Challenges
A multi-million-dollar industrial machine is down. Your top field service engineer is on-site, but they’re in a remote area with no cellular coverage. They need access to detailed schematics or diagnostic guidance, but the cloud-based support tools are out of reach. Every minute of downtime racks up thousands in losses. This isn’t a rare scenario — it’s the daily reality for service teams around the world. That’s why we built a solution that works when everything else fails: a robust, on-device AI assistant that runs entirely offline.
- Remote Environments: Service engineers work in locations with no or poor connectivity—mines, factories, ships, oil rigs—making cloud-based support unreliable.
- Time-Critical Troubleshooting: Engineers often face urgent technical issues and need fast, accurate answers. Traditional manuals (dense PDFs or printed guides) slow them down.
- Costly Support Delays: Waiting for remote support or expert calls leads to wasted time, delays in operations, and increased costs.
- Device Constraints: Engineers use a variety of mid-tier smartphones with limited storage, RAM, and battery life.
Solutions : A Custom-Built Offline AI Assistant
We developed a React Native app featuring an offline AI assistant powered by TinyLlama‑1.1B (quantized) using the llama.cpp engine embedded via a native bridge (JNI for Android, Swift/ObjC for iOS).
Key components:
- Model :Quantized TinyLlama (~1GB gguf, Q4KM), optimized for mobile CPUs.
- On-device LLM Inference :llama.cpp runs silently in the background, triggered from JS via native module.
- Embedded Knowledge Base :Manuals split into chunks; embeddings generated ahead of time; local SQLite semantic search retrieves top context segments.
- Offline Q&A Workflow :Engineer types a question → app retrieves relevant manual chunks → TinyLlama generates answer based on provided context.
- UI Experience :Clean chat interface, offline indicator, and settings for manual updates when online.
