OutcomeGraph-backed retrieval design for support, documentation, and policy knowledge
Signal7 entity types modeled
Signal3 source systems mapped
SignalEvaluation rubric defined

Challenge

Support knowledge was fragmented across tickets, documentation, spreadsheets, and internal notes. Plain keyword and vector search struggled when questions required connected context.

Approach

  • Modeled key entities such as customer, product, feature, issue, policy, owner, and resolution.
  • Designed a hybrid retrieval pattern combining semantic search with graph traversal.
  • Defined source-of-truth rules, permission boundaries, and freshness checks.
  • Created answer evaluation criteria for relevance, citation quality, and escalation behavior.

Architecture and deliverables

  • Ontology and entity relationship model
  • GraphRAG retrieval pattern
  • Source citation and lineage model
  • Knowledge stewardship process

Result

The team received a production-ready blueprint for AI support intelligence that emphasized accuracy, traceability, and maintainable knowledge operations.