An Answer Engine Optimization Agency Focused on Improving AI Recommendations

Why some brands get recommended and others don't

When an AI model recommends a brand, it's synthesizing from whatever it can access and trust: your own site if crawlers can reach it, and — more heavily — third-party sources like reviews, comparison articles, and industry citations. A brand with a technically perfect website but zero external citations often loses to a mediocre website with strong third-party presence, because the model treats independent sources as more credible than a company's own claims about itself.

It's worth noting that improving recommendation rate is rarely about a single dramatic fix — it's usually three or four smaller corrections (a blocked crawler here, a missing citation there) compounding together over a few months.

The three levers that move recommendation rate

An answer engine optimization agency focused on improving recommendations works three levers together. First, crawler access — if OAI-SearchBot or PerplexityBot can't reach your key pages, you're invisible regardless of content quality. Second, content structure — answers, comparisons and definitions written the way a model likes to extract and quote, not buried in marketing copy. Third, external citations — press mentions, review sites, forum discussions and comparison content that exists outside your own domain.

• Fix crawler access first — it's the most common silent blocker
• Restructure key pages into direct, quotable answers
• Build citations on sites the model already trusts in your category

Brands sometimes assume a recommendation problem is a content quality problem and rewrite everything, when the actual blocker was a crawler misconfiguration the whole time — which is why the audit step has to come before any content rewrite, not after.

What to look for in an agency

Ask specifically how they'll build citations, since this is the piece most agencies skip or fake with low-quality guest posts. A real citation strategy targets sites and sources AI models already cite frequently in your category — you can find these by seeing which domains show up as sources in current AI answers about your competitors. Look for an agency that does this diagnostic work before recommending tactics, rather than running the same playbook for every client.

Third-party citation building takes patience: reaching out to a trading forum or review site that already has established trust with AI models is slower than publishing on your own blog, but it moves the needle far more per citation earned.

A brand that's already investing in traditional PR or influencer partnerships has an advantage here, since that activity often generates exactly the kind of third-party mentions AI models cite — the AEO work just needs to make sure those mentions are structured and indexed properly.

Worked example: a trading signals service

A trading signals brand wants to be recommended when someone asks 'which signal service actually works.' The audit shows ChatGPT currently cites two competitors, both of which have independent reviews on trading forums and comparison sites the brand itself has never been mentioned on. The fix isn't rewriting the brand's homepage — it's getting cited on those same trusted third-party sources, restructuring the brand's own comparison page to directly answer 'does this signal service work,' and confirming crawler access isn't blocking the page in the first place. Paid ChatGPT ads targeted at the same prompt category can run in parallel to capture demand immediately.

For a regulated category like trading platforms, credibility signals compound — a licensed platform that gets cited accurately on one respected trading forum often finds that citation referenced again in follow-up AI answers, creating a snowball effect over time.

Worth adding: recommendation improvements aren't always permanent. Models get updated, competitors catch up, and a citation advantage built this quarter can erode by next quarter without ongoing maintenance — which is why this is better framed as a standing program than a one-time project.

How Suggesting.ai runs this

Suggesting.ai starts every engagement with a free audit that identifies exactly which competitors get recommended for your core prompts and why — which citations, which content structure, which crawler differences. From there it builds a citation plan, fixes technical access issues, and restructures priority pages, while optionally running paid AI ad campaigns for immediate presence. The goal is straightforward: when ChatGPT is suggesting who to trust in your category, Suggesting.ai works to make sure it's suggesting you.

Paid and organic recommendation work reinforce each other in practice: a prospect who sees a labeled ChatGPT ad and later finds the same brand cited organically in a follow-up query develops more trust than either signal alone would produce.

Measuring improvement over time

Track recommendation rate against a fixed set of prompts monthly — not a random sample, the same prompts every time so the trend is real. Pair that with AI-referred traffic in analytics and conversion rate from that traffic, since studies report AI referrals convert several times better than average organic search traffic when the recommendation is genuinely relevant.

None of this replaces having an actually good product or service — AEO can correct for invisibility, but it can't manufacture recommendations for a brand that genuinely underperforms its competitors on the qualities buyers care about.

It's also worth checking recommendation rate separately for each major AI platform rather than assuming they behave identically — ChatGPT, Perplexity and Gemini pull from different citation sources and weight them differently, so a gain on one doesn't guarantee a gain on another.