title: "AI Product Channel for OEM Suppliers | ept AI" lastUpdated: "2026-04-23" route: "/solutions/ai-product-channel" description: "Turn your catalog into a product intelligence layer with catalog-scale discovery, real-time pricing, and a native MCP server." products: ["AI Product Channel"] owner: "rob@ept.ai" kpis: [ ] --- layout: "text-left-animation-right" h1: "Your customers' engineers find a part. Download a datasheet. Then disappear." subhead: "That invisible loss is the manufacturer's problem. AI Product Channel is a product intelligence layer on your domain for your customers' engineers — proven in production across catalogs with 100k+ parts showing real-time distributor pricing and stock." imagePath: "/images/pages/digital-sales-hero.svg" altText: "Natural-language product search returning ranked part matches for digital sales" ctaPrimary: "Book a live catalog demo" ctaPrimaryUrl: "https://go.ept.ai/meetings/eptai-demo/ept-ai-demo" ctaSecondary: "See the platform" ctaSecondaryUrl: "/platform/overview" embeddedMetrics: [ ] --- title: "What AI Product Channel is" subtitle: "OEM supplier websites are evolving from page-based destinations into product intelligence layers. AI Product Channel is that new layer: built for your customers' engineers today, and built for engineering AI workflows that are already arriving." features: [ { title: "The new interaction layer for OEM supplier websites", description: "Your page-based website still matters, but the primary interaction model is shifting toward natural language and structured tool calls. AI Product Channel sits on your domain as the interface layer for product discovery, evaluation, and next-step attribution.", benefits: [ "On your domain", "Built for OEM suppliers", "Designed for design win workflows" ], icon: "/images/icons/brain.svg" }, { title: "Catalog-scale product discovery", description: "This is not just Q&A on a known part. AI Product Channel narrows 100,000+ parts to the right candidates from a natural-language requirement, across product families, constraints, and application context.", benefits: [ "Natural-language requirement to shortlist", "Catalog-scale narrowing", "Discovery before the part number is known" ], icon: "/images/icons/search.svg" }, { title: "Real-time distributor pricing and stock in the same flow", description: "Product discovery is only useful if it connects to orderable reality. AI Product Channel brings distributor pricing and availability into the same interaction, so your customers' engineers and sourcing teams can act on what they find.", benefits: [ "Real-time distributor pricing", "Real-time stock visibility", "Fewer dead-end evaluations" ], icon: "/images/icons/trending-up.svg" }, { title: "Dual-interface deployment", description: "Deploy AI Chat on your public website for unauthenticated product discovery or inside your customer portal for authenticated account intelligence. Expose the same intelligence through a native MCP server for AI agents running design and procurement workflows.", benefits: [ "AI Chat for your customers' engineers", "Native MCP server for AI agents", "One intelligence layer behind both" ], icon: "/images/icons/target.svg" } ] --- title: "What one manufacturer proved as the AI-agent shift accelerates" subtitle: "The largest catalogs benefit the most." stories: [ { useCase: "Natural-language discovery", title: "A requirement, not a part number", scenario: "An engineer asked: \"I need a 24V DIN-rail power supply for factory automation, at least 240W, with the right compliance status, and I need to know what is available through distribution now.\"", persona: "Engineer", solution: "AI Product Channel returned a matching TDK Lambda part, the datasheet link, compliance status, and live distributor pricing and stock in the same interaction. No FAE call. No form.", outcome: "Under 5s to part, datasheet, and live stock", icon: "search" }, { useCase: "Catalog scale", title: "135,000 parts without a product finder maze", scenario: "Instead of drilling through filters family by family, the engineer starts with the application need and constraints in plain English.", persona: "Engineer", solution: "AI Product Channel narrows the catalog to the most relevant candidates, ranked against the stated requirement, and keeps the conversation grounded in product data and documentation.", outcome: "135,000-part catalog made searchable by intent", icon: "layers" }, { useCase: "Production deployment", title: "On the manufacturer's domain, with attribution", scenario: "The interaction happens where engineers already research parts, not in a side demo or disconnected prototype.", persona: "VP Digital", solution: "Discovery, documentation, and distributor intelligence run on the manufacturer's domain with a verified research trail that can connect the session to sample requests, design registration, and downstream commercial outcomes.", outcome: "Production live with a verified research trail", icon: "globe" } ] --- title: "The next design win may never touch your product finder" subtitle: "The same discovery problem is about to get structurally worse as engineering AI takes on more of the research workflow." benefits: [ { icon: "/images/icons/bot.svg", title: "AI agents are already doing product research", description: "Claude, Cursor, OpenAI Agents, and custom LLM pipelines are already handling comparison and early evaluation work that engineers used to do manually." }, { icon: "/images/icons/cpu.svg", title: "Most product discovery surfaces are unreadable to that workflow", description: "AI agents cannot use a product finder or fill out a sample request form. If your catalog is not AI-readable, it is skipped." }, { icon: "/images/icons/zap.svg", title: "Native MCP is how catalogs become usable to engineering AI", description: "AI Product Channel exposes the same product intelligence through a native MCP server, using structured tool calls instead of scraping." }, { icon: "/images/icons/target.svg", title: "Dual-interface wins the transition", description: "Serve your customers' engineers today through AI Chat and serve engineering AI tomorrow through the same product intelligence layer." } ] --- eyebrow: "Who it serves" title: "Three audiences. One product intelligence layer. Each getting what they actually need." subtitle: "AI Product Channel serves your customers' engineers, AI agents, and the manufacturer through the same catalog intelligence — grounded in your Product, Application, and Customer Living Profiles." roles: [ { id: "engineer", title: "For your customers' engineers", surface: "Catalog chat", color: "blue", pain: "Stop navigating PDFs, parametric tables, and stale distributor pages to find the right part.", outcome: { metric: "< 60s", label: "from plain-English question to a cited, compliance-checked shortlist" }, capabilities: [ { title: "Natural-language discovery", description: "Ask in plain English and get the right part — specs, documentation, and application fit in one flow, no parametric table drilling." }, { title: "Docs, compliance, and stock in context", description: "Datasheets, app notes, compliance status, lifecycle, pricing, and distributor stock surface together — cited, not pieced together." }, { title: "On your domain", description: "Your customers' engineers get the answer on your site or inside your customer portal — not on a competitor's search engine." } ], demo: { kind: "chat", channel: "#catalog-chat", user: "Maya · Sr EE", question: "Need a sealed IP67 encoder, SSI output, 14-bit, under 2ms latency for a wash-down servo loop. Shortlist?", answer: "Three fits: EC-45H (14-bit SSI, IP67, 1.2ms, RoHS/REACH, active), EC-38 (14-bit SSI but IP65 — borderline for wash-down), EZ-S2 (12-bit — skip). Stock: 240 at Arrow today, 3-wk lead direct.", citations: [ { id: "EC-45H Datasheet · §4", label: "DS" }, { id: "Compliance pack v3.2", label: "CP" }, { id: "Distributor stock · live", label: "D" } ], follow: "Sample request queued · EC-45H eval kit shipping to your MPLS lab." } }, { id: "agent", title: "For the AI agent", surface: "MCP tool call", color: "green", pain: "Engineering AI agents shouldn't have to scrape your PDFs and product pages to reason about parts.", outcome: { metric: "Native", label: "MCP server — structured tool calls, no brittle scraping" }, capabilities: [ { title: "Native MCP server", description: "A first-class MCP endpoint agents can discover and call — schemas, parts, docs, compliance, and lifecycle returned as structured payloads." }, { title: "Structured, not scraped", description: "Agents make typed tool calls instead of hallucinating from HTML, so downstream design and procurement workflows stay deterministic." }, { title: "Works with the stack you already use", description: "Compatible with Claude, OpenAI Agents, LangChain, Cursor, and custom pipelines — no bespoke connectors." } ], demo: { kind: "chat", channel: "MCP · tools/call", user: "claude/research-v2", question: "search_parts(category='isolated-dcdc', vin_min=36, vout=5, iout_min=2, iso_kv=3, footprint='open-frame')", answer: "3 matches. Top: IDC-5Q3 · vin 9–75V · 5V/3A · 3kV iso · -40/+85°C · 10M-hr MTBF · lifecycle: active · RoHS/REACH. Structured payload + citations returned to caller.", citations: [ { id: "MCP schema v1.3 · search_parts", label: "MCP" }, { id: "IDC-5Q3 Datasheet · §2", label: "DS" } ], follow: "Tool call logged · research trail attributed to Halden Controls · workflow: design-evaluation." } }, { id: "manufacturer", title: "For the manufacturer", surface: "Design-in attribution", color: "orange", pain: "You can't price a channel you can't measure — and today's catalog clicks don't tell you which accounts actually designed you in.", outcome: { metric: "Pay-on-win", label: "attribution-first pricing, from first query to production order" }, capabilities: [ { title: "Verified research trail", description: "Every engineer and agent interaction is captured as a structured trail — discovery, evaluation, sample, design registration, order." }, { title: "Attribution across the funnel", description: "Link design-register events and production orders back to the specific queries and sessions that drove them — at the account level." }, { title: "Authenticated when it matters", description: "Supports signed-in portal workflows so account context, entitlements, and NDA-gated docs flow through the same intelligence layer." } ], demo: { kind: "brief", account: "Halden Controls · Design-in candidate · Q2", rep: "Channel team · ept AI attribution", fitScore: 88, fit: "Halden's new wash-down servo line queried 14-bit SSI encoders 11× in the last 30 days. EC-45H matched on query 3; 2 sample requests in flight; design-register event expected within 30 days based on research-trail cadence.", objections: [ { q: "Stocking depth if we production-qualify?", a: "48k at Arrow + 12-wk lead direct; bonded inventory option documented in the channel pack." }, { q: "Approved second-source for dual-sourcing?", a: "EC-45HX is pin-compatible from our second fab — both on Halden's approved components list." } ], citations: [ { id: "Research trail · Halden · 30d", label: "RT" }, { id: "Sample req · SR-40218", label: "SR" } ] } } ] --- eyebrow: "AI Product Channel FAQ" title: "Questions we get from product and marketing leaders" items: [ { question: "What is AI Product Channel?", answer: "A product intelligence layer that sits on your domain and exposes your catalog through two interfaces: an AI chat that engineers use to find parts in plain English, and a native MCP server that AI agents use to query your catalog with structured tool calls." }, { question: "How large a catalog can it handle?", answer: "Proven in production across catalogs with 100k+ parts. TDK Lambda runs ~135,000 parts through AI Product Channel with real-time distributor pricing and stock in the same flow." }, { question: "Why is a native MCP server important?", answer: "Engineering teams are increasingly using AI coding and design agents — Claude, Cursor, OpenAI Agents, LangChain. An MCP server lets those agents research your catalog with structured tool calls instead of scraping your PDFs. If your catalog is not AI-readable, your parts are not being considered." }, { question: "How is pricing structured?", answer: "Attribution-first: you pay when we prove we drove measurable outcomes like design registration or an order. The alternative is continuing to lose engineers who land on a datasheet and disappear." }, { question: "Do you support authenticated workflows?", answer: "Yes. AI Product Channel runs on your domain and can gate catalog access behind authentication when account context matters, while remaining open for discovery where that is the right trade-off." }, { question: "What does deployment look like?", answer: "We ingest your catalog, datasheets, compliance and lifecycle data, and distributor feeds into a Living Profile per part. First demo on your catalog in weeks, not quarters." } ] --- backgroundImage: "/images/feature_highlight.png" backgroundColor: "transparent" headline: "See AI Product Channel on your catalog." copy: "Bring a real requirements set, part family, or distributor question. We will walk through natural-language discovery, live pricing and stock, and the native MCP server on a your catalog." ctaText: "Book a live catalog demo" ctaUrl: "https://go.ept.ai/meetings/eptai-demo/ept-ai-demo" ctaSecondary: "See the full platform" ctaSecondaryUrl: "/platform/overview" dataEvent: "cta-ai-product-channel-demo"