What is AI visibility and why your startup needs it

The short version

When someone types "what's the best tool for X" into ChatGPT or Gemini, the model produces a ranked list of products. AI visibility measures how often your product appears on those lists, and how high up it sits.

It's the new version of "ranking on page one of Google" — except the results come from AI training, web presence, structured documentation, and how well the broader internet talks about your product. Not links and keywords alone.

Why this matters now

A few numbers that explain the shift:

• 80–83% of Google searches now end with zero clicks because AI Overviews answer the question directly on the results page. Organic search traffic is declining.
• Perplexity, ChatGPT, and Gemini handle hundreds of millions of queries per day — and a growing share of those are product research queries ("best analytics tool for SaaS", "cheapest email marketing for small business").
• Products recommended by AI see an average of 35% more clicks than those that don't appear, because AI recommendation carries trust weight. When ChatGPT says "try Notion for notes," people try Notion.
• Only 1.2% of local businesses and software products get recommended by AI when buyers ask about their category. For most products, the score is zero.

This isn't a future trend. It's already the dominant discovery path for technical buyers — developers, founders, and early adopters who use AI assistants daily.

How AI models decide what to recommend

AI assistants don't have a PageRank-style score for products. Instead, they synthesize from everything they know about a product category — training data, indexed web pages, documentation, user reviews, forum discussions, and structured data. A few factors consistently separate products that get recommended from ones that don't:

1. Presence in training data and index

If your product is mentioned across multiple credible sources — docs, blog posts, comparison articles, forums — the model has more signal to draw on. A product that only exists on its own landing page is easy to miss.

2. Clear, consistent category positioning

AI models use category labels to match products to questions. If your product is described differently across your site, your README, and third-party reviews, the model gets confused. "Color palette tool for designers" should appear in your tagline, your description, and in how others describe you.

3. Mentions on trusted sources

Reddit, Hacker News, GitHub stars, and product directories like Product Hunt all feed into what AI knows about your space. A product with active community discussion gets recommended more often than one that doesn't.

4. Structured FAQ and documentation

AI models are very good at extracting structured answers. A well-written FAQ that uses the exact buyer question as the heading ("What's the best way to export color tokens from Figma?") is more likely to surface your product when that question is asked.

5. Being named in comparison content

Articles titled "X vs Y", "best tools for Z in 2026", or "alternatives to X" are heavily weighted. If your product appears in these comparisons — even in third place — it enters the model's recommendation pool for the category.

How to measure your AI visibility

Manual testing is the starting point: open ChatGPT, Gemini, and Perplexity separately and type the questions your buyers would ask. Note whether your product is named, and where in the list it appears. Do this for 5–8 buyer questions across your category.

The problem with manual testing is that it's a point-in-time check, it doesn't scale, and you can't track it over time. A better approach is to automate it — define your buyer questions once and let a tool run them across all three models on a schedule.

Viestro's AI visibility check does exactly this: run 5–8 buyer questions against ChatGPT, Gemini, and Perplexity in one click, get a score (0–100) and a per-model breakdown, see who the AI recommends instead of you, and track how the score changes over time.

What a good score looks like

Based on checks across the Viestro user base:

• 0–24: Not yet ranked. The AI models don't associate your product with the relevant buyer questions. You're likely a newer product, or one without enough external mentions yet.
• 25–59: Building presence. You appear for some questions on some models. The product is known but not dominant — room to climb by improving positioning and external mentions.
• 60+: Strong AI visibility. You appear consistently, often in the top 3, across most of your buyer questions. Established products with strong community presence and good documentation tend to land here.

Most products start at 0 and reach the 25–45 range within 3–6 months of consistent work on the factors above.

The GEO concept

Generative Engine Optimization (GEO) is the emerging field that applies SEO-like thinking to AI recommendation systems. The core idea: if you want to appear in AI-generated answers, you need to optimize for how AI models build knowledge about your category — not just for link graphs and keyword density.

Tactics that work for GEO:

• Write FAQ content that uses exact buyer questions as headings
• Get your product listed on comparison and directory sites
• Maintain a public changelog so your product looks actively maintained
• Use consistent language across your landing page, docs, and GitHub README
• Get mentioned in community discussions where your buyers already are (Reddit, Discord, Hacker News)

Next steps

Once you have a baseline score, the next step is understanding why you're invisible for certain questions and what specific actions would move the needle. That's where an AI action plan comes in — a prioritized list of steps specific to your product and the questions you're missing.

Related guides:

• How to get your tool recommended by ChatGPT and Gemini
• How to turn your Git commits into a clean changelog
• Where to share your side project launch