This is a submission for the Redis AI Challenge: Beyond the Cache.
What I Built
Control Points Finder – Real-Time Geospatial Intelligence for Field Engineers Powered by Redis Cloud
Control Points Finder is a mobile-first geospatial search and navigation tool designed for surveyors, GIS specialists, and field engineers in Ghana. It solves a critical problem: locating verified control points quickly and accurately in the field.
Built with Flutter and powered by Redis Cloud, the app uses Redis not just as a cache—but as a primary database, full-text search engine, and real-time geospatial query layer.
🚀 Redis Capabilities
Redis powers the Control Points Finder with multi-model intelligence far beyond caching:
🔧 Capability | 💡 Description |
---|---|
🔍 Fuzzy Full-Text Search | Search control point IDs and town names with flexible matching |
🌍 Geospatial Radius Filtering | Instantly find nearby control points using location-based queries |
✨ Autocomplete Suggestions | Live input matching with prefix and wildcard logic |
📇 Dynamic Metadata Display | Coordinate conversions and rich data cards for each control point |
🧵 Real-Time CLI Access | Direct querying and validation using Redis CLI |
🔧 Key Features & CLI Examples
1️⃣ Full-Text Search by Town Name
FT.SEARCH control_points_idx "@town:(Tema)" RETURN 3 id town location
2️⃣ Geospatial Radius Search
FT.SEARCH control_points_idx "@location:[5.5882 -0.1751 10 km]" RETURN 3 id town location
Result: Returns control points like:
SGGA C2600 17 4
SGGA 935/01/1 (ADGIRIGANO)
SGGA 7/89/106 (SPINTEX RD)
3️⃣ Autocomplete with Live Suggestions
Typing "SG" returns:
- SGGA C2600 17 4
- SGGA 935/01/1 (ADGIRIGANO)
- SGGA 7/91/640 (TEMA)
4️⃣ Dynamic Card Display
Each control point card includes:
🆔 ID, 🏙️ Town, 📍 Coordinates
📐 Original Easting/Northing
🏷️ Label and Redis Cloud source
🧭 Navigate button to Google Maps
🧰 Tech Stack
🐍 Python – GeoJSON to Redis Cloud loader for control point ingestion
🧱 Layer | ⚙️ Tool |
---|---|
🗄️ Data Store | ☁️ Redis Cloud |
🔍 Search Engine | 🧠 RediSearch |
🗺️ Geospatial | 📍 Redis GEO |
🎨 Frontend | 📱 Flutter |
🛠️ Backend | 💻 Redis CLI + API |
🚀 Hosting | ☁️ Redis Cloud |
🎯 Impact
- ⏱️ Saves time for surveyors and engineers
- 📐 Improves planning and field accuracy
- 💰 Reduces costs by minimizing location errors
- 🧭 Enables real-time decision-making in the field
- 🧮 Preserves legacy coordinate systems for compatibility
🏁 Final Thoughts
Redis didn’t just support this project—it enabled it. From full-text search to geospatial intelligence, Redis proved it’s more than a cache. It’s a real-time, multi-model powerhouse that’s ready for the field.
Control Points Finder is a testament to Redis’s versatility, speed, and reliability. And it’s already making a difference in how engineers work.
🎥 Demo
Architecture Diagram
🔧 Redis CLI Demo Watch on YouTube
🔧 Redis CLI Demo
📱 Mobile App Demo
📱 Mobile App Demo Images
How I Used Redis 8
Redis was the backbone of this project — not just for caching, but for powering intelligent, real-time geospatial workflows.
🔧 Redis Capabilities Used
Primary Database: All control point data stored as Redis JSON objects
RediSearch: Full-text search on town names and control point IDs
GEO Indexing: Radius-based location filtering for field proximity queries
Autocomplete: Live suggestions based on partial input using prefix and wildcard matching
Redis CLI: Used for direct querying, debugging, and validating search logic
🧪 Redis CLI Examples**
KEYS cp:*
Returns all control points in the Redis database
FT.SEARCH control_points_idx "@location:[5.5882 -0.1751 10 km]" RETURN 3 id town location
Returns all control points within 10 km of a given location
FT.SEARCH control_points_idx "@town_tag:{TEMA}" RETURN 3 id town_text location
Returns all control points located in Tema
🙌 Special Thanks
Special thanks to my AI collaborators: ChatGPT (OpenAI), DeepSeek, and Microsoft Copilot for brainstorming, code review, and moral support.
These AI companions weren’t just tools—they were sounding boards, co-designers, and late-night motivators.
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