Partsgraph.ai - 2026 Pricing, Features, Reviews & Alternatives

Partsgraph is an agent-ready parts data infrastructure platform that transforms manufacturer and distributor catalogs into machine-readable, canonical data structures for AI agents. The platform ingests technical documentation, parametric specifications, CAD files and compliance documents to build a unified Parts Graph that AI agents can query and cite. It serves the graph through Model Context Protocol endpoints, server-rendered agent-readable web pages and AI shopping feeds. Partsgraph operates as an overlay to existing systems without requiring any website rewrite.

The data ingestion process supports PDF datasheets, Product Information Management exports, Enterprise Resource Planning data, website content and CAD/BIM files. An evaluation gated extraction workflow assigns confidence scores to each extracted field before promotion to production. Typed parametric classification follows ETIM and eCl@ss standards while entity resolution consolidates manufacturer part numbers, packaging variants, supersessions and cross references into unified part identities. Every attribute retains full provenance including source document, page reference and file hash.

Distributed output surfaces include hosted MCP endpoints exposing parametric search, part lookup, alternates identification and compliance document retrieval. Server-rendered agent-readable pages provide structured JSON-LD markup and markdown for AI crawlers and shopping feeds refresh every fifteen minutes for assistant consumption. Compliance exports generate ETIM xChange, EU Digital Product Passport and Asset Administration Shell submodels under relevant industry standards. OAuth gated access controls allow public exposure of technical specifications while restricting pricing and inventory data to authorized channels.

Continuous accuracy monitoring runs golden set engineering queries against major AI assistants to detect discrepancies and regressions against source documentation. Agent analytics track querying systems, query types, data gaps and specification attribution for agent-mediated interactions. Tool call responses target a p95 latency below fifty milliseconds at edge locations and parametric extraction follows a minimum precision threshold for production data. Implementation follows a fixed scope model that connects to existing data sources, configures extraction and resolution pipelines and deploys output surfaces for operational use.