Use cases where connecting AI to live data needs more than RAG or raw SQL — ontology playbooks, policy as code, query-in-place across sources, and proof on every answer.
Each scenario maps to the same pattern: define vocabulary and access in git, bind live sources without moving data, query through MCP in natural language.
Clinical AI over PHI fails if one doctor can see another’s patients. Care-team relationships and ReBAC enforce scope before any query — governance first, not “stay in bounds” in the prompt — with auditable proof on lab and patient answers.
Sales teams want natural-language access to live CRM data, but agents connected to Postgres or Salesforce can see every account. Playbooks scope queries to rep-owned relationships via policy as code — with proof on every answer.
Leads sit in Salesforce, enrichment in Postgres, exports in CSV. One ontology playbook federates all sources in place so teams ask cross-system questions without ETL, brittle glue code, or hallucinated joins.
Analysts trace customers, accounts, transactions, and alerts across MongoDB and Postgres daily. Governed graph queries replace unconstrained agent SQL — scoped to assigned cases with logged sources and relationship paths.
The same customer appears under different IDs in CRM, core banking, and a KYC API. An ontology playbook defines identity links across bindings so reasoning stays explicit, testable, and versioned in git.
HR bots over SQL or CSV org charts often expose the full directory. ReBAC over manager–employee relationships ensures managers see only their subtree — same governed layer for Slack, Teams, or internal copilots via MCP.
Shipments live in MySQL; supplier contracts in Postgres. Ops asks which delayed shipments are covered by which contract — through a shared ontology over live sources, not a custom script every time.
Teams connect LLMs to production databases before agreeing what “customer” or “order” means. Agents propose playbooks via MCP; humans review in git, test bindings, then enable queries — policy ships as code first.
Claude, Cursor, and internal bots shouldn’t each reimplement SQL, schema context, and auth. One MCP server and one set of playbooks serve all agents — update access rules once in git instead of per integration.
Adjusters ask whether a procedure is covered for a member across plan, claim, and procedure data — wrong answers have legal exposure. Access is scoped to assigned claims; every answer includes proof of playbook, subject, and data path.
Command staff need fused answers across logistics, asset tracking, and personnel systems — but access must follow mission assignment and clearance, not prompt discipline. Playbooks encode unit-to-asset-to-mission relationships; ReBAC limits what each operator’s agent can query, with proof for after-action review.
Warehouse robots and physical AI systems need structured knowledge of sites, assets, tasks, and sensors — not unconstrained database access. A semantic layer binds live telemetry and facility data in place; each robot or zone gets a scoped ontology playbook so reasoning stays bounded to what it may act on.
Drone fleets generate telemetry, flight plans, and incident logs across Postgres, REST APIs, and file stores. Operators should only see aircraft and missions they control. Governed graph queries link pilot → assigned_uas → mission → telemetry without copying streams into an LLM context — auditable paths on every answer.
Walk through a scripted demo — clinical PHI, ontology playbook, ReBAC, and governed query with proof. Or install the CLI and connect your own sources.