Private Enterprise LLM (RAG)
Enabling a corporate entity to leverage generative AI on highly confidential internal documents. Inference runs locally on the client's own machines: no document leaves their infrastructure, and no call is made to third-party public APIs like OpenAI.
Operational HQ: Milan, ItalyTarget sector: LogisticsOperating language: Italian
The Technical Challenge
Enabling a corporate entity to leverage generative AI on highly confidential internal documents. Inference runs locally on the client's own machines: no document leaves their infrastructure, and no call is made to third-party public APIs like OpenAI.
Software & AI Integration
- Local LLM Deployment: Configuration and hosting of open-source models (e.g., Llama, Mistral) on isolated private servers.
- Advanced RAG Architecture: Development of a proprietary vector database in Python for real-time semantic retrieval of internal knowledge.
- Custom FastAPI Layer: Engineered a bespoke API backend to seamlessly integrate the isolated AI engine with the client's existing ERP.
- Zero Data Leakage: An end-to-end encrypted pipeline designed to strictly confine all information processing within the corporate firewall.
Verified facts
- RAG in production in 2 weeks, from the first workshop to daily use.
- Inference on employee workstations. No document leaves the client's infrastructure and no call is made to an external API.
- The index updates itself when a file is added to the dedicated folder, with no human intervention.
- Response time of 3–10 seconds per query, on client hardware, with no dedicated GPU and no network calls.