Your products are invisible
to AI agents. Fix that.
Beam enriches your catalog so humans find what they mean — and AI agents find what they need. One API, every discovery channel.
Stop speaking database.
Start speaking human.
Your catalog has products. Beam gives them context — who they're for, why someone would buy them, and every weird way a real human might search for them.
Plug In Your Store. We Handle the Rest.
Products flow in via API. Seconds later, AI turns bare listings into rich, searchable knowledge — no manual tagging required.
From "Nike Pegasus" to "Marathon Training Shoe for Neutral Runners"
Three layers of AI analysis turn every product into a rich knowledge node — what it is, who it's for, and every way a real person might search for it.
Search That Understands "Shoes for Standing All Day"
Semantic vectors, keyword matching, and cross-encoder reranking fused into one engine. Your customers type naturally — Beam figures out what they actually mean.
Protocol Whack-a-Mole? Not Your Problem.
ACP, UCP, MCP, and whatever acronym drops next week — Beam keeps your catalog fluent in all of them. You ship product, we ship compliance.
A Shopping Assistant That Actually Gets It
"Gift for my yoga-obsessed sister" isn't a search query — it's a conversation. Beam's chat turns your catalog into an assistant that understands context, not just keywords.
Your Store, Inside ChatGPT and Claude
Don't just be listed — own the experience. Build brand storefronts inside AI platforms where your customers are already shopping.
See What Your Customers Can't Find
Every zero-result query is a missed sale. Track what people search for, catch enrichment gaps, and keep AI hallucinations away from your product data.
Ship in minutes. Not sprints.
One API for catalog enrichment, semantic search, and AI agent distribution. No ML team required — just your existing stack and a few lines of code.
1import { Beam } from '@beamhq/node';23const beam = new Beam({ apiKey: process.env.BEAM_API_KEY });45// Beam automatically extracts use-cases, audience,6// and aesthetic from your raw product data.7await beam.catalog.ingest({8 id: "prod_123",9 title: "Nike Air Max 90",10 description: "Classic silhouette with visible Air cushioning.",11 price: 129.99,12 images: ["https://example.com/nike.jpg"]13});1415console.log('Product enriched and indexed for AI agents!');
The 3 Layers of
Agentic Commerce
AI in e-commerce isn't a chatbot in the corner. It's an invisible engine across your entire stack — from how products are stored, to how they're found, to who finds them.
Frequently asked questions
Everything you need to know about Beam.
Those platforms index what you give them — garbage in, garbage out. Beam enriches your catalog before indexing. It figures out what products actually are, who they're for, why someone would buy them, and every weird way a human might search for them. No synonym lists to maintain, no facets to configure manually.
Layer 1 (Understanding) nails down precise categories, structured attributes, and visual analysis from product images. Layer 2 (Context) is where it gets interesting — Beam infers use-cases, target audiences, occasions, and problems the product solves. Layer 3 (Discovery) generates colloquial terms ("dad shoes"), problem phrases ("won't leak in my bag"), comparisons ("cheaper than Dyson"), and common misspellings real people actually type.
Never. Your data is sacred — that's a core design principle. Beam adds context alongside your original data but never overwrites it. If you say the product is "Red," Beam won't change that because an image looks slightly orange. Everything is fully exportable and deletable on demand.
Beam exposes your enriched catalog through MCP (Model Context Protocol), ACP (Agentic Commerce Protocol by OpenAI & Stripe), and UCP (Unified Commerce Protocol by Google & Shopify). When Claude, ChatGPT, or any autonomous shopping agent searches for products, they hit Beam's endpoints and get semantically rich data that helps them make better recommendations.
Three retrieval methods in one: semantic vector search (understands meaning), full-text keyword matching (catches exact phrases), and direct name matching. Results are merged via Reciprocal Rank Fusion with configurable weights (default: 50/35/15). Then an optional cross-encoder reranker re-scores the top candidates — the same technique Google and Bing use.
You do — full stop. Your product data never trains third-party models. Everything is scoped to your project, fully exportable, deletable on demand. Beam operates on your data, not with it.
You pay for what you use — products enriched, searches performed, API calls made. No feature gates. Every plan gets the full stack: enrichment, search, recommendations, chat, and multi-channel distribution. Volume pricing, not capability pricing.
Yes — early access is open. The full intelligence stack is live: three-layer enrichment, hybrid search, AI query understanding, analytics dashboard, and official SDKs. Conversational commerce, context-aware recommendations, and protocol distribution are shipping soon. Join the waitlist for priority access.
Every product findable. Every search understood.
Early access is open. Start enriching your catalog and powering AI-native discovery — no credit card, no ML team, no 6-month integration.