Our Story
We're closing the gap between how products exist and how humans search.
A customer searches "comfortable shoes for standing all day." Your catalog says "Nike Air Max 90 — Men's Lifestyle Sneaker." They never find each other. We fix that.
The Problem
Ecommerce search hasn't evolved in 20 years.
The average product listing has a name, a price, some specs, and maybe a paragraph of marketing copy. That's all a traditional search engine has to work with.
When someone searches "gift for coffee lover under $50," nothing matches — even though you carry the perfect artisanal pour-over set. The vocabulary gap between how people search and how products are described is the single biggest source of lost revenue in ecommerce.
Customers use slang ("dad shoes"), describe problems ("won't leak in my bag"), make comparisons ("cheaper than Dyson"), or express intent ("something nice for a first date"). None of that is in your product data.
68%
of ecommerce searches return irrelevant results
The Journey
From insight to infrastructure
We realized that 68% of ecommerce searches return irrelevant results — not because the products don't exist, but because the data describing them is too sparse for modern search to work.
What if AI could understand products the way a great salesperson does — not just what they are, but who they're for, when they're needed, and every weird way someone might ask for them?
We built a three-layer enrichment pipeline that turns bare listings into rich knowledge nodes. Then we wrapped it in hybrid search that actually understands human intent.
AI agents are already shopping for humans. Beam ensures your products show up when Claude, ChatGPT, or any autonomous agent goes looking — structured, rich, and ready to be recommended.
How It Works
Four steps. Zero complexity.
Products enter Beam through a simple API. Seconds later, they're enriched, searchable, and discoverable by every AI agent on the planet.
Sync
Push your raw catalog via API. Names, prices, images — whatever you have.
Enrich
AI adds use-cases, audiences, occasions, and the vocabulary humans actually type.
Discover
Hybrid search combines vector, keyword, and cross-encoder reranking in one call.
Distribute
Products flow to AI agents via MCP, ACP, and UCP — discoverable everywhere.
3
Layers of AI Enrichment
50/35/15
Hybrid Search Fusion
<2s
Enrichment Per Product
0
Synonym Lists to Maintain
The Intelligence Model
Three layers. One knowledge graph.
Every product goes through three layers of AI analysis, each building on the last. The result: products that are understood, not just categorized.
Understanding
What is this product?
- Precise categorization beyond generic labels
- Structured attributes from text and images
- Visual analysis: stitching, textures, finishes
- Quality scoring with confidence metrics
Context
Who, when, and why?
- Specific, actionable use cases
- Target audience personas, not demographics
- Occasion and seasonality mapping
- Problems it solves and pain points it addresses
Discovery
How would someone search for this?
- Colloquial terms: "dad shoes," "teddy jacket"
- Problem phrases: "won't leak," "sweat-proof"
- Comparisons: "AirPods alternative"
- Common misspellings and abbreviations
How Beam Compares
Not an incremental improvement. A different foundation.
Our Principles
What we believe
Your Data Is Sacred
Merchant data is ground truth. Beam enhances product data but never contradicts explicit information. If you say it's red, we won't call it orange. Your catalog, your rules.
AI Is the Foundation, Not a Feature
We didn't bolt AI onto existing search. We built the entire stack around intelligence — enrichment, search, recommendations, and distribution — from day one.
Provider Agnostic
Beam abstracts AI providers behind clean contracts. The enrichment engine, embeddings, and reranker are all swappable. You're never locked into a single vendor.
Multi-Channel by Default
Products shouldn't live in one storefront. Beam distributes through MCP, ACP, and UCP — making your catalog discoverable wherever AI agents shop.
The Future
By 2027, 30% of product searches will start outside traditional search engines.
In AI assistants. In conversational interfaces. In autonomous agents that shop on behalf of humans. Products that aren't accessible through these channels simply won't exist for a growing segment of buyers.
Beam's enrichment data is specifically designed to make products intelligible to AI systems, not just searchable by humans. We're building the infrastructure for how products will be discovered in the AI era.
MCP
Model Context Protocol
ACP
Agentic Commerce
UCP
Unified Commerce
We're building the intelligence layer for every product on Earth.
Early access is open. Start enriching your catalog and powering AI-native discovery — no credit card, no ML team, no 6-month integration.