Enterprise LLM development that ships the product around the model

1. Problem. Critical institutional knowledge was scattered across hundreds of documents and disconnected systems. A single-vector-search setup had already failed. The organization needed multi-document, multi-hop reasoning that held up under real query volume.

2. Solution. Multi-agent reasoning with a Neo4j knowledge graph for complex cross-document queries. RAG Fusion lifted precision well above single-retrieval approaches. Role-based access with a complete audit trail across every department, handling 8,000 queries per day at launch.

1. Problem. 5 AI models were running in production with no shared visibility, no drift detection and no governance. Model behavior was tracked by hand in spreadsheets. In a regulated financial environment, that is an audit failure waiting to happen.

2. Solution. A single LLMOps control plane across all 5 models: scheduled automated evaluations, statistical drift detection against rolling baselines, shadow deployment and A/B testing before any version reaches live traffic, and a full model registry with SOC 2 compliant lineage and approval workflows.

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".