Language Models That Never Leave the Building

What you can do together

• Ask AI Intelligence Hub a question across interview recordings, body-camera transcripts and discovery documents and get a cited answer without a single external call.
• Run the whole AI stack disconnected: Ollama for language and embedding models, with local Whisper transcription, vision, OCR and PII detection alongside.
• Choose the model per agent, so a small model handles routine questions and a larger one handles case analysis, both on your hardware.
• Keep every answer inside what the asking user is entitled to open, with the agent scoped further to a case folder or category.
• Build workflows with a human approval step, with the model node pointed at Ollama.

How it connects

AI Intelligence Hub calls the Ollama server over its REST API on your network, at the address supplied in configuration. The model provider is configuration, set per deployment or per agent. Pointing an agent at Ollama rather than a hosted endpoint changes where inference happens without changing what the workflows do.

Two things flow across your own network. At indexing time the text of transcripts, OCR output, visual descriptions and documents goes to Ollama's embedding model and vectors return to the index. At question time the question and the passages retrieved for the asking user go to the language model and the answer returns, cited to the passages it drew on.

Nothing flows outside. Licensing verification is the one outbound call the platform makes, independent of AI processing, and it is disabled for a disconnected deployment. Model quality follows the models you host, and the server needs the GPU capacity for them.