How to Get Your Products Discovered in AI Search & Agentic Commerce (ChatGPT, Perplexity, Copilot & More)

AI search and agentic commerce are changing how people discover and buy products. Instead of typing “best running shoes size 42” into a search bar, shoppers now ask ChatGPT, Perplexity, or Copilot things like “I need cushioned running shoes for flat feet, under €120 – any suggestions?”. The AI doesn’t return a list of blue links. It returns a handful of products – and in many cases, enables checkout directly inside the conversation.

For brands and retailers, this creates a new visibility challenge: how do you become the product that the AI recommends? Recent research from Yext, based on 6.8M AI citations across ChatGPT, Gemini and Perplexity, shows that 86% of sources used by AI come from brand-managed assets such as websites, listings and reviews. In other words: your own product data, feeds and content are now the primary levers to appear in AI answers.

This guide explains how AI assistants “see” your catalog, what concrete actions to take on your product data and feeds, and how Lengow helps you become visible in agentic commerce – including ChatGPT Shopping via the OpenAI Product Feed.

From SEO to AI search & agentic commerce

Traditional SEO was about ranking in a list of links. AI search and agentic commerce are different: they produce direct answers, often including only a few products, and can drive the shopper all the way to payment without leaving the assistant.

• Conversational intent: queries look like questions or mini-briefs (“eco-friendly sofa for small living room, kids-friendly fabric”). AI breaks them into needs, constraints and preferences – and then looks for products that match.
• Fewer but richer results: instead of 10 blue links, the user may see 3–6 product cards with images, specs, reviews and badges like “Popular” or “Best value”.
• Agentic journeys: the assistant can refine the search (“show only vegan leather”), check stock, compare options and, with the Agentic Commerce Protocol (ACP), complete checkout in-chat via integrations such as Stripe.
• New discipline, GEO (Generative Engine Optimisation): instead of keyword stuffing, you optimise for how AI models understand entities, attributes, and trust signals across sources like your site, feeds, marketplaces and reviews.

To win in this environment, you need AI-ready product data and a distribution layer that gets this data into every channel and AI surface that matters – from marketplaces to ChatGPT Shopping. This is exactly what Lengow’s product feed and marketplace management suite is built for.

How AI assistants “see” your products

Under the hood, AI search engines and assistants draw on three main layers of data. Understanding these will help you decide where to invest first.

1) Direct product feeds into AI platforms (ChatGPT Shopping & Agentic Commerce)

OpenAI’s Product Feed lets merchants send a structured catalog directly to ChatGPT. The feed is the single source of truth for titles, prices, stock, logistics, images and policies, powering both search and Instant Checkout.

• Push model, not crawling: merchants push CSV/TSV/XML/JSON files over encrypted HTTPS to an allow-listed endpoint; updates can be sent as often as every 15 minutes to keep stock and price fresh.
• Required fields: each product needs a stable id, title, plain-text description, link, image_link, price, availability, inventory_quantity, brand, condition, and flags like enable_search and enable_checkout, plus basic policy URLs.
• Agentic Commerce Protocol (ACP): ACP defines how AI agents, merchants and PSPs (e.g. Stripe) exchange data to complete secure payments and fulfilment.

Lengow supports the OpenAI Product Feed specification so you can connect your catalog to ChatGPT Shopping with minimal technical work, using the same feed foundation you already use for marketplaces and advertising.

2) Marketplaces & retailer ecosystems

Amazon, Zalando, Miravia, Decathlon and many others are investing in their own AI search and assistant layers. Those systems are powered by the product feeds you already send them and will increasingly surface your offers in conversational journeys on their sites and apps.

If your content is incomplete, inconsistent or poorly structured on marketplaces today, you won’t just lose classic search ranking – you’ll also miss out on AI-driven placements such as “assistant” recommendations or personalised collections.

With NetMarkets and Lengow’s product feed management, you can keep one clean, central feed and adapt attributes, titles and categorisation for each marketplace automatically.

3) Open web content, listings and reviews

Even when a direct product feed exists, AI assistants still rely heavily on public web content and third-party citations to validate and enrich their answers. As mentioned in the intro, Yext’s 2025 study shows that 86% of AI citations come from websites, listings and reviews that brands can control or influence.

For commerce, that means:

• Your product detail pages (PDPs) and category pages.
• Local listings and business profiles on Google, Maps, retail store finders, etc.
• Structured reviews and Q&A content on your site and on marketplaces.
• Editorial content: buying guides, comparisons, FAQs – all of which can be quoted in AI answers.

In short: the product page is still the foundation. The richer and more structured your content, the more likely AI is to understand, select and cite your products.

AI-ready product data: the practical checklist

Think of your product catalog as something you are preparing for two audiences at once: humans and AI models. Here is a checklist you can apply across your PIM, website and feeds.

1) Standardise and complete your catalog attributes

• Make sure every product has complete core fields: title, brand, category, GTIN/UPC/MPN, price, availability, main image, and key technical attributes (size, material, colour, capacity, voltage, etc.).
• Use consistent, machine-friendly values (e.g. “100% cotton”, “EU 42”, “IPX4”) instead of vague marketing language like “super comfortable” as the only attribute.
• Centralise this in a product feed platform so you can reuse it across marketplaces, ads and AI channels. Lengow’s feed management and optimisation features are designed for exactly this.

2) Write for real customer intent (not just keywords)

AI matches your content to questions, not to short keywords. Your copy should sound like how people actually ask for your product.

• Include use cases and constraints: “ideal for small apartments”, “designed for oily skin”, “suitable for induction hobs”, “vegan leather, PVC-free”.
• Translate technical specs into benefits: “3000 lumens” becomes “bright enough for daylight home office work”.
• Use short, scannable bullet points on PDPs and in feed descriptions; AI models handle bullet structures extremely well.

3) Add machine-readable structure (schema.org & feeds)

• Implement schema.org Product, Offer, Review and AggregateRating markup (preferably JSON-LD) on product and category pages so AI crawlers like OAI-SearchBot, GPTBot and others can instantly parse your catalog.
• Keep your Google Shopping / Performance Max feed clean and aligned; many GEO best practices mirror Google’s shopping quality rules.
• Prepare a ChatGPT-ready product feed using OpenAI’s Product Feed spec. Lengow provides a dedicated guide and mapping for this.

4) Optimise images and rich media

• Use high-resolution, clear images (minimum 1,000 px on the longest side) with clean backgrounds; avoid text-heavy overlays that can confuse both users and AI cropping.
• Add additional angles, zooms and, where relevant, video/3D models; ChatGPT’s feed spec supports extra media fields for richer product cards.
• Fill in alt text and captions in descriptive, non-spammy language (“woman wearing black waterproof parka in rain”) for accessibility and semantic clarity.

5) Leverage reviews, UGC and social proof

• Encourage detailed, authentic reviews that mention use cases (“I live in a small flat…”, “great for sensitive skin”). AI uses these to qualify recommendations and may quote them.
• Expose ratings and review counts in structured data and feeds (e.g. average_rating, review_count fields in your ChatGPT and marketplace feeds).
• Monitor and respond to reviews; active, up-to-date reviews signal that your brand is still relevant and trustworthy.

6) Keep data fresh & consistent everywhere

Nothing erodes AI (and user) trust faster than showing products that are out of stock, with outdated prices or broken links.

• Synchronise availability, price and logistics data across your website, marketplaces, ads and ChatGPT feed via an automated feed management layer.
• Use feed rules to automatically pause products when inventory is low or margin thresholds are not met.
• Follow OpenAI’s recommendation to refresh the Product Feed up to every 15 minutes for near real-time accuracy.

7) Don’t accidentally block AI crawlers

• Check your robots.txt and security tools to ensure GPTBot, OAI-SearchBot and other approved AI crawlers can access your product and content pages where appropriate.
• If you use a CDN or WAF, ensure it doesn’t aggressively rate-limit or block these user agents unless there’s a specific risk.
• For sensitive sections (e.g. customer accounts), you can still block AI crawlers while keeping product and content pages open.

8) Scale all this with automation

Doing all of this manually for thousands of SKUs is impossible. Lengow lets you:

• Ingest data from your shop, PIM or ERP and normalise attributes once, then reuse them across dozens of channels.
• Use rules and templates to generate better titles and descriptions at scale, following GEO best practices and marketplace requirements.
• Continuously monitor feed quality and channel-specific errors, so you know where content is blocking visibility.

ChatGPT Shopping & Agentic Commerce: a concrete playbook

If you want to appear in ChatGPT Shopping and Agentic Commerce experiences, here is a pragmatic sequence you can follow.

Step 1 – Check eligibility and apply

• Review OpenAI’s commerce documentation and Product Feed spec to understand requirements, security expectations and supported regions.
• Submit your interest to OpenAI via their merchant or product discovery forms; most guides recommend doing this even while you prepare your feed.
• Read Lengow’s own summary on the ChatGPT Product Feed to align your internal teams on roles (IT, e-commerce, legal).

Step 2 – Build your canonical product feed

• Use Lengow as your single canonical catalog with full attributes and categorisation, enriched beyond what you currently send to marketplaces.
• Fix structural issues first: duplicate IDs, missing GTINs, inconsistent categories, messy variants.
• Align internal naming and taxonomy so that mappings to OpenAI and marketplaces stay robust over time.

Step 3 – Map to OpenAI’s Product Feed spec

• Map your catalog fields to the required attributes (id, title, description, price, availability, etc.) and add as many recommended ones as possible (logistics, variants, extra images, 3D, reviews).
• Set enable_search and enable_checkout flags thoughtfully: you may start with search-only, then roll out checkout for selected SKUs or markets once ACP endpoints are ready.
• Include clear policies (returns, privacy, T&Cs) at merchant level; these are mandatory for trust and compliance.

Step 4 – Optimise for conversational relevance

• Enrich your titles and descriptions with the language people actually use in ChatGPT, using insights from your support tickets, search logs and marketplace queries.
• Highlight benefits and constraints directly in product attributes (e.g. “max_weight_capacity=120kg”, “noise_level=40dB”) so AI can filter and justify its recommendations.
• Ensure your feed mirrors the same structured information you expose via schema.org on your website – consistency is a strong trust signal.

Step 5 – Validate, test and fix

• Use Lengow’s feed validation and error reporting to catch missing fields, invalid prices, broken URLs or unrecognised categories before sending to OpenAI.
• Submit a sample feed or sandbox run if requested by OpenAI and iterate until you get a clean import.
• Once live, test real prompts in ChatGPT (and other AI engines) to see how your products appear and which sources are cited.

Step 6 – Automate updates & protect margins

• Schedule frequent exports (e.g. every 15 minutes for price/stock) from Lengow to keep ChatGPT aligned with your real inventory.
• Use rules to exclude low-margin or restricted products from AI channels, or to push specific hero SKUs in each category.
• Monitor the impact on marketplace and ads performance – AI discovery often lifts brand searches elsewhere.

Step 7 – Track your “answer share” in AI search

The equivalent of “share of voice” in AI is how often you appear in AI answers or product carousels compared to your competitors. Specialist tools such as Rank.bot, Profound and others already track product visibility across ChatGPT Shopping, Perplexity and Google AI Overviews.

Use these insights to prioritise where to deepen your data work: certain categories, brands, price points or geographies will show much more AI upside than others.

Winning visibility in Perplexity, Copilot, Gemini & emerging AI engines

Not every AI engine offers a direct merchant feed yet. Many, including Perplexity and some Google and Microsoft experiences, still rely heavily on crawled web content and citations.

• Audit your presence in AI answers: run the same high-intent questions in ChatGPT, Perplexity, Gemini and Copilot. Note which sites are cited (your site, marketplaces, media, review platforms) and which brands appear most often.
• Upgrade product pages and guides: create buying guides, comparison articles and FAQ sections that directly answer “which X should I choose for Y?” questions in your main categories.
• Strengthen listings & local data: maintain complete, structured business and store listings; Yext’s research shows listings are one of the biggest drivers of AI citations.
• Focus on authority sources: invest in reviews from recognised publishers, marketplaces and vertical platforms that AI tends to quote.
• Measure & iterate: track how often your brand is mentioned and cited over time with dedicated AI visibility tools or your agency’s reporting stack.

Operationalising GEO with Lengow

You don’t need an internal AI engineering team to show up in AI search. You need clean product data, smart feeds and automation. Lengow provides the operational layer that connects your catalog to agentic commerce:

• Central product feed management: ingest product data once and distribute it to marketplaces, ad platforms and now ChatGPT Shopping, with per-channel mappings and rules.
• Feed optimisation at scale: use NetAmplify and Lengow rules to enrich titles and descriptions, adjust attributes and create channel-specific versions without manual copy-pasting.
• Marketplace excellence: ensure marketplace listings meet the strictest standards for data quality, imagery and categorisation, which directly impacts their own AI-powered search and recommendations.
• Price & assortment intelligence: combine NetRivals price monitoring with your feeds to make margin-aware decisions on which products to push most aggressively in AI channels.

AI search is moving quickly, but the underlying rule is simple: the brands that invest in structured, accurate, use-case-driven product data today will control tomorrow’s AI shelves. With Lengow as your feed and marketplace hub, you can adapt once and benefit wherever buyers choose to search – search engine, marketplace, social platform, or AI assistant.