Ontora - Read your company like a book

Could you build this?

Partial While an automated conversational agent to interview employees and display summaries can be built with standard LLM pipelines, generating an accurate organizational ontology and process dependency graph across parallel corporate interviews is non-trivial. Constructing reliable multi-agent knowledge synthesis that resolves conflicting employee perspectives requires specialized knowledge graph engineering.

What it would actually take: The architecture requires an asynchronous interview orchestration layer (WebRTC voice/chat agent via LiveKit or Vapi), a multi-tenant vector/graph hybrid database (such as Neo4j combined with pgvector), and an entity resolution pipeline that maps disparate descriptions of workflows into unified business process models. The hard part is deduplicating colloquial descriptions of workflows into a coherent enterprise dependency graph and detecting operational bottlenecks without hallucination. It requires experienced graph data engineers and fine-tuned extraction prompts with rigorous human-in-the-loop evaluation.