Lets AI agents query a computerized system inventory as a knowledge graph using Cypher, enabling blast radius, data lineage, regulation checks, and change impact assessments while preventing hallucinated regulatory claims.
Enables agents to route spoken requests to the right project on the right machine and to remember the outcome by reading and writing a FalkorDB graph, so learned aliases, clarification answers, and episodes carry over across sessions. Exposes five fixed, validated graph tools — route_request, whats_running, machine_blast_radius, project_history, and why_heard — each answering with its MATCH path, Cypher, and parameters, and reporting not-in-graph or ambiguous names distinctly instead of guessing.
Enables querying a Neo4j knowledge graph built from Git repositories and relational database schemas via MCP, with tools for schema inspection, Cypher queries, and searching code and database entities.
Exposes seven tools that turn Azure cost-pattern and AI FinOps analysis into drafted optimization plans, with human approval bound to a SHA-256 digest and dry-run execution recorded in a hash-chained audit log. Deliberately ships no approve tool, so nothing can be changed in the estate without people signing off first.