LLMOps consulting for teams that cannot say whether the system got worse last week

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. 5 million+ files across regional data centers, S3, document systems, databases and application servers: contracts, financial statements, engineering reports, spreadsheets and email. The usual fix is 1 central index, and regional data rules ruled it out. So every cross-region question, and every workflow touching more than 1 system, depended on someone stitching answers together by hand.

2. Solution. The data stays in its region and the question travels to it. Retrieval runs locally and returns only what the asking user is already allowed to open. 7 LangGraph agents split the work, hand off to each other over A2A and reach each system through MCP servers with scoped permissions. Anything that changes a record waits for human approval, and n8n keeps ingestion, approvals and notifications moving around the clock.

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.