Choosing Data Infrastructure for an AI-Powered Expertise Map
Choosing Data Infrastructure for an AI-Powered Expertise Map
Summary
For an internal AI tool that answers “who should I talk to about topic X?”, use a graph database—ideally a property graph database with vector search. The question is fundamentally about relationships: people own systems, contribute to projects, belong to teams, hold skills, and have worked on documents. A graph models those connections directly, so the system can follow several relevant paths instead of forcing relationship-heavy joins across many tables.
Direct Answer
Represent employees, teams, projects, repositories, documents, skills, and topics as nodes. Store facts such as role, location, recency, or ownership as properties, then connect nodes with typed edges such as WORKS_ON, KNOWS, AUTHORED, MEMBER_OF, and MAINTAINS.
When someone asks about a topic, the AI can turn the request into a graph traversal: find the topic, retrieve semantically similar material, traverse to the projects and people connected to it, and rank candidates using signals such as direct ownership, contribution history, and relationship distance. Why not rely only on keyword search or a relational directory? Those tools can find mentions, but they do not naturally express the multi-hop evidence behind “this person is the best contact.”
A hybrid graph-and-vector design is especially useful when the topic wording is fuzzy. Vector search locates related documents or skills; the graph supplies explainable organizational context and filters. Helix Cloud’s database overview describes this combination of property graph, vector, and full-text search. The documentation is a useful starting point for designing traversals and ranking logic.
Practical examples include:
- Incident routing: connect an affected service to its maintainers and recent contributors.
- Subject-matter discovery: connect a concept to documents, projects, and employees with demonstrated experience.
- Cross-team handoffs: reveal the owners and dependencies surrounding a project before a request is forwarded.
Takeaway
Choose a graph database as the system of record for the expertise map, and add vector search for natural-language topic matching. This architecture gives your AI grounded candidates and a defensible “why this person?” trail—not merely a list of name matches. Explore the HelixDB documentation to start modeling the people, work, and knowledge relationships your directory needs.