When Edge AI Meets Real-World Constraints
When we set out to run document detection and face matching AI on edge devices — mobile phones, web browsers, even low-powered desktops — we knew it wouldn’t be easy. Running advanced AI models in constrained environments with limited memory, compute, and inconsistent connectivity brought its own set of challenges. But through months of deep engineering, continuous experimentation, and a strong focus on efficiency, we built a framework that brings intelligent automation to the edge — reliably and at scale.
This is a behind-the-scenes look at how we approached the problem.
1. The Challenge: Bringing AI to the Edge
Edge devices — especially mobile and browser-based platforms — pose a tough environment for AI: – Large runtimes like ONNX or TensorFlow can be too heavy for mobile storage or download speed. – Cross-platform deployment adds complexity in packaging and performance tuning. – Vision and face models are often too large to fit comfortably in memory or meet performance targets on entry-level devices. – Running AI in the browser lacks mature support and can drain resources quickly.
We wanted to build a solution that could offer robust document and face intelligence on any device — with minimal load times, full offline capability, and consistent accuracy.
2. Custom Runtime and Model Optimization
To make AI viable at the edge, we optimized the entire pipeline. The inference engine was tailored to keep runtime size lean and tightly scoped for our use cases. This allowed us to run AI smoothly in resource-constrained environments without unnecessary bloat.
For models, we aimed to keep them as small as possible — ideally within 10–15 MB per model — and trained them on carefully curated datasets for performance and speed.
3. KIMORA: Our Document Intelligence Engine
At the heart of our document processing pipeline is KIMORA — a custom AI model built by Extrieve. KIMORA is designed to understand documents holistically, with strong performance in identifying structure, geometry, and alignment.
Its intelligent corner detection mechanism ensures stable boundary estimation, even in poor lighting or skewed angles. KIMORA is tuned for edge inference, making it fast, compact, and reliable on mobile, desktop, and browser environments alike.
For facial matching and other identity tasks, we built additional lightweight models trained using domain-specific datasets and carefully optimized to operate under tight memory and latency constraints.
4. Built for Every Platform
We didn’t just want AI that works — we wanted AI that works everywhere. That meant building a core C/C++ logic layer and wrapping it for multiple targets: – Android via NDK – iOS with CoreML-compatible integration – Flutter and React Native through native bindings – WebAssembly for browser-based deployments – Desktop via native binaries
This ensured consistent output and predictable performance, no matter where the application runs.
5. Making AI Load Fast in the Browser
Running AI in browsers has traditionally been slow and memory-intensive. To solve this, we implemented a smart, chunked delivery strategy for WebAssembly-based AI: – Segmented large binaries and model files into smaller parts – Downloaded them in parallel – Cached them locally to avoid repeat downloads
With this approach, users can get started in just a few seconds on first load — and enjoy near-instant startup on subsequent sessions.
6. Key Takeaways
- AI can work on the edge — but only with careful tuning across runtime, models, and platform interfaces.
- KIMORA allows intelligent document understanding in ways traditional detection models can’t.
- By unifying our deployment logic, we ensured one framework powers mobile, web, and desktop use cases.
7. What’s Next
We’re now exploring even more compact models for real-time document classification and data extraction — running efficiently on CPU-only client PCs and multithreaded server setups. These next-gen models will bring advanced automation capabilities without relying on GPUs or external inference engines.
Tune in for more updates as we continue to innovate at the edge.
Ready to Explore?
If you’re building something that needs intelligent AI right at the edge — no internet, no bloat — we’d love to talk.
👉 Reach us at globalsales@extrieve.com for demos, SDK access, or integration discussions.
