Teifi×Rack Attack
Teifi × Rack Attack — Agentic fitment

Find the rack that actually fits your vehicle.

Tell us your vehicle — or just ask in plain language. The same fitment engine powers this page, our on-site AI assistant, and a public MCP server that ChatGPT and Claude can call.

How agentic discovery works
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Under the hood

Powered by Teifi

Two purpose-built Teifi products turn a complex fitment catalog into a catalog an AI agent can query in real time — at the scale and freshness merchants actually need.

Teifi Parts

YMM elastic search across millions of SKUs

Instant, typo-tolerant fitment search at full-catalog scale. Every vehicle-finder dropdown, every agent tool call, and every MCP query on this demo resolves through Teifi Parts — returning the exact rack systems that fit a specific Year / Make / Model / body / roof configuration in milliseconds.

  • ·Elastic search handles brand misspellings, abbreviations, and partial queries
  • ·Fitment graph covers YMM + body type + roof / hitch / bed configuration
  • ·Structured JSON responses are designed for agent and MCP consumption

Teifi Bridge

Two-way sync from PIM / ERP / data lakes

Keeps the product catalog and the fitment graph continuously in sync from the merchant's source systems. Bridge ingests from any PIM, ERP, or data lake — normalises it — and pushes structured fitment data into Teifi Parts. It also hosts the whole experience: this storefront, the MCP server, and the agent.

  • ·Connectors for Inriver, Jitterbit, Shopify, and custom REST / GraphQL sources
  • ·Bidirectional: enriched fitment data flows back to the merchant's PIM
  • ·Hosts the MCP server, the on-site agent, and the storefront front-end

On the roadmap

Connect the merchant's customer-service platform and internal knowledge base so the assistant answers from their own fit advice, install guides, and support history — the same agent, now backed by the team's expertise. Available to wire in; not enabled in this demo.

Agent-ready

Built to be used by AI agents

Three surfaces make this catalog legible and operable to ChatGPT, Claude, and Copilot.

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Instruction surface

agents.md / llms.txt tell a visiting agent what this store is and how to query fitment.

/agents.md
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MCP capability

A public Model Context Protocol server exposes the fitment tools to any external agent.

/api/mcp
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On-site agent

An embedded Claude assistant resolves YMM + disambiguation and recommends real products.

Learn more →