Distributors & Multi-Supplier B2B
AI agents only recommend products they can verify.
AI answer engines — ChatGPT, Perplexity, Gemini, Claude — can only recommend products they can verify. Verification requires complete attributes, resolved product identity, and accurate classification. Claro is the catalog data layer that makes your products verifiable: it fills missing attributes from source documents, resolves product identity across suppliers, validates classification, and keeps everything current as the catalog changes. When the underlying data is clean, complete, and canonical, AI agents cite your products — instead of competitors whose specs are clearer.
The problem
Bad product data makes you invisible to AI — not just search.
ChatGPT, Perplexity, and Gemini are now shopping destinations. They recommend products based on what they can verify. If your product data is incomplete, fragmented, or wrong — they recommend someone else.
AI agents can't verify missing specs
Wrong category means wrong audience
Competitors with cleaner data get cited instead
How it works
From unverifiable product data to AI-citable catalog.
Claro doesn't track AI visibility. It fixes the underlying data that determines whether AI can cite your products at all.
Step 1
Resolve product identity
The same product must be the same product, across every supplier and source. Resolved identity means AI agents encounter one canonical entity — not three fragmented records with conflicting specs.
Step 2
Fill and verify attributes
Complete specs. Filled dimensions. Verified certifications. Claro pulls missing attributes from supplier documents and approved sources, with provenance on every value — so AI agents can cite the source, not just guess.
Step 3
Validate classification
Products in the right categories get recommended to the right audiences. Claro classifies against your taxonomy with confidence scoring, and repairs classification drift as the catalog grows.
Step 4
Monitor and maintain
AI visibility degrades when catalog data decays. Claro detects schema drift, missing attributes, and quality regressions before they affect what AI engines can verify — and automatically keeps the canonical layer current.
What you get
Products AI agents can cite. Not products they have to guess about.
When ChatGPT or Perplexity answer "what's the best [product type] for [use case]," they recommend products whose specifications they can verify against the user's query. If your specs are missing, your dimensions are inconsistent, or your product appears under three different names across your catalog, AI agents skip you. Claro builds the canonical data layer that makes your products verifiable — resolved identity, complete attributes, validated classification, continuous monitoring — so AI agents can recommend you with confidence.
Who is it for
Built for teams losing ground to AI-first search.
Search, e-commerce, and data teams at retailers, distributors, and marketplaces where AI shopping agents, AI-powered procurement, and LLM-based discovery are becoming primary traffic and sales channels.
Seeing traffic shift from Google to ChatGPT and Perplexity
AI recommendations returning competitors with cleaner data
Launching AI-powered search or recommendations in-product
Need provenance for AI citations and agent explainability
The teams winning at AI search have better catalogs underneath.
GEO tools track and optimize AI visibility. Claro builds the foundation that determines whether AI visibility is even possible. When product identity is resolved, attributes are filled and verified, and classification is accurate, AI agents can recommend your products confidently — and cite the source.
Problem Claro solves:
AI agents skipping your products because specs are incomplete
competitors with cleaner product data getting cited instead
AI recommendations crossing category boundaries from bad classification
catalog data decaying faster than AI visibility tools can optimize it
Hours of Work. Done in Minutes.
The canonical product data layer that AI search, agent workflows, and recommendations depend on.
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Resolved Identity for AI
One canonical entity per product, across every supplier and source. AI agents stop encountering conflicting records for the same product.
Continuous Monitoring
Catalog drift detected before AI visibility degrades. Quality stays stable as suppliers change and assortments grow.
Verifiable Attributes
Every value carries a confidence score and a link to its source. ChatGPT can cite your spec because Claro can prove where it came from.
Validated Classification
Right category, right taxonomy, right audience. Claro classifies and repairs classification drift — so AI recommendations stay accurate.
GEO-Ready API
Canonical entities, attributes, and provenance exposed via API to your search infrastructure, recommendation engine, or AI agent framework.
FAQ
Frequently asked questions
What's the difference between GEO and traditional SEO for product data?
Why does AI skip products with incomplete specs?
Is Claro a GEO tracking tool like Glara or Peec AI?
Does Claro work with our existing search infrastructure?
How quickly does AI search benefit from better catalog data?
