How to Get Recommended by AI: A Practical Guide for Products and Brands

Getting recommended by AI means appearing as a named suggestion — not just a cited source — when buyers ask ChatGPT, Perplexity, or Gemini which product, service, or brand to choose.

This is different from getting cited. When AI cites your content, it uses your page as a source. When AI recommends your brand, it names you as the answer. "You should check out [your product]" is more valuable than a footnote link, and it's the outcome most brands should be optimising for.

Here's how it actually happens, what drives it, and what most guides leave out.

Why AI recommendations are now a primary purchase driver

When a buyer asks ChatGPT "what CRM should I use for a 10-person sales team?", they receive a synthesised answer that names 3-6 specific products with reasons to consider each. There's no list of 10 blue links to scroll through. There's no awareness that the AI might be wrong. There's just: "Here are the tools people use for this."

G2's 2025 survey of 1,000+ decision-makers found that 87% say AI tools like ChatGPT, Perplexity, and Gemini are changing how they research software. Half of SaaS buyers now start product research in AI chat rather than Google. AI referrals to websites increased 357% in 2025 alone.

When buyers start their research in AI and your brand isn't in the answer, you're not in their consideration set before they've even begun comparing options.

How AI decides which brands to recommend

Understanding the mechanism is what separates tactical guesswork from systematic optimisation.

AI recommendation is driven by two interacting forces: consensus and confidence.

Consensus: When multiple credible, independent sources describe your product in the same way, AI develops confidence that this description is accurate. If your website, G2 reviews, Reddit threads, industry publications, and LinkedIn discussions all consistently describe your product in the same terms — what it does, who it's for, what problems it solves — the AI treats that agreement as reliable evidence. Conversely, if these sources conflict or are silent about you, the AI either hedges ("might be worth looking at") or omits you entirely.

Confidence: AI recommends brands it can describe accurately with specific supporting evidence. Vague positioning ("the all-in-one solution for growing teams") produces vague AI descriptions that don't generate recommendations. Specific positioning ("CRM built for SDR teams doing high-volume outbound") produces specific AI descriptions that match specific buyer queries.

The practical implication: AI recommendation isn't about tricking an algorithm. It's about making the web's genuine understanding of your brand accurate, consistent, and specific enough that AI can confidently match you to relevant buyer queries.

The three layers that drive AI recommendations

Layer 1: Your owned content (the foundation)

Your website, documentation, case studies, and comparison pages are the primary source AI retrieval systems check first.

Make it readable by AI crawlers. Verify your robots.txt allows OAI-SearchBot, ChatGPT-User, PerplexityBot, Google-Extended, and ClaudeBot. Many sites inadvertently block AI crawlers through Cloudflare firewall settings or overly restrictive robots.txt configurations. Content AI can't access can't contribute to recommendations.

Put content in server-side rendered HTML. AI crawlers generally can't execute JavaScript. Content that loads via client-side rendering — product features, pricing, comparison tables — may be invisible to AI retrieval. Check key pages by disabling JavaScript and verifying content is still visible.

Set up Bing Webmaster Tools. ChatGPT's live search runs on Bing. Submit your sitemap. Free, 10 minutes. Most brands have never done this despite it directly affecting their visibility in the most widely used AI tool.

Use answer-first structure. AI retrieves specific passages from pages. A passage that answers a question in two sentences is more valuable than a paragraph that builds to the answer. Put the direct answer first, then elaborate. Every H2 and H3 should be answerable in the first sentence beneath it.

Maintain visible freshness dates. AI systems strongly prefer recent content. Pages with visible "last updated" timestamps receive 1.8x more AI citations than those without. Update your Article schema `dateModified` field to match actual changes.

The brands that appear most consistently in AI recommendations are the ones whose content appears across the full range of questions buyers ask during their research process.

Layer 2: Third-party validation (the trust layer)

Your own content tells AI what you claim. Third-party sources tell AI what's been independently verified. AI systems weight these differently — and heavily favour independent validation for product recommendations.

Review platforms are critical. G2 is the 4th most-cited source in ChatGPT and 6th in Google AI Mode across the entire tech and SaaS category (Semrush AI Visibility Index). When buyers ask AI "is [your product] good for [use case]?", AI retrieves review platform data to validate or contradict your claims. A brand with 200 detailed G2 reviews describing specific use cases appears in product recommendation answers; a brand with 12 generic reviews often doesn't.

Prioritise getting reviews that describe specific outcomes and use cases, not just star ratings. "We moved from [competitor] to [your product] for [specific reason] and reduced [specific metric] by [specific amount]" is the kind of evidence AI uses to build confident product recommendations.

Industry publication mentions. Being mentioned in publications your buyers read creates citation signals AI trusts. These don't need to be major media placements — niche industry publications that AI retrieval systems have established as credible for your category are often more valuable than general technology coverage.

"Best of" and comparison listicles. When reputable third parties include your brand in "best tools for X" articles, this creates the citation pattern AI looks for. Reach out to authors running relevant roundups and make a genuine case for inclusion. Create your own honest comparison content — including competitors — because AI systems retrieve these pages for comparison queries and your perspective shapes the narrative.

Consistent entity description everywhere. Check what your brand description says on your website, LinkedIn, Crunchbase, G2, Capterra, and any industry directory. These should describe the same product in consistent terms. AI systems cross-reference these sources — conflicting descriptions create uncertainty that reduces recommendation confidence.

Layer 3: Community presence (the most overlooked layer)

Here's what the Entrepreneur article and the Composite guide don't adequately address: for product recommendation queries specifically, community discussion is the primary retrieval source.

When a buyer asks "what project management tools do people actually use?" or "best alternatives to [competitor]?", AI systems retrieve and weight community discussions — Reddit threads, Hacker News posts, LinkedIn conversations, forum discussions — as their primary evidence of authentic user experience and peer recommendation.

Perplexity cites Reddit in 46.7% of its responses. ChatGPT cites Reddit in approximately 11%. For the most commercially valuable AI queries — the ones where buyers are actively evaluating options — community signal is often more influential than anything on your website.

The gap between brands that get recommended in AI product queries and brands that don't is often explained entirely by community presence, not content quality or technical optimisation.

What meaningful community presence looks like:

Find the Reddit subreddits where your buyers discuss their problems and tools. For SaaS, these often include r/[industry], r/entrepreneur, r/saas, r/[category], and more specific communities. Find the Hacker News threads about your product category, the LinkedIn Groups for your industry, and the specialised forums where practitioners compare tools.

When relevant conversations appear — recommendation requests, problem discussions, competitor comparisons — participate with genuine, helpful answers. Not "check out our tool" — answer the actual question, then mention your product where it's genuinely the right fit. An upvoted comment that says "we switched from [competitor] to [your product] because of [specific operational reason], here's what changed" is exactly what Perplexity retrieves when assembling product recommendation answers.

The compounding effect: Authentic community mentions accumulate in both AI training data and live retrieval pools. A brand with consistent, genuine community presence appears in AI answers to product recommendation queries. A brand absent from community discussion is invisible to Perplexity's dominant citation source.

At scale: Monitoring buying intent conversations across Reddit, LinkedIn, Hacker News, and industry forums manually isn't sustainable for most teams. Tools like Handshake monitor these platforms simultaneously for conversations where your product is genuinely relevant — recommendation requests, competitor comparisons, problem discussions — and draft contextually appropriate replies for posting from your account. This builds the community footprint that directly feeds AI product recommendation retrieval across multiple platforms.

Specific content that drives recommendations

Case studies with measurable outcomes. "We helped Company X reduce churn by 34% over 90 days by implementing [specific approach]" is citable. "We help companies reduce churn" is not. AI systems use specific outcome data as evidence when making confident product recommendations. Case studies should include company name (when possible), specific metrics, and the specific mechanism that produced the result.

Comparison pages you publish yourself. If you don't create content comparing your product to competitors, someone else controls that narrative. Create honest "[your product] vs [competitor]" pages that acknowledge competitor strengths while explaining your differentiation. These pages rank for high-intent queries and get retrieved by AI for comparison questions — Omnisend's own comparison blog post appeared as a citation when ChatGPT was asked to compare Omnisend vs Mailchimp.

FAQ content that mirrors buyer questions. Structure FAQ content around the actual questions buyers ask when evaluating your category — not the questions your marketing team thinks they should ask. Questions like "Is [your product] good for [specific company type]?" and "How does [your product] compare to [competitor] for [specific use case]?" are the queries AI systems retrieve FAQ content for.

Pricing transparency. When SaaS brands don't publish transparent pricing, AI models fill the gaps using community speculation. Community speculation is often associated with negative sentiment. Publishing clear pricing information — even range pricing — reduces the likelihood of AI presenting inaccurate, negative pricing narratives about your brand.

Practical starting checklist

For brands new to AI recommendation optimisation, this is the priority order:

How to know if it's working

Manual share of voice testing. Query 15-20 relevant prompts monthly across AI platforms. Log which brands appear, how they're described, and whether yours is among them. Track changes month over month.

AI referral traffic. Monitor GA4 for referrals from chat.openai.com, perplexity.ai, gemini.google.com, and related platforms. Volume is still small for most brands but growing fast and converting at 4.4x the rate of traditional organic search traffic.

Branded search volume. Rising branded search in Google Search Console is a downstream signal of increasing AI recommendation frequency — buyers encounter your brand in AI answers and search for you directly.

Dedicated tracking tools. Semrush AI Visibility Toolkit, Otterly.AI ($25/month starting), Peec AI (€89/month starting), Profound (enterprise pricing) all track mention frequency, share of voice, and sentiment across AI platforms.

For implementation context, review Google Search documentation. For implementation context, review Schema.org.