Decision to improve
How can people analyze sensitive, very long documents without sending the material to a cloud service?
What shapes the outcome
Sensitive documents may not be allowed to leave the device, yet the most useful analysis often involves material longer than a local model can process at once. A practical system therefore has to protect the full workflow while remaining fast, understandable, and useful to non-specialists.
Constraints and uncertainty
- The application must run well across different CPU and GPU configurations.
- Long documents need a structured analysis process rather than silently cutting off the text.
- Users need to understand what the model is doing, what it remembers, and how deeply it is analyzing the document.
- Privacy depends on the boundaries of the entire application, not only on where the model weights are stored.
How the system works
- Built a desktop application that runs quantized models locally through llama.cpp.
- Used available hardware acceleration when possible, with a safe CPU fallback and a model service restricted to the local machine.
- Added recursive map-and-reduce analysis so documents longer than the model’s context window could still be processed systematically.
- Created clear fast and thorough modes, configurable conversation memory, and local PDF workflows.
What this system enables
The application provides private chat and long-document analysis without API keys or cloud processing, packaged as a desktop product that people can use without managing the underlying model infrastructure.
What I built
I conceived and built the complete private AI product, including the desktop interface, local model integration, long-document workflow, hardware-aware execution, packaging, and privacy boundaries.