Private LLM deployment where the audit answer to "where did this data go" is "nowhere"

1. Problem. A regulated enterprise wanted a modern language model over its internal documents, under a contractual ban on sending that content to any third-party processor. Even zero-retention API terms did not clear the bar: nothing could leave the environment at all.

2. Solution. A fully private deployment inside the customer VPC: open-weight models on client-owned GPU nodes, self-hosted vector store, orchestration and observability, with egress to external model APIs blocked at the network layer. The audit answer to "where did this data go" is the same as "where does it live".

1. Problem. A major international law firm was running general-purpose models that hallucinated citations and produced inconsistent clause analysis across 11 jurisdictions. Every wrong answer was a liability risk in client-facing work.

2. Solution. We fine-tuned Gemma 3 27B on 400,000 of the firm's own legal documents using QLoRA on GCP Vertex AI. DPO alignment sharpened precision for time-pressured lawyers. A custom evaluation pipeline covering ROUGE-L, BERTScore and citation accuracy ran before any model reached production.

1. Problem. A US hospital network had patient records, lab results and ICD mappings sitting in silos, none of it reachable at the point of care. Clinicians needed decision support without adding friction to already demanding workflows.

2. Solution. A HIPAA-compliant clinical platform on GCP Vertex AI using a hybrid Agentic RAG pipeline. Patient records, lab data and medical literature unified into one queryable layer. Role-based access across every clinical and admin role, a full audit trail on every response, and a zero-downtime SLA.