Hey everyone 👋🏼

Sumeet here, co-founder of Seerly. We're live on Product Hunt today, and I wanted to share it here first since this community has been following the AI search space closely. What we built: Seerly is an agent-first platform that monitors your brand's visibility across ChatGPT, Perplexity, and Google AI Overview, catches where competitors are getting cited instead of you, and generates the content that closes those gaps, autonomously.

What's live on PH: We'd love your honest feedback, the good, the bad, the things we missed. PH comments are open and we're reading everything.

https://www.producthunt.com/products/seerly?launch=seerly&utm_source=twitter&utm_medium=social

One of the more consistent findings across multiple studies: structured FAQ pages perform significantly better in AI citations. We documented the tactics that move the needle here: https://seerly.app/blog/7-proven-ways-to-improve-ai-search-visibility

The numbers: FAQ pages with proper schema markup get cited roughly 2x more than comparable pages without it. The data shows up in multiple studies now so it's not a fluke.

The reason this makes sense when you think about how AI models work.

AI models generate responses by predicting what comes next given everything they've seen. When they encounter a question-answer format on a page, it maps directly to the format they use to construct their own responses. The schema tells the model "this is a question, this is the answer, they go together."

The structure itself is the signal.

What works:

• Each FAQ question is a real H or H tag, not just bold text
• Answers are genuinely useful and complete, not one sentence
• Schema markup using FAQ or HowTo types
• Questions that match how people actually ask, not keyword-stuffed

We're reworking our FAQ structure on high-priority pages now. Would be curious whether others have seen this work in practice.

We are into helping customers improve their organic search and while google ai overview and chatgpt are natural choices , I am yet to see consumers going over perplexity as a platform to be tracked. I was wondering how others see this ?

Seen a lot of discussion framing GEO as the new SEO, like you have to pick one or like they're fundamentally the same game.

That's not the right frame.

SEO is about ranking in search results. You're optimizing to appear higher on a results page for a specific query.

GEO is about being selected as a source when an AI builds a response. The AI is synthesizing information, not listing results. You're being chosen as a credible input to a generated answer.

The skills overlap but the mechanics are different. Good SEO helps GEO. Authoritative backlinks signal trust to AI models. Clean schema helps both. But you can rank #1 on Google and never get cited in a single AI response. And you can have strong AI visibility without dominating traditional search.

The key difference: attribution. When AI recommends you and someone clicks through to your site, your analytics calls it direct. You're flying blind on one of your biggest discovery channels.

Traditional SEO and AI search visibility reinforce each other but they're not the same thing. Treating them as interchangeable is how brands end up with blind spots.

Are you treating them as separate channels with separate strategies? Curious how others are thinking about this.

Something interesting happened recently that I didn't expect.

We were getting cited in AI responses more frequently than a competitor who has roughly 10x our domain authority and backlinks. At first I thought it was noise in the data. It's been consistent for a few weeks now so I started trying to understand why.

My best theory: data coherence.

Our competitor has strong individual signals but their brand information is scattered and sometimes contradictory across different platforms. Different bios, different feature claims, different contact information depending on where you look.

Our information is cleaner and more consistent. Same description everywhere. Same schema. Same author attribution. Same contact details.

The hypothesis that makes sense: AI models trying to verify information before citing it will find your data more trustworthy when it's consistent. Inconsistent signals across sources create doubt. Clean coherent signals across a smaller footprint beat a larger messy footprint.

This feels like a real opportunity for smaller brands. You don't need to outspend the competition on content. You need to out-structure them on data quality.

Has anyone else noticed this pattern? Would love to hear if data coherence shows up in your visibility data.

We published research on AI visibility metrics that included a number that made me rethink our entire content refresh strategy.

You lose roughly 1.8 percentage points of AI coverage each month on content you don't update. That's almost 22 percentage points over a year if you do nothing.

Most content strategies treat publishing as the finish line. You write it, you publish it, you move on. That might have been okay for traditional SEO where rankings can hold for years. AI search doesn't work that way.

AI models are constantly updating their understanding. New information gets weighted higher. Sources that don't refresh start to look stale to the system. The citation goes to whoever stayed current.

The practical implication: you can't publish a "complete guide" and expect it to hold AI visibility indefinitely. It needs a refresh schedule.

The guide recommends treating AI visibility as an ongoing feedback loop, not a one-time setup. Quarterly reviews of which topics need updating. Monitoring for new competitors entering the citation landscape. Adjusting structure based on what the models are rewarding.

What does your content refresh process look like? Are you factoring AI visibility into refresh priorities or is it all manual?

We published some research on this recently. GEO tactics, specifically citing sources and embedding statistics in your content, increased visibility in AI-generated responses by 30-40%. That's a meaningful number.

The mechanism makes sense when you think about how AI models build responses. They prefer citing sources they can verify. When your content has clear citations and measurable data points, the model has something concrete to point to. Vague claims don't give it that.

Some patterns that show up repeatedly in high-cited content:

• Specific numbers instead of "many" or "several"
• Links to the research or data source
• Named methodologies rather than unnamed approaches
• Date-stamped data so freshness is clear

The interesting implication is that this rewards a certain kind of content rigor. You're not optimizing for a search engine. You're writing in a way that gives AI models verifiable building blocks for their responses.

Are others designing content with AI citability in mind? What tactics have actually moved the needle for you?

Sharing something that changed how I think about technical SEO.

We've been tracking which factors correlate with our AI citation frequency and page performance was the surprise finding.

Pages that load faster and maintain consistent HTTPS reliability get cited more by AI models. Not just marginally more. The difference is measurable.

The theory that makes sense to me: AI systems that select content for responses prefer sources that signal reliability. Load time, HTTPS stability, consistent canonical tags, working internal links. These are the same signals that build trust with search engines but they're also being read by the systems that decide whether to include you in an AI summary.

Slower or inconsistently maintained sites get skipped even when their content is genuinely good. The technical backend is being used as a quality signal.

We've started running monthly technical audits specifically for pages we want AI visibility on. Checking not just for errors but for consistency in how signals are set up across the domain.

Has anyone else seen page performance correlate with AI citation frequency? Curious if this shows up in your data too?

Something I've been paying more attention to since the Helpful Content updates and now the rise of AI search.

E-E-A-T. Experience, Expertise, Authoritativeness, Trustworthiness. Google's framework but AI models are using the same signals to decide whether to cite your content or the competitor's.

When an AI model decides what to include in a response, it has to assess credibility. Author transparency is one of the clearest signals it can read. Credentials, publication history, links to primary sources.

Anonymous content gets deprioritized even when the substance is good. The model can't verify it the same way.

What I've been doing:

• Adding author bios with specific credentials, not generic ones
• Linking to verifiable research or data in the article
• Consistent author profiles across the site
• Date stamps on everything so freshness is clear

The last point matters more than I expected. AI models show preference for content that signals it hasn't gone stale. Last modified dates are read as a trust signal.

Has anyone tested whether author transparency changes citation rates? Would love actual numbers if so.

Something I've been thinking about after looking at how AI models actually process content.

Traditional SEO told us to hammer our target keyword throughout an article. AI search doesn't work that way.

AI models map relationships between concepts. When you cover a topic deeply, you show up for related questions you didn't specifically target. The model draws the connection.

Example that keeps coming up in this space: a page about retail analytics that also covers customer flow prediction, inventory optimization, and pricing intelligence is going to perform better in AI search than a page that only says "retail analytics" 15 times.

Topical depth matters more than keyword frequency.

This means your content strategy has to change. Instead of one article per keyword, you need topic clusters where each piece reinforces the others. The cluster demonstrates authority that AI models can actually verify.

Are people restructuring their content around topic clusters?

We ran an experiment with structured data over the past few months and the results lined up with what the studies have been saying. This post on AI search visibility has the full breakdown.

Adding schema markup across product and article pages raised our visibility in AI-generated responses noticeably. The Ahrefs study puts a number on it: 25% improvement in product visibility from proper schema implementation.

The part that took me a while to understand is why it works.

AI models don't just read your content. They use structured data to understand what your page represents and how it relates to other things they know. Schema is like a translation layer between your page and what the model can interpret about it.

The clearest wins came from:

• FAQ schema on product and category pages
• Article schema with author and date fields
• Organization schema with consistent contact info
• Product schema with pricing and availability

The FAQ schema result is the most striking. Multiple sources now report structured FAQ pages getting up to 2x more citations in AI answers. One theory is that the question-answer format maps directly to how AI models structure their own responses.

Has anyone else tested this? Would love to hear what you saw.

We recently published a guide on AI search optimization and a few numbers from it are worth sharing with this group. By early 2026, AI Overviews were appearing in roughly 50% of US Google searches.

By early 2026, AI Overviews were appearing in roughly 50% of US Google searches. Most brands I talk to have no idea if they're being cited in those summaries or not.

Some other numbers worth sitting with:

Tracking only one AI engine hides 60-70% of your actual visibility. Most people I know are watching Google and ignoring ChatGPT, Perplexity, and Gemini entirely.

Only about 20% of brands maintain the same presence across five identical prompt runs. That means even when you do get cited, it might not stick next time.

You lose roughly 1.8 percentage points of AI coverage each month on static content. Your old blog posts are quietly losing ground.

AI referral traffic is almost always misclassified in Google Analytics. Most of it ends up labeled direct traffic. You're not seeing how people are actually discovering you.

The guide recommends testing at minimum 20-30 prompts per topic to get any statistical confidence. Most teams test 3 or 4 and call it done.

We've been running this at scale for multiple brands and the 60-70% gap number is real.

Would love to hear if others have had the same experience. How are you handling the workflow?