Sourcepull

Checking your AI search visibility isn't like checking your Google rankings. There's no dashboard, no position tracker, no weekly report. AI models don't publish a list of who they're recommending. You have to go look.

The good news: a useful self-audit takes less than an hour and will tell you exactly where you stand. Here's how to do it.

Start with test queries across multiple models

Open ChatGPT, Perplexity, Claude, and Gemini. You're going to run the same queries across all four, because each model has different retrieval behavior and citation tendencies. A business can be invisible to ChatGPT and well-cited by Perplexity, or vice versa.

For each model, run three query types:

Category + city queries. "Best [your service] in [your city]." "Top-rated [your service] near [your city]." This is the most common format for how real customers search via AI. Write down whether you appear, whether a competitor appears, and how confidently the model answers.

Specific need queries. "Who does [specific service] in [your city]." For a plumber: "Who fixes burst pipes in [city]." For a lawyer: "Find a family law attorney in [city] who handles custody disputes." These narrower queries often surface businesses that the broad queries miss.

Entity recognition check. Type your business name directly. Does the model know what you do, where you are, and what makes you different? Or does it confuse you with another business, give no information, or say it doesn't have data on you?

Document every result. The goal isn't a score — it's a pattern. Are you consistently absent? Occasionally mentioned but not recommended? Named with confident details, or named with vague or wrong information?

Check whether AI crawlers can access your site

Before any model can recommend you, it needs to be able to read your site. Open your robots.txt file (yourdomain.com/robots.txt) and look for any rules that block the following bots: GPTBot, PerplexityBot, ClaudeBot, Googlebot-Extended, CCBot, and OAI-SearchBot.

If you see Disallow: / under any of these user-agents, that crawler is blocked. GPTBot is the most commonly blocked by accident — website templates, privacy plugins, and security configurations added it to blocklists when it launched. Check every crawler individually rather than scanning for a single name.

If you run a WordPress site, check your SEO plugin settings too. Some plugins expose per-bot crawl controls in their AI/robots section, separate from the robots.txt file. Both places can block visibility.

Look for your llms.txt file

Check whether your domain has an llms.txt file (yourdomain.com/llms.txt). This is a plain text file that gives AI models structured context about your business — what you do, who you serve, your key service pages, your location.

If the URL returns a 404, you don't have one. Most businesses don't. This is one of the easiest signals to add. A basic llms.txt gives models a clear, authoritative description of your business in plain language, and it requires no technical expertise to create.

If you do have one, read it. Does it accurately describe what you do now? Is the service list current? Does it reference the cities you serve? Many llms.txt files are set up once and never updated.

Audit your entity consistency

Entity consistency is how well AI models can construct a reliable picture of your business from multiple independent sources. Pull up these profiles and compare them carefully:

Your Google Business Profile name, address, phone, and service categories. Your website's contact page name, address, and phone. Your Yelp listing. Your LinkedIn company page if you have one. Your top two or three industry-specific directories.

For each field, check: does it match? Not approximately — exactly. "Acme Plumbing" vs "Acme Plumbing Co." vs "Acme Plumbing & Drains" are three different entities in a model's view. Inconsistent names fragment your signal.

Also check what services are listed. If your website lists 12 services but your GBP lists three, AI models may not know the other nine exist. The more sources that confirm a specific service in a specific city, the higher your confidence signal for that offering.

Review your schema markup

Go to Google's Rich Results Test (or any structured data validator) and enter your homepage URL. Check whether LocalBusiness schema is present. If it is, look at what's inside.

Three things to verify specifically: First, is the schema type specific (Dentist, LegalService, Plumber, Contractor) rather than the generic LocalBusiness? Specific types are stronger retrieval signals. Second, does areaServed list the actual cities and regions where you work? If it's blank or just your address city, you're missing coverage for every surrounding area. Third, does hasOfferCatalog or makesOffer describe your specific services? Generic schema that says "plumbing" without listing what kind of plumbing doesn't help a model match you to a specific query.

If you don't have schema at all, that's a clear gap. If the schema is present but thin, the fix is usually an afternoon of work.

Read your own reviews for signal content

Pull up your most recent 20 Google reviews and read them like an AI model would. Models don't just read star ratings — they extract language about what your business does, where it does it, and what kind of client it serves.

Ask yourself: would a model reading these reviews know what specific services you offer? Would it know the cities and neighborhoods where you work? Would it understand the type of customer you serve?

A plumber with 40 reviews that all say "great service, very professional" is giving an AI model no useful signal. A plumber whose reviews say "fixed our main water line break in Burlington in the middle of January" is giving the model a lot. The difference isn't star rating — it's specificity.

You can't write your clients' reviews for them, but you can ask satisfied customers to mention what they had done and where. That's not gaming anything — it's just clear communication that happens to build useful signal.

What to do with what you find

Most self-audits produce one of three patterns.

You're absent everywhere. Your crawlers are blocked, or your schema is missing, or your entity profile is so thin that models can't construct a confident picture of your business. Fix crawl access first, then schema, then entity consistency. These are the structural foundations everything else rests on.

You're mentioned but not cited. Models know you exist but don't recommend you confidently. This usually means your signal exists but is incomplete or inconsistent. Run the entity consistency and review checks carefully — fragmented data is almost always the cause.

You're cited by one model but not others. This is common and often means your real-time crawlability is strong (which helps Perplexity) but your training-data presence is thin (which matters more for ChatGPT and Claude). Content depth and inbound links from recognized sources become the priority here.

A manual audit like this surfaces the pattern. It won't give you a numeric score or tell you which specific gaps are costing you the most — that's where a structured audit tool earns its value.

If you want a faster starting point, run a Signal Check on your domain. It runs the technical checks, entity validation, schema analysis, and AI query testing automatically and returns a scored report with a prioritized fix list. The free version covers the fundamentals. It takes about three minutes and tells you what a manual audit would take an hour to find.

Either way — do the audit. Most of your competitors haven't.

The Role of Backlinks in AI Search Visibility

When someone who's done traditional SEO first hears about AI visibility, their first question is usually some version of: "Does any of my link-building still count?"

The honest answer: yes, but not the way you think. Backlinks matter for AI search visibility, but the mechanism is completely different from how they work in Google. If you're treating them the same way, you're probably investing in the wrong things.

How Google uses backlinks vs. how AI models do

Google's ranking algorithm treats backlinks as votes. The more links you have from authoritative sites, the more PageRank flows through your site, the better you rank. It's a popularity contest weighted by source quality.

AI models don't rank. They make inferences. When ChatGPT or Perplexity decides whether to recommend your business, it's asking a different question: does the web consistently and credibly describe this business as a real, legitimate option for this query? Backlinks — or more precisely, the mentions those links represent — are one input into that inference.

What AI models actually look for

The key concept is corroboration. AI models build a picture of your business from multiple sources: your website, your Google Business Profile, directory listings, reviews, and mentions across the web. When multiple independent, credible sources describe you consistently, the model has higher confidence recommending you.

A backlink from a local news article that says "Hamilton roofer Jackson Contracting was recognized for storm response work" does several things at once. It names you. It places you geographically. It associates you with a specific service. It connects you to a credible source. That's far more valuable than 50 links from generic blog networks pointing to your homepage.

Raw link count means much less than it used to

This is the thing that trips up SEO-experienced business owners. A high domain authority score and a strong backlink profile don't automatically translate to AI citation frequency. We've run Signal Checks on businesses with excellent traditional SEO metrics that score 3 or 4 out of 10 for AI visibility, and businesses with almost no backlink profile that score 7 or 8 because their entity data is clean and their mentions are clear.

What matters is not volume. It's source authority — are the sites mentioning you trusted by the crawlers AI models use? Contextual clarity — do the mentions specify what you do and where? And corroboration — do independent sources agree on your name, location, and service?

A mention in your local newspaper, a listing in an industry association directory, a guest appearance on a relevant podcast with detailed show notes — these carry more weight than dozens of thin links from low-quality sites.

Unlinked mentions count too

Here's where AI search diverges sharply from Google. In SEO, an unlinked mention is nearly useless — no link equity, no crawl path, minimal signal. For AI models, an unlinked brand mention can carry nearly as much weight as a linked one.

When Perplexity runs a live search on "best plumber in Barrie," it reads the text of the pages it finds. If a review aggregator, a community forum, and a local blog all mention "Calloway Plumbing" as a solid option, that's three corroborating sources — even if none of them link to your website. The model picks up on the consistency.

This changes the calculus. You're not just trying to earn links. You're trying to earn clear, accurate, consistent mentions across reputable sources.

What link-building efforts still pay off

Not all traditional link-building is wasted. Some of it maps directly to what AI models need.

Local press coverage is high-value. A mention in your city's news site, even a brief one, carries a strong geographic signal. AI models treat local news as a credible source for local business recommendations.

Industry directory listings with backlinks are double-value. They're a credible corroborating source and they pass some traditional SEO value. The key is that the listing contains accurate, complete information — not just a link.

Podcast appearances and speaking engagements with written summaries are underrated. They put your name, business type, and location into searchable text on a credible site with clear context attached.

Generic guest posts on low-authority blogs are close to worthless for AI visibility. They don't corroborate anything meaningful about your business, and they're not the kinds of sources AI models treat as reliable.

The local angle: why this favors small businesses

For local businesses, this dynamic is favorable. You don't need to compete with national brands for broad authority. You need to be clearly and consistently mentioned in the sources AI models consult for local queries — local news, local directories, Google Business Profile, area-specific forums, and industry sites with geographic coverage.

A small HVAC company that gets a mention in a community newspaper feature, has a complete Yelp and HomeStars listing, and gets genuine Google reviews that include the city name is in a strong position for AI recommendations — regardless of its domain authority.

Where to start

If you're auditing your AI visibility and trying to improve it, backlinks are part of the picture but not the first place to look. Start with entity clarity: are you consistently described as the same business, in the same location, doing the same service, across your website, GBP, and top directories? That's the foundation.

Then look at your mention profile. Which credible, independent sources describe your business? Are they specific about what you do and where you do it? If you have gaps, local press outreach, industry associations, and community directories are where to build first.

A Signal Check through Sourcepull will show you your current corroboration score and flag the specific gaps in your mention profile — which sources are missing, and what a competitor in your category looks like by comparison.