, grooming, and general care with different scheduling rules, multi-pet data isolation, AI tool execution for creating reminders from chat, subscription guarding for premium AI and pet limits, and a separate swipe-style matchmaking domain for pet discovery.

3 weeks

App Store Release

iOS + Android

Platforms

Care Domains

Services Delivered

AI Integration MVP

AI-Powered Pet Care Mobile App

Architecture Overview

Data Layer

PostgreSQL

Backend & Orchestration

NestJS Cron Jobs IAP

Frontend

Expo React Native Next.js

Infrastructure

Vercel VPS

Key Technical Decisions

System Design

Anabulku was built as a monorepo containing a NestJS backend API, an Expo/React Native mobile app, and supporting Next.js web surfaces. PostgreSQL stores users, pets, reminders, subscriptions, and health records. The mobile app communicates with the API for authentication, persistence, subscription validation, reminder scheduling, AI context retrieval, and tool execution. For AI workflows, the system retrieves relevant pet-specific data before generating responses, and the tool layer enables action-taking behavior such as creating reminders from chat. A reminder engine uses scheduled jobs and notification logic, with caching and indexing on frequently accessed records.

Key Decisions

NestJS was selected for clear module boundaries across pets, reminders, AI, payments, matchmaking, and accounts. Expo and React Native accelerated cross-platform delivery to iOS and Android from one codebase. The monorepo kept backend, frontend, and mobile aligned on shared contracts and types. The AI assistant was designed to retrieve data and perform actions—not only answer questions—so users could manage real care tasks in-product. Subscription and price plan validation was enforced on the backend to protect premium AI usage, pet limits, and paid feature boundaries beyond client-side checks.

Implementation Highlights

RAG integration over structured pet profile and health data, with an AI assistant connected to backend tools for reminder creation. A dynamic reminder engine supports vaccines, medication, grooming, and general care via cron-based scheduling. Backend-level price plan guarding, PostgreSQL indexing, and caching for performance-sensitive retrieval paths. Multi-pet support with pet-specific context retrieval, swipe-style matchmaking logic, and IAP integration for mobile subscriptions—all within a monorepo supporting rapid development across surfaces.

Results & Validation

Released to the Apple App Store within 3 weeks of development start.

Acquired first users within the first month after launch.

Shipped AI assistant integrated with pet-specific structured data and tool-connected reminder creation.

Implemented dynamic multi-type reminder system and multi-pet management.

Delivered premium subscription flow through in-app purchase with backend access control.

Established modular core architecture prepared for continued feature expansion.

Key Insights

Designing AI that works with structured product data, not only static documents.

Connecting AI chat behavior with real backend actions such as reminder creation.

Building reminder logic that supports multiple care types and recurrence patterns.

Managing multi-entity data across users, pets, reminders, subscriptions, and health records.

Shipping a cross-platform mobile product under a compressed timeline.

A key insight: AI in consumer apps becomes more useful when connected to user-specific data and product workflows—the assistant acts as part of the pet care operating system, not a separate chatbot.

Who This Applies To

This architecture is relevant for consumer mobile products where AI must interact with user-specific data, not only provide generic answers. The same approach applies to healthcare, wellness, education, lifestyle, productivity, and vertical SaaS products that need reminders, records, subscription access, and AI-assisted workflows operating together in a production mobile app.

Mobile Apps AI-Native Products Consumer Health RAG Systems Subscription & IAP

Technologies Used

Backend

NestJS Next.js

Frontend

Expo

Infrastructure

React Native PostgreSQL Vercel

Data & Integrations

VPS RAG Cron Jobs

Patterns & Techniques

In-App Purchase Monorepo GitHub

Building something similar?

We specialize in ai integration and mvp for consumer / pet care companies. If you're facing challenges like the ones we solved for Anabulku, let's talk.

Book a Free Consultation → View All Case Studies

30 minutes · No commitment · We respond within 24h