Private LLM Deployment for a Multi-Site Health Network
Multi-site health network, Mid-Atlantic US
01 / Challenge
Clinical teams across 14 facilities needed AI-assisted documentation and care-gap analysis, but PHI could not leave on-premises infrastructure under any circumstances. Every commercial LLM vendor required data egress, and the CISO had blocked all cloud AI tools.
02 / Approach
QuettaMinds deployed a private LLM stack on the network's existing GPU cluster using a containerized inference layer with full HIPAA audit logging. We implemented a retrieval-augmented architecture against deidentified clinical summaries, with a hardware security module protecting all model weights.
03 / Outcome
Clinical staff now interact with a full ChatGPT-equivalent experience inside the network perimeter — zero data egress events since go-live. Documentation time per patient encounter dropped 38%, and the CISO approved the architecture for full rollout within 90 days of the pilot.
Representative case study illustrating common agentic-AI deployment patterns in Healthcare; not a specific QuettaMinds client engagement.
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