RAG endpoints. MCP interfaces. Vector index. CI-ready. Built for teams implementing AI systems on top of enterprise content, with governance, observability and controlled output consistency.
of AI representation comes from sources outside your stack
Your enterprise data is governed. Your AI representation is not. The gap between the two is the value at risk.
now query brand data simultaneously
Different AI systems retrieve different sources. Without a governed surface, outputs diverge across systems, and so does brand truth.
forensic trace across every agent interaction
Compliance and security need to replay exactly what AI was told, when, by whom. Most stacks have no equivalent for the agent surface.
Five integration points for engineering teams running AI on top of enterprise content.
POST /v1/content/retrieve. Top-k retrieval with relevance scoring. Filter by category, status, locale, tag. Designed for production-grade LLM grounding.
Discoverable by external AI systems via Model Context Protocol. Cryptographically signed responses. Per-endpoint auth.
Auto-built and maintained. Default model: kodiac-embed-v3-large (1024-dim). Semantic chunking. Cross-encoder reranking.
Event subscriptions on record.created / updated / published. CI test scenarios for agent outputs. Pass/fail validation on responses.
SAML 2.0 SSO, MFA, SCIM 2.0, SIEM export, audit logging (365-day retention), IP allowlisting, data residency controls.
Your Brand Agent exposed via MCP and REST. Customer AI systems discover and query it. You see every interaction.
Architecture deep-dive with our CTO. SOC2 / SCIM / MCP / RAG endpoint live demo.
