Traditional search only works when you already know the exact part number. That is rarely the case.
Keyword search misses parts when descriptions do not match exactly. Different terminology for the same part returns zero results.
Finding equivalent parts across manufacturers requires checking multiple catalogs and reference documents by hand.
Traditional search does not understand that a "filter" for a Cat 320 is different from a "filter" for a Komatsu PC200.
Keyword results give no indication of match quality. You have to manually verify every result before ordering.
Three specialized search agents work in parallel, combining their results for maximum accuracy.
Powered by 1024-dimensional vector embeddings, the semantic search agent understands the meaning behind your query. "Hydraulic cylinder seal kit" matches parts described as "cylinder repair kit" or "hydraulic rod seal set."
The Neo4j graph agent traverses manufacturer-model-part relationships to find parts connected to your equipment. It discovers cross-references and compatibility matches that flat search cannot.
Ask for parts the way you would describe them to a colleague. The query understanding engine parses your intent, extracts equipment context, and routes the search to the right agents.
Specify your equipment and the search automatically filters for compatible parts. Serial number ranges, model years, and configuration variants are all considered.
Multi-agent AI delivers results that traditional search cannot match.
Confidence-scored results with multi-signal verification across semantic, structural, and graph-based matching.
PostgreSQL, Pinecone, and Neo4j searched in parallel by specialized agents for comprehensive coverage.
Describe parts in your own words. No need to memorize part numbers or use specific catalog terminology.
Common questions about AI-powered parts search.
Traditional keyword search requires exact part numbers or precise terminology. PartsIQ's AI search uses semantic understanding to interpret natural language descriptions like "hydraulic pump seal for excavator." It searches across vector embeddings, graph relationships, and full-text indexes simultaneously, finding parts that keyword search would miss entirely.
PartsIQ's multi-agent orchestrator searches three databases in parallel: PostgreSQL for structured inventory data and full-text search, Pinecone for semantic vector similarity matching, and Neo4j for graph-based relationship traversal (manufacturer-model-part connections, cross-references, and compatibility data).
Yes. When you specify a vehicle (e.g., "Cat 320GC excavator"), the AI search engine uses that context to filter results by manufacturer, model, and serial number range. The Neo4j graph database maps part-to-vehicle relationships, ensuring search results are compatible with your specific equipment.
Every search result includes a confidence score (0-100%) that indicates how well the part matches your query. The score is calculated from multiple signals: semantic similarity, exact text matches, graph relationship strength, and vehicle compatibility. High-confidence results appear first, helping you quickly identify the right part without manual verification.