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Documentation Index

Fetch the complete documentation index at: https://docs.ontora.com/llms.txt

Use this file to discover all available pages before exploring further.

Ontora’s pipeline is built around three layers:
  1. Conversation layer — voice or chat interviews conducted by an autonomous agent.
  2. Knowledge layer — transcripts processed through chunking, embedding, and entity extraction into Postgres + Neo4j.
  3. Insight layer — synthesis outputs (cartography, roadmap, personas) and a GraphRAG query endpoint over the campaign.

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Pipeline

When a campaign completes a conversation, a job pipeline kicks off: 
INTERVIEW_COMPLETED
  ├─► CHUNK_DOCUMENT  (sentence-boundary, 1000 chars, 200 overlap)
  │     └─► EMBED_CHUNKS  (text-embedding-3-small → pgvector)
  └─► EXTRACT_ENTITIES_RELATIONS  (gpt-4o-mini → JSON)
        └─► UPSERT_GRAPH  (Neo4j, deduped by stable keys)
              └─► SYNTHESIZE  (cartography + roadmap + personas)
                    └─► webhook: synthesis.completed
Every stage is idempotent and retried up to 3× with exponential backoff.

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Multi-tenancy

Every record carries a workspace_id. API keys are workspace-scoped — there is no cross-workspace data access. See Workspaces.

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What gets stored

StoreHoldsUsed for
Postgres (pgvector)Documents, chunks, embeddings, jobs, conversationsVector search, transactional state
Neo4jEntities (Person, Topic, Process), relationsGraph traversal during GraphRAG
Object storageRaw transcripts, audioExport endpoints

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Three integration surfaces

The same data is exposed through three interfaces, all backed by the same workspace API key:
  • REST API — request/response for programmatic integration
  • MCP server — tool-calling interface for AI agents
  • CLI — terminal and CI-friendly wrapper around the REST API
Pick whichever fits your environment; mix them freely.