Local Document Intelligence

I have developed a document intelligence system that does not require sending any documents to cloud services. Extraction is performed entirely locally, ensuring that content remains confidential.

Model training relies on anonymized datasets selected from the most advanced available solutions. Once trained, the models are used to generate extraction rules and templates that operate directly on the device.

For deployment, I chose a Python engine as the primary execution environment, but local LLMs can also be integrated, offering flexibility without compromising security.

This architecture allows sensitive documents—from contracts to technical reports—to be managed without ever leaving the author’s controlled environment.