How to Get Your Business Recommended by ChatGPT

Getting your business recommended by ChatGPT starts with a simple shift in how people search. Someone typing “who’s a good web designer in Belfast” no longer scrolls through ten blue links. They ask an assistant, and the assistant answers with a short list of named businesses, drawn from entity data, reviews, directories, and structured markup rather than traditional rankings.

That list isn’t random, and it isn’t paid placement either. ChatGPT, Gemini, and Perplexity assemble their answers from specific, checkable signals: how consistently a business names itself online, how complete its Google Business Profile is, how many recent reviews it has, whether it appears in the directories these systems draw on, whether its website carries Organisation and Service schema, and whether its own pages contain sentences an AI can quote with confidence.

ProfileTree, a Belfast-based digital marketing agency, has run this exact programme on its own listings, Google Business Profile, and service pages over the past year. What follows is the practitioner’s version: six concrete steps, in the order that actually moves the needle, plus a test any business can run tonight to see where it currently stands.

Why AI recommendations are becoming their own channel

For years, the goal of local marketing was straightforward: rank in Google, appear in the map pack, get clicked. That’s still true, but it’s no longer the whole picture. Search Engine Land and several independent studies through 2025 and early 2026 tracked a steady rise in “assistant-first” queries, in which the first stop in a buying decision is a conversation with an AI tool rather than a search box.

The shift matters most for service businesses selling to other businesses, because B2B buyers already default to asking around before they buy. A recommendation from a trusted colleague used to come from a phone call or a LinkedIn message. Increasingly, it comes from typing a question into ChatGPT and treating the answer the way you’d treat advice from a well-informed friend, useful, quick, and rarely double-checked.

That’s the opportunity and the risk in one sentence. If an assistant has good information about your business, they will happily recommend you. If it has thin, inconsistent, or outdated information, it will either recommend a competitor with cleaner signals or hedge so heavily that the recommendation is worthless. Businesses that have invested in search engine optimisation already have a head start here, because a lot of the underlying groundwork, consistent entity data, structured content, and genuine authority signals overlap directly with what makes a business AI-recommendable.

Ciaran Connolly, founder of ProfileTree, puts it plainly: “The businesses getting recommended by ChatGPT right now aren’t necessarily the biggest names in their sector. They’re the ones whose information is clean, consistent, and easy for a machine to verify in seconds. That’s a fixable problem, not a budget problem.”

How AI assistants actually decide who to recommend

It helps to understand the mechanics before changing anything, because guessing at this stage wastes effort on the wrong fixes.

Large language models don’t “know” your business the way a person does. They build a picture from training data, from live web retrieval when the tool supports it (ChatGPT with browsing, Perplexity, Gemini, and Google’s AI features all do this to varying degrees), and from structured data sources such as knowledge graphs, business directories, and review platforms. When someone asks for a recommendation, the assistant is effectively running a rapid, informal version of due diligence: does this business exist, is it real, is it active, is it liked, and does it do the specific thing being asked about?

Five signal categories carry most of the weight:

Entity consistency. The same legal or trading name, the same descriptor, the same location details, repeated identically across the web. Inconsistency signals a risk to a system trying to verify that a business is real.

Review signals. Volume, average rating, and recency. A business with 460 reviews and a steady trickle of new ones every month looks active and trustworthy. A business with 12 reviews from three years ago looks dormant, even if the reviews are glowing.

Directory and citation presence. Appearing correctly across the directories and data aggregators that feed AI knowledge graphs, not just Google Business Profile.

Structured data. Schema markup that states, in a format machines parse reliably, what the business is, what it does, where it operates, and what it charges or how to get a quote.

Quotable content. Sentences on the business’s own site that answer the exact question a buyer might ask, written so an AI system can extract them cleanly and attribute them with confidence.

None of these five categories works in isolation. A business with excellent reviews but inconsistent name formatting still confuses the verification step. A business with an immaculate schema but no reviews still looks unproven. The programme below addresses all five together, because that’s what the data supports.

Step 1: Lock down one consistent name and descriptor everywhere

This sounds almost too basic to matter, and that’s precisely why most businesses skip it.

Pick one exact form of the business name, one descriptor phrase, and one address format, then audit every place the business appears online: Google Business Profile, Companies House (or equivalent), LinkedIn, Facebook, industry directories, supplier listings, and the footer of the website itself. Look for variations such as “ProfileTree Ltd” in one place and “Profile Tree” in another, or a Belfast address written three different ways across three platforms.

Each inconsistency is a small piece of friction for an AI system trying to confirm the business is a single, real, ongoing entity. With enough friction, the system either skips the business in favour of a cleaner competitor or hedges its answer with language of uncertainty, which undermines the whole point of being recommended.

The fix is unglamorous: a spreadsheet listing every place the business name appears, the current wording in each, and the corrected wording to match. It’s slow work, but it’s the foundation on which everything else sits. Our Belfast team run this audit as the first step on every new AI visibility engagement, before touching schema or content, because fixing entity consistency after the fact means redoing work twice.

Step 2: Build out a complete, current Google Business Profile

Google Business Profile data feeds directly into Google’s own AI features and, through various data partnerships and public APIs, into other assistants as well. A thin or neglected profile is one of the most common reasons a genuinely good business gets overlooked.

A complete profile includes accurate categories (primary and secondary), a full business description written in plain language rather than marketing copy, current opening hours, a working website link, service area details where relevant, and regularly updated photos. Businesses serving Northern Ireland and further afield should also make sure service-area settings reflect that; a profile that only signals “Belfast” when the business also delivers SEO across Northern Ireland is quietly limiting its own visibility.

Posts matter too, though not in the way most businesses assume. Google Business Profile posts aren’t primarily for driving clicks; they’re a freshness signal. A profile with a post from 14 months ago suggests to an AI system that the business may not be actively monitored. A profile updated every few weeks says the opposite.

Step 3: Treat reviews as an ongoing signal, not a one-off task

Review volume and rating still matter, but recency has become just as important, arguably more so for AI recommendation specifically. An assistant weighing up two businesses with similar ratings will lean toward the one with reviews from the past few weeks over the one whose most recent review is from two years ago, because recency signals the business is currently operating and currently satisfying customers.

The practical implication is a standing process, not a campaign. Build review requests into the natural end of a project or service delivery, rather than running an occasional push when someone remembers. Aim for a steady drip rather than a burst followed by silence.

Review language matters, too. Reviews that mention specific services, locations, or outcomes (“helped us redesign our WordPress site ahead of a product launch in Derry”) give AI systems more to work with than generic five-star ratings with no detail. Without asking customers to write anything specific, businesses can prompt for detail simply by asking, “What did we help you with?” rather than “Leave us a review.”

Step 4: Check the presence across the directories that AI systems actually draw on

Beyond Google Business Profile, a handful of directories and data aggregators feed into the knowledge graphs and retrieval systems that AI assistants consult. Coverage varies by industry and region, but the principle holds everywhere: if a business’s information exists only on its own website, it’s asking an AI system to take its word for it without independent confirmation.

Relevant categories to check include general business directories, industry-specific listing sites, Companies House or the equivalent company register, LinkedIn’s company page, and any accreditation bodies the business belongs to. Each of these acts as a corroborating source. The more independent, consistent sources that confirm the same facts about a business, the more confidently an AI system can include them in an answer.

This doesn’t mean indiscriminately chasing every directory. A handful of accurate, high-quality listings outperform dozens of low-quality ones, and low-quality directories can actively hurt entity consistency if they carry outdated information the business no longer controls.

Step 5: Add Organisation and Service schema to the website

Schema markup is the most technical step in this programme and also one of the highest-leverage. Organisation schema tells search engines and AI crawlers, in a structured format, exactly who the business is: legal name, logo, address, contact details, social profiles, and founding information. Service schema does the same for individual offerings: what the service is, who it’s for, and where it’s delivered.

Without a schema, an AI system has to infer these facts from unstructured page text, which is slower and less reliable. With the schema in place, the same facts are provided in a format optimised for machine parsing. Google’s own documentation on AI features confirms that structured data matching visible content is one of the basics that support AI-generated results, alongside internal linking and page experience.

Practically, this means every service page should carry Service schema, matching what’s visibly described on the page (never markup describing something the page doesn’t actually say), and the site as a whole should carry Organisation schema in the header or footer template. This is developer work rather than content work, so it’s worth flagging clearly in any brief handed to a development team rather than assuming it’ll be picked up as an afterthought.

Step 6: Write service pages that an AI system can quote with confidence

The final piece is the content itself. Even with perfect entity consistency, a glowing review profile, and a clean schema, an AI system still needs sentences on the business’s own site that directly answer the question being asked, written clearly enough to lift and attribute.

This is where the BLUF (bottom line up front) principle earns its place. A service page that opens with three paragraphs of scene-setting before mentioning what the service actually includes gives an AI system nothing quotable in the section it’s most likely to extract from. A service page that, in the first two sentences, states exactly what the service is, who it’s for, and what it typically costs or how pricing works, hands the assistant a ready-made answer.

Specificity beats generality every time. “We help businesses grow online” is unquotable because it says nothing concrete. “ProfileTree provides WordPress web design for small and medium businesses across Northern Ireland, with most projects delivered in four to eight weeks”, gives an assistant a fact that they can repeat with confidence. The same logic applies across every service line, from content marketing to video production to organic social media management: state plainly and early what the service covers, who it suits, and what a buyer can expect.

A dedicated trust page, sometimes called a “Why Choose Us” page, deserves particular attention here. AI systems draw heavily on these pages when assembling recommendations, because they consolidate exactly the facts an assistant needs in one place: services offered, review numbers, accreditations, team experience, and a clear route to getting a quote. A business without a page like this is making an AI system work harder than it needs to, and an AI system that has to work harder usually just moves on to a competitor whose facts are easier to find.

Businesses further along this path often extend the same thinking to web design services, YouTube marketing, and animation services pages, applying the same answer-first structure across the full service catalogue rather than treating it as a one-page exercise. The digital marketing strategy service page is a good template to study, since strategy pages tend to force the clearest, most quotable statements of scope and process.

The test to run tonight

Before spending a budget on any of the above, it’s worth finding out where a business currently stands, and that takes about ten minutes.

Open ChatGPT, Perplexity, and Gemini in three separate tabs. Ask each one the same question, phrased as a real customer would: “Can you recommend a [service type] in [town]?” Note exactly who each assistant names, in what order, and what each one says about the business. Then ask a follow-up: “Why did you recommend them?”

The answers reveal a lot fast. If a business doesn’t appear at all, that’s a clear entity or visibility gap. If it appears but the assistant gets the basic facts wrong (the wrong location, the wrong service list, outdated information), that’s an entity consistency problem worth fixing first. If it appears correctly but is ranked behind competitors, the review and schema signals are the likely next place to look.

Repeat the test every few weeks rather than once. AI-generated answers shift as the underlying data changes, sometimes faster than traditional search rankings do, so a business that doesn’t appear this month may well appear next month once the review count climbs and the schema goes live. It’s worth tracking that movement rather than assuming a single check tells the whole story.

Where to start this week: Business Recommended by ChatGPT

Entity consistency comes first, since every other signal gets discounted if an AI system can’t confirm the business is real and singular. Reviews and schema tend to move fastest after that, since both sit within a business’s direct control.

None of this replaces solid search engine optimisation; it sits alongside it. Businesses working with an agency, or considering AI training for SMEs to run internally, should ask for evidence of what changed and why, not just a promise of improvement.

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