Central builds a verified product record, then exposes it as Schema.org Product, FAQPage, AggregateRating, JSON-LD, x-central provenance metadata, and AI-readable feeds — so ChatGPT Shopping, Perplexity, Google AI Mode, and Bing Generative Search can answer with grounded facts instead of guesses.
Without a verified record, AI shopping engines either skip your product, hallucinate a spec, or quote a retailer that drifted from the brand record. With one, they answer the same way — and they cite your record.
Central runs the same buyer question against ChatGPT Shopping, Perplexity, Google AI Mode, and Bing Generative Search. Every answer gets graded against the verified record and the Schema.org payload behind it.
AI drafts the content. Verification decides what ships. Three visible steps from a product page to a Schema.org-grounded record that AI shopping surfaces can quote.
Brand spec sheets, retailer pages, marketplace listings, regulatory feeds. Central scores agreement, holds unsupported claims, and never lets unverified facts enter copy, FAQ, or schema.
Channel Studio shapes the verified record into structured payloads per surface: Schema.org markup for rich results, FAQPage for buyer Q&A, AggregateRating where reviews allow it, and x-central provenance metadata for agents.
Schema.org for Google rich results. FAQPage for buyer questions. x-central for agents. llms.txt and agent feeds for ChatGPT Shopping, Perplexity, and Bing Generative — so every surface cites the same verified record.
Schema.org cascade, agent answer matrix, FAQPage from verified facts, x-central provenance, meta optimization, Smart Negatives, rich result coverage, and image alt + IPTC — all from one record your team can actually audit.
"I don't have specific info about the TSF01's slot width. Most 2-slot toasters fit a regular slice but not a bagel."
"Yes - the Smeg TSF01 has a 33 mm slot that fits a standard bagel. It's wider than the typical 28 mm 2-slot toaster."
"The TSF01 has 30 mm slots. Some retailers describe it as compatible with 'bagels XL'."
"33 mm slot - cited by brand spec and 12 agreeing retailers. Fits a standard bagel. Power: 950 W."
The same Central product system that verifies enrichment and publishes channels is what makes your product visible and citable across AI shopping surfaces.
Scans 10-20 sources, scores agreement, and holds unsupported claims out of page copy, FAQ, and JSON-LD. Each fact carries a source list and a confidence.
Turns the verified record into Schema.org Product, FAQPage, AggregateRating, JSON-LD, x-central provenance metadata, llms.txt agent feeds, and custom AI-readable payloads.
FAQs, Smart Negatives, buyer answers - drop one script tag and the widget renders only from the verified record. AI agents reading the page see grounded copy and structured Q&A.
The same Smeg TSF01 - hallucinated by AI agents on the left, cited by them on the right. The difference: a verified record, Schema.org markup, and x-central provenance shipping together.
Central re-checks ChatGPT, Perplexity, Google AI Mode, Bing Generative, Google rich results, and the llms.txt agent feed - and routes drift, missing fields, or hallucinated answers into a review queue.
uploadDate missing on tsf01-product-video - rich result blocked
14 min ago
Schema.org markup makes your product facts machine-readable. AI shopping engines and agent crawlers index those structured payloads alongside the page copy. Central generates Product, FAQPage, AggregateRating, and other Schema types from one verified record, plus an x-central provenance block so agents can prefer cited claims over guessed ones.
No vendor can guarantee placement in any AI engine. What Central does guarantee is that when an agent does find your record, it sees verified facts with sources - so the agent can quote, cite, and recommend with confidence. Pages without grounded structured data and without provenance metadata routinely get skipped or hallucinated.
A Central-defined metadata block embedded alongside Schema.org payloads. It carries the source list, agreement count, confidence, and verification timestamp for each claim. Agents that read the payload can prefer multi-sourced claims over single-retailer guesses. It's additive - your existing JSON-LD stays valid.
Yes. Central publishes the verified record as a llms.txt-style agent feed and custom JSON / JSONL payloads, so AI shopping and answer engines can pull a clean, sourced, confidence-scored representation of your catalog without scraping HTML.
Both surface structured data and rich results from the same Schema.org markup Central generates. Channel Studio shapes the payload to each surface's spec (Bing's JsonLD recommendations, Google's rich result types), and the monitoring dashboard re-checks each surface and routes failures.
No. Smart Negatives hold unsupported, conflicting, or below-floor claims out of page copy, FAQ, Schema.org, and the agent feed until a second source agrees or a merchant approves. Confidence floor defaults to 0.85 with brand-owned values at 1.00.
Indirectly. Central produces strong structured payloads (Product, brand, gtin13, aggregateRating, Offer) that are the same signals Knowledge Graph pipelines and product-knowledge indexers depend on. Cleaner upstream data, stronger downstream signals.
No. Central layers structured data, FAQPage markup, and x-central metadata onto your existing pages. Canonical tags, titles, and URLs stay yours. The widget and the JSON-LD payload are additive; the rich result test and monitoring dashboard tell you immediately if a type stops being eligible.