PRODUCT

Halal Maps — Case Study

A community-powered halal restaurant discovery app, designed for scale and reliability

Role: Founder, Product Engineer Platform: iOS (Swift), Firebase, React Dashboard Status: Beta | Selected for Generate Accelerator Showcase

🧭 Discovery & Research

Before writing code, I researched how users currently find halal restaurants. I examined:

• Blogs & halal websites — limited listings, often outdated, hard to use on mobile
• Social media (Instagram, TikTok) — hard to search by area or cuisine; lots of restaurant do not have Halal info on their social media pages
• Google Maps — missing halal filters or lacking detail
• Call — phone restaurants to check if they offer Halal food; can be time consuming
• Halal apps — poor coverage and outdated data

I also spoke with ~12 people who adhered to a Halal diet. Everyone said the same thing:

To validate demand and messaging, I also did light outreach:

• Built a landing page and ran low-budget ads with low CPC
• Shared the concept across social media and relevant communities
• Reached out directly to people I knew in Muslim and food-focused circles

These early experiments brought in ~300 sign-ups in a few days of outreach, confirming clear user interest in the idea.

🧰 Defining Product Requirements

I scoped the MVP around real user pain points.

User-facing features:

• Locate nearby halal restaurants on a map view
• Browse options in a scrollable list view
• Filter by rating, cuisine, or open status
• Save favourites and add personal notes
• Submit corrections or suggest new restaurants
• Offline functionality

Backend needs:

• Automate data refresh via Google Places API
• Moderate user submissions with feedback
• Implement credibility scoring to partially automate moderation and rate limiting
• Prevent abuse (e.g. spam, malicious edits)

🛠 Backend & Internal Tools

I built a custom React dashboard to support internal ops:

• Review and approve user-submitted content
• Add admin feedback and rejection notes
• Auto-fetch metadata (e.g. ratings, hours) from Google
• Test mode to simulate updates before pushing live
• Controls for creating and publishing new dataset versions

🔁 Versioned Data Sync System

To minimise bandwidth and ensure users stay in sync, I designed a comprehensive version-controlled update system which you can read more about >>here<<

How it works:

1. App checks metadata.json (tiny file with version + hash)
2. If out of date, it loads manifest.json with available update paths
3. Based on current version, the app downloads the most efficient update
4. Each file is validated using a SHA-256 hash before applying

🔄 Version Flow Logic

There are two main paths to reach the latest version (1.04):

1. Diff-Based Path (for regular users):

1.00 (full) → 1.01 (diff) → 1.02 (diff) → 1.04 (diff) ✅ Small updates (~5–15KB) for users who stay current

2. Full Update Path (for new or out-of-sync users):

1.03 (full) → 1.04 (diff) ✅ Full updates (~1-2MB) for new users or inactive users

✅ Reliable fallback when version chain is broken

🧠 Debugging & Recovery

If a version is faulty:

• The file can be silently overwritten on the server
• Users who downloaded it will fail hash validation and fall back to a known-good full update
• This allows for safe recovery without breaking the update chain or requiring an app update

🚀 Outcome

• Built and testing Beta version
• Selected for Generate Accelerator Showcase
• Backend + sync system designed for scale and community contribution