Meta AI Checks Two Indexes Before It Recommends a Business. Are You in Both?

The timing is not an accident. Meta's Muse agent passed 5 million US downloads in its first 22 days, faster than ChatGPT managed, and on October 6 TIME published an investigation into the instruction files the agent writes about the people who use it. That story is about memory and privacy. The marketing story sits in an older and less reported document: the captured prompt for Meta AI, the assistant built into Meta's apps, which spells out where the assistant looks before it answers a buying question.

Sources: Crypto Briefing, October 2, 2026; captured Meta AI system prompt, Muse Spark 1.0 and 1.1 versions, community archive on GitHub. Full table in the statistics section.

Why this is surfacing now

Meta launched Muse, a personal AI agent that acts on a person's behalf across email, shopping and the web, on September 8, 2026, in the US. It was the No. 2 app on the US App Store within two days and No. 1 soon after, and it crossed 5 million US downloads on day 22. By Crypto Briefing's count, ChatGPT took 56 days to reach that figure, Grok 103 and Claude 492.

Then researchers noticed that Muse would show its own instruction files when asked through the normal chat interface. TIME's October 6 investigation worked through those files, which describe the agent keeping a running record of the user's life. Meta's position is that the files are meant to be seen. David Singleton of Meta Superintelligence Labs told TIME: "We want you to be able to see the markdown files Muse writes while it thinks."

The document that matters for marketers is older. Meta AI, the assistant at meta.ai and inside WhatsApp, Instagram, Facebook and Messenger, runs on the same Muse Spark model family, and its system prompt has been captured and archived twice: an earlier copy from the 1.0 model, which describes several of the tools in detail, and the copy in the asgeirtj archive on GitHub that dates itself to July 12, 2026 and identifies the model as Muse Spark 1.1. It is that prompt, not Muse's memory files, that explains how a recommendation gets built. The SEO Stuff newsletter connected the two stories this week. This article checks the claims against both archived captures and sets out what to do about them.

Two indexes, one answer

The captured prompt gives Meta AI two search tools and a rule for each. The first, browser.search, is for "open web facts, current events, verifiable public info." The second, meta_1p.content_search, is for "first-party Meta content (Instagram, Facebook, Threads)." The prompt then lists the kinds of questions that should trigger the social tool, and the list reads like a directory of small businesses and consumer brands: "things to do (restaurants/cafes/bars/shops/gyms/salons), fashion/beauty/design," "product recommendations/shopping advice," "lifestyle tips/how-to," and "public opinion/social reactions."

What each search tool is told to handle, from the Muse Spark 1.1 capture

Both tools can run on one question. The shopping instructions are explicit: "Call content_search and web_search in parallel when both triggered." Take "best gym in Austin for beginners." The web tool is told to handle "local businesses/restaurants/near me" and the social tool is told to handle gyms, so a gym in that market is being looked up in two places at the same time.

Two properties of the social index decide how much of your existing content counts. In the 1.0 capture, the tool describes itself as "built from content understanding (captions, visual analysis, transcripts)," so it matches on what a post means, including what is said aloud in a video, and not only on the caption's keywords. And in both captures its data starts on January 1, 2025. Anything older is invisible to it, however well it performed at the time.

How the social index orders posts

The content search tool has a ranking setting with three values. The 1.1 capture lists the three and names the default. The 1.0 capture spells out when each one applies.

The three ranking modes of Meta AI's content search

Read this narrowly. Likes are not a universal ranking factor in Meta AI. Relevance is the default, and engagement is one setting inside one tool. But the questions the capture assigns to engagement mode, reviews, comparisons, "best X" and recommendations, are the ones closest to a purchase. For those, the posts the assistant sees first are the ones people responded to.

"Buyable" is a narrower category than "mentioned"

The shopping instructions draw a line between what the assistant can sell and what merely informs the answer. In the capture's words: "Only products returned by meta_catalog_search and facebook_marketplace_search are shoppable, recommend only those. Web/social are research/social proof, not buyable."

Four more rules follow from the same section.

• Tags are a bridge. "When content_search returns tagged products/brands, search catalog for them." A customer's post that tags your product sends the assistant to look you up.
• Social proof always arrives with a shelf. "If content_search returned, always show catalog too."
• First-hand beats promotional. The tool is told to "prioritize first-person experience over promotional," which favors customer and creator posts over ad copy.
• Services and software stay off the shelf. "Catalog doesn't cover vehicles, auto parts, prepared food, real estate, services, software subscriptions." For those, the instruction is to skip the catalog and rely on web search.

The voice guidance is "recommend honestly, not like a salesperson," and the web tool is called before the catalog "to build opinion." In practice the assistant forms a view from reviews and expert pages, checks what people are saying in posts, and then shows what it can sell.

Meta's public statements line up with this. When it introduced Muse Spark on April 8, 2026, Meta said the model would "unlock new features that cite recommendations and content people share across Instagram, Facebook, and Threads," and that its shopping mode "draws from the styling inspiration and brand storytelling already happening across our apps, surfacing ideas from the creators and communities people already follow."

What is confirmed and what is not

A captured prompt is a reading of what a system can do, not a measurement of what it does. The table separates the claims in this article by where they come from.

Each claim, its source and its status

Three limits apply to everything above. Meta has not confirmed the captures, and the text already changed between the 1.0 and 1.1 versions. The instructions describe what the assistant can retrieve, not how it weighs sources when it writes the answer. And the Muse agent that made headlines this week publishes a different set of files in the same archive, none of which define a content search tool, so whether Muse recommends businesses the way Meta AI does is an open question.

What it means for your type of business

The two indexes and the catalog gate land differently depending on what you sell.

Where each type of business gets found inside Meta AI

If you sell physical products

The catalog is the gate. Get the products into Meta's catalog, keep the feed accurate, and tag them in your own posts. Then put the real effort into earning first-person posts from customers and creators, because those are what the engagement mode ranks and the tag lookup rewards. A tagged, first-person post about a catalogued product satisfies every rule in the shopping section at once. Our study of e-commerce brands in AI answers shows how far citation rank can diverge from organic traffic rank in this category.

If you sell software or services

Your website carries the weight, because the catalog cannot represent you. The assistant is told to rely on web search for services and subscriptions, which means your service pages, use-case pages, comparison pages and answers to buyer questions have to be retrievable and extractable. That is what the 25-point GEO audit checks. Social posts still count on opinion and how-to questions, so a founder explaining a problem on camera is not wasted effort, but the web is the main route in.

If you run a local business

You are the category the social search was built for, and local queries trigger the web tool as well, so both indexes are reading you. Post on a schedule and say plainly what you do, who it is for and where you are, in the caption and out loud, because transcripts are indexed. Ask customers to post and tag you. Nothing before 2025 counts, so a strong 2023 does not help. The pattern of pairing search work with daily platform posting is the one that took a 30-agent Austin brokerage from absent to cited in eight out of ten relevant AI queries.

Six things to do this quarter

1. Audit the pages that answer buyer questions. Run the AI Visibility Checker on each service or product page. The web index is the one every assistant reads, and it is where software and services companies live or die.
2. Write captions and scripts in the buyer's words. The social index matches on meaning. "We fit same-day crowns for busy professionals in Leeds" is searchable. "New week, new smiles" is not.
3. Keep posting. The cutoff is January 1, 2025. A quiet year means an empty index, whatever your archive looks like.
4. Ask for the tag. A customer post that tags your product or brand triggers a catalog lookup. Make tagging part of the unboxing, the follow-up email and the in-store sign.
5. If you sell goods, be in the catalog. Nothing outside it is presented as buyable, and an accurate feed is what the tag lookup lands on.
6. Ask the assistant yourself. Open Meta AI, ask the question your buyer would ask, note who comes back and why, and repeat it monthly. The captures tell you the inputs. Only the answers tell you the outcome.

How we do it

LaCleo runs agentic SEO and GEO as a human-led managed service, and Meta's two indexes map onto two halves of the program.

1. Prompt tracking. 100 or more buyer prompts tracked across the major LLMs and Google's AI surfaces, with Meta AI added to the probe set for brands whose buyers live on Instagram and Facebook. Licenses are included.
2. Audit first. The 25-point GEO audit and a technical audit run in week one on the pages that answer buyer questions. This is the web index, and for services and software it is most of the job.
3. Entity and citation work. The web tool is told to build an opinion from reviews and expert pages before it looks at the catalog, so third-party corroboration is worked as a deliverable: the domains and communities that cite your competitors become a target list, with outreach reviewed before it goes.
4. Captions and scripts written to be searched. Our social media agents draft Instagram and Facebook posts in the words buyers use, with the what, who and where stated in the caption and in the spoken script. A person approves every post before it publishes.
5. Earned first-person mentions. Our reputation agents run review generation campaigns and collect testimonials, and the request to post and tag is built into the same moment.
6. Catalog and schema in agreement. For product brands, the implementation pack includes the product schema and data fixes that keep the catalog feed and the product pages telling the same story, delivered as files your developers paste in.
7. Monthly report and re-check. Rankings, citations, share of voice and referral sessions read together, with the next pack attached, then the prompts are re-run.

Statistics with sources

Figures used in this article, with the source and the caveat that applies

Frequently asked questions

Does Meta AI search Instagram and Facebook posts when it recommends a business?

According to its captured system prompt, yes. Meta AI has a web search tool and a separate content search tool over public Instagram, Facebook and Threads posts. The social tool is triggered for local spots such as restaurants, cafes, bars, shops, gyms and salons, for fashion, beauty and design, for product recommendations and shopping advice, and for opinion and how-to questions. When a question fits both tools, the assistant is told to run both at once.

What is the difference between Meta AI and Muse?

Meta AI is the assistant at meta.ai and inside Meta's apps. Muse is the personal AI agent app Meta launched on September 8, 2026. Both run on the Muse Spark model family. The captured search and shopping instructions discussed here are Meta AI's. The archived Muse agent files cover identity, memory and tools, and do not define a content search tool, so it is not confirmed that Muse recommends businesses the same way.

Do likes and followers affect whether Meta AI recommends my business?

Partly. The content search tool has three ranking modes: informational, which sorts by relevance and is the default; engagement, which ranks by likes, shares and author follows; and recency. The earlier capture assigns engagement mode to how-to, advice, reviews, comparisons, best-of and recommendation questions. So engagement matters for the questions closest to a purchase, but it is one setting inside one tool, not a universal ranking factor.

Does Meta AI search old Instagram and Facebook posts?

No. In both archived versions of the prompt, the content search tool describes its data as starting on January 1, 2025. Posts older than that are not retrievable by the tool, however well they performed when they were published.

Can Meta AI sell my product or service directly in the answer?

Only items returned by Meta's product catalog search or Facebook Marketplace search are presented as buyable. Web pages and social posts are treated as research and social proof. The instructions say the catalog does not cover vehicles, auto parts, prepared food, real estate, services or software subscriptions, and that the assistant should rely on web search for those.

How is GEO for Meta AI different from GEO for ChatGPT or Perplexity?

The website work is the same: retrievable, extractable pages with third-party corroboration, as set out in how AI search engines choose what to cite. Meta adds a second index built from its own apps' posts, with a January 2025 cutoff and an engagement mode for buying questions, plus a catalog layer that decides what is buyable. A GEO program aimed at Meta therefore has to cover posting, tagging and catalog data as well as the site.

How reliable are captured system prompts as a source?

They are community captures, not official documentation. Meta has not confirmed them, and the text already differs between the two archived versions: the later one keeps the ranking modes and the date cutoff but drops the sentence about captions, visual analysis and transcripts. They describe what the assistant can retrieve, not how it weighs sources in a final answer. Treat them as a map of inputs, and check the outputs by asking the assistant directly.

Working with LaCleo

LaCleo runs agentic SEO and GEO as a human-led agentic managed service, from $750 a month. A program includes 100 or more prompts tracked across the major LLMs with licenses included; the Ahrefs and Semrush keyword universes plus LaCleo's own agentic probe for first-hand answer data; the 25-point GEO audit and a technical audit in week one; Reddit and subreddit citation tracking; full competitor comparison, gap analysis and a strategy per gap; on-page LLM optimization; and deployable schema, metadata, page changes and blogs delivered as an implementation pack that your developers copy and paste. For the second index, our social media and reputation management agents handle the posting, the review generation and the tagging routine, with a person approving every post and every response. A named person reviews every deliverable and is on the monthly call. There is nothing to install and no dashboard to interpret.

Connect to know more Book a call Get a free AI audit

Sources

TIME, "Meta's Muse AI Agent Is Building a Dossier On You", October 6, 2026. TechCrunch, "Meta's AI agent Muse is now the No. 2 app in the US", September 10, 2026. Crypto Briefing, "Meta's Muse hits 5 million downloads in 22 days", October 2, 2026. Meta Newsroom, "Introducing Muse Spark", April 8, 2026. Captured Meta AI system prompts, Muse Spark 1.0 and Muse Spark 1.1, community archive on GitHub; the Muse agent's files are in the same archive. SEO Stuff newsletter, "How To Get Meta's Muse To Recommend You", October 7, 2026. Quotations from the captured prompts were checked against the archived files on October 7, 2026.