Launched this week

CITAQ

Launched this week

Verify product catalog claims for AI shopping agents.

2 followers

Verify product catalog claims for AI shopping agents.

2 followers

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CITAQ converts product catalog claims into machine-verifiable citation graphs. This allows AI agents to retrieve verifiable product facts instead of marketing claims.

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Launch tags:Sales•SEO•E-Commerce

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MongoDB Atlas — Build AI apps, not pipelines. Unified data on Atlas.

Build AI apps, not pipelines. Unified data on Atlas.

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Hey Product Hunt! 👋 Wisdom here, founder of CITAQ. The idea came from a frustrating observation: AI shopping agents are already being used to recommend and purchase products — but they're doing it by reading marketing copy. That's a structural problem. Marketing copy is written to persuade, not to inform. When an AI agent retrieves a product claim — "industrial-grade," "100% organic," "waterproof to 50m" — it has no way to distinguish a verified specification from a claim someone typed into a Shopify description field. It just trusts it. That gap is what CITAQ is built to close. The problem in plain terms: Product catalogs are the foundational data layer of e-commerce. But they were never designed for machine consumption. They were designed for human browsers who apply judgment. AI agents don't apply judgment — they retrieve, reason, and act. If the data they retrieve is unverified, the downstream decisions are built on sand. What CITAQ does: We convert product catalog claims into cryptographically-anchored citation graphs, then serve them to AI agents via the Model Context Protocol (MCP). When an agent queries a product through CITAQ, it receives structured, verifiable facts — not marketing language. Each claim carries a provenance trail. The agent knows what the claim is, where it came from, and whether it has been verified. How the approach evolved: I started building this as a Shopify-focused recommendation readiness tool — helping merchants optimize their catalogs for AI discovery. The more I dug into the architecture, the more I realized the real problem wasn't optimization. It was trust infrastructure. Merchants can optimize claims all they want; if there's no verification layer, agents still can't trust them. That realization drove the pivot to CITAQ — from "help sellers rank" to "build the rails that make AI-mediated commerce trustworthy." We're at early access right now. If you're building AI shopping agents, working on e-commerce data infrastructure, or thinking seriously about how agentic commerce actually needs to work — I'd love to hear from you. Drop a comment or hit me on X: @omordiawisdom.

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2d ago

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2d ago

How are you currently handling product data trust for AI agents?

One thing I kept running into while building CITAQ: most teams working on AI shopping agents are either scraping retailer pages, trusting merchant-provided feeds, or pulling from aggregators like Google Shopping.

All three have the same problem the data is unverified at the claim level. An agent retrieving "waterproof to 50m" has no way to know if that's a tested specification or a line someone typed into a product description.

Curious how others in the space are thinking about this. Are you ignoring it for now and shipping anyway? Building internal verification logic? Relying on return rates as a signal of bad data?

CITAQ is our answer to this but I'm genuinely interested in how the problem looks from where you're building.

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