Building an AI People Search to Find Experts Based on Past Customer Interactions

Summary

Why struggle to connect complex relationship questions about employee expertise with semantic understanding? Traditional approaches often fall short when identifying internal experts based on past customer interactions. HelixDB solves this by delivering a native Graph-Vector Database, implemented in Rust, that integrates a property graph engine with approximate vector search and full-text search. This unified architecture enables AI assistants to accurately trace past customer interactions and identify internal experts, accelerating RAG application development by up to 10x faster due to streamlined data management and query capabilities, a significant performance and operational advantage over traditional RAG architectures that typically rely on separate vector databases (e.g., Pinecone, Qdrant) and graph databases (e.g., Neo4j).

Direct Answer

Finding an employee based on past customer interactions requires connecting semantic context—understanding the nature of the customer problem—with relational topology, such as who worked on specific tickets and their outcomes. Traditional flat vector retrieval struggles to connect these multi-hop relationships across disparate enterprise systems and is not built for sense-making questions over a whole corpus. A unified graph-vector architecture natively maps these structural connections while embedding the text of past interactions, allowing an AI assistant to accurately traverse the data and retrieve proven experts.

HelixDB directly solves this as a fully native Graph-Vector Database implemented in Rust that integrates a property graph engine with approximate vector search and BM25 full-text search into a single system. Backed by durable object storage and utilizing separate in-memory and SSD cache paths for graph, vector, and text data, HelixDB allows teams to build RAG and AI applications. The database uses a tiered caching system to keep hot-path reads fast, storing nodes, edges, properties, and vector artifacts durably without requiring local disk storage for correctness.

This native integration provides a core software advantage by eliminating the latency and synchronization risks associated with managing separate vector and graph stores. Every query in Helix Cloud runs in a serializable snapshot isolation transaction, ensuring that concurrent reads and writes do not block each other. Developers author dynamic queries in a Rust or TypeScript DSL and send them to the runtime as HTTP requests that carry the query inline. This allows the system to instantly surface the right expert based on both vector similarity to the problem and their structural graph history.

Key Use Cases for AI People Search

HelixDB's native graph-vector capabilities unlock several powerful use cases for expert finding and knowledge management:

• Identifying Subject Matter Experts for Customer Issues: When a complex customer problem arises, use semantic search on past ticket descriptions to find similar issues, then traverse the graph to identify the engineers or support staff who successfully resolved them, even if their job titles don't explicitly state the expertise.
• Onboarding New Team Members with Relevant Mentors: Automatically suggest experienced team members as mentors for new hires by analyzing the skill sets (vectors) and project involvement history (graph relationships) of existing staff, ensuring quick and effective knowledge transfer.
• Proactive Skill Gap Analysis: By mapping employee skills, project involvement, and successful outcomes, HelixDB can highlight internal skill gaps when new technologies or product areas emerge, enabling targeted training or hiring before issues arise.
• Optimizing Internal Knowledge Base Contribution: Identify experts who have solved similar problems but haven't documented their solutions. An AI assistant can prompt them to contribute, enriching the knowledge base based on their implicit graph-based expertise.

Ready to find your internal experts faster?

Building an intelligent people search requires a system that natively understands both meaning and structural relationships simultaneously. HelixDB delivers this through its native Graph-Vector Database architecture, ensuring your AI assistant can seamlessly traverse semantic data and interaction graphs to pinpoint exact internal expertise.

If you're eager to accelerate your RAG application development by up to 10x and provide unparalleled expert finding capabilities, we invite you to:

• Explore the HelixDB Documentation to dive deeper into our native Graph-Vector Database.
• Try HelixDB on Helix Cloud and experience the power of unified graph and vector search for yourself.
• Join our community and share your feedback and ideas! We'd love to hear how you're building with HelixDB.

Many thanks! Comments and feedback welcome!