AI Assistants Read PeerPush 5 Million Times a Month. Here Is What They Look For in a Product.

Guest post by the team behind PeerPush, a product discovery platform that describes products in structured data for people and AI.

PeerPush is a product discovery platform built for two audiences at once: people and AI. Every product on it is described in structured, machine-readable data: use cases, audiences, pricing, platforms, alternatives. That design choice produced an unusual dataset. In one recent 30-day window, AI crawlers and agents made about 5 million requests to PeerPush, roughly 12% of all its tracked events. ChatGPT, Perplexity, Claude, and Meta all read it continuously, and AI agents query its structured API through MCP (Model Context Protocol) thousands of times per month.

That volume shows, request by request, what AI systems actually look for in a product page. This article turns those observations into a checklist any founder can apply. It matters because industry research estimates that around a third of product discovery queries now start in an AI assistant, not in a search box.

Key facts

• AI crawlers and agents made about 5 million requests to PeerPush in 30 days. Structured product pages are what they came for.
• AI assistants answer from three sources: training data, search indexes, and live page fetches. A product needs to be present in all three.
• Assistants rarely cite a company's own marketing pages. A 2026 study of 150,000 AI citations found that Reddit and Wikipedia together carry about two thirds of all citations.
• Unknown products do not get silence. They get hallucinated descriptions.
• Structured, consistent product data is what AI systems extract reliably. Prose is what they guess from.

The problem: assistants get small products wrong

The failure mode is easy to reproduce. In one recent test, six frontier AI models received the same "What is [product]?" question about a niche software product. One model answered correctly, and only because it searched the live web. Two models admitted they did not know. Three models invented a confident, wrong answer: one described a content syndication tool, another a peer-to-peer payment service. The product was neither. A user who asks an assistant about a product receives that wrong answer as fact.

An AI assistant is not a search engine. It composes an answer from what it has. For a small product, it usually has almost nothing: a few scattered mentions with different wording, no reference page, no structured facts. So it fills the gap by pattern matching on the name. The model guesses.

The fix is not one great blog post. The fix is many independent sources that agree on the same structured facts about the product. That is the principle PeerPush is built on, and it is the principle behind every step below.

Step 1: write one canonical description

Write one sentence that says what your product is, who it is for, and what category it is in. Then reuse that sentence everywhere: your homepage, your directory listings, your GitHub profile, your social bios, your footer.

This sounds trivial. It is the highest-leverage step. Models learn entities from repeated, consistent statements. Ten sites that describe a product the same way teach a model one fact. Ten sites with ten different descriptions teach it nothing.

Put the sentence in the first paragraph of your homepage, in plain HTML. Assistants extract answers from the top of the page, and many do not run JavaScript.

Step 2: publish structured data, not only prose

AI systems extract facts most reliably from structure: schema.org markup, comparison tables, FAQ blocks, and labeled fields such as pricing, platforms, and use cases.

On your own site:

• Add schema.org Product and Organization markup.
• Add an FAQ section that answers the questions people actually ask an assistant: "What is X?", "How much does X cost?", "What is X an alternative to?".
• Show pricing in plain text. Hidden pricing is a blank field in every AI answer about you.

Off your site, list the product in places that publish structured data about it. A directory listing in 2026 is not a traffic play; it is a machine-readable reference record. This is exactly what PeerPush provides: every listing carries controlled-vocabulary use cases, target audiences, pricing type, platforms, and alternatives, in clean HTML with schema markup, which is why AI systems read it at the scale described above. Whichever directories you choose, check what the listing page looks like to a bot, and fill every field you are given. Empty fields are invisible to AI assistants.

Step 3: exist where assistants actually look

The citation study above is blunt: assistants cite community and reference sources, not vendor sites. Act on that.

• Reddit. It is the single largest citation source for AI search engines. Participate honestly in the threads where your category is discussed. Answer questions, disclose who you are, and mention your product only where it genuinely fits. One factual mention in a relevant thread outweighs any amount of copy on your own site.
• Comparison and "alternatives" content. When someone asks an assistant "best tool for X", the pages it reads are third-party comparison guides. Get the product included in existing guides in its category. Ask the authors; most maintain their lists.
• Wikidata. If the product has any public footprint, create or maintain a Wikidata entity with correct facts. It is a machine-readable reference that several AI systems consume.
• GitHub. If you have any public repositories, make the descriptions carry the canonical sentence. Code hosts are heavily crawled for training data.

Step 4: be readable by agents, not only crawlers

A growing share of AI traffic is not background crawling. It is an agent fetching a page right now because a person just asked a question. On PeerPush, this live-fetch traffic grew through all of 2026, and a new channel appeared next to it: agents that skip HTML entirely and query the platform's structured API through MCP. Structured answers beat scraped pages, and agents already behave accordingly.

Practical consequences for your own site:

• The site must return full content fast, without JavaScript, without login walls on basic facts.
• Do not block AI user agents in robots.txt unless you truly mean to. Blocking the fetcher blocks the answer.
• Do not spend effort on llms.txt. Industry studies and PeerPush's logs agree: crawlers largely ignore it. Structure and clean HTML move behavior; llms.txt does not.

Step 5: measure it like a channel

Do not guess. Once a month, ask the major assistants three questions and record the answers:

1. "What is [your product]?" Check for accuracy, not just presence.
2. "Best tools for [your use case]?" Check if you appear, and which sources are cited.
3. "[Competitor] alternatives?" Check if you are in the list.

Track corrections over time the way you track rankings. When an assistant cites a source page for your category, that page is your next outreach target.

Conclusion

The 5 million monthly AI requests hitting PeerPush are not an anomaly. They are what the demand side of product discovery looks like now: machines reading structured product data so they can answer a human's question. The products that get described correctly will be the ones that made themselves easy to read: one canonical description repeated everywhere, structured data on and off the site, honest presence in the community sources assistants cite. Start with the canonical sentence today, then give it a structured home.

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Data in this article comes from PeerPush, the product discovery platform where products are described in structured, machine-readable data for people and AI.