We've run thousands of products through pickedby.ai's probe system. We've seen scores range from 0 to 97. And after all of that data, the same patterns show up again and again in the low-scoring products.
These aren't obscure edge cases. They're the default state for most indie products, micro-SaaS tools, and digital products built by founders who are good at building — but haven't thought about how AI discovers and recommends things.
Here are the five mistakes we see most often. And exactly what to do about each one.
A single Gumroad listing. One Product Hunt page. Your own website. That's it.
AI systems learn from patterns across many sources. One page means one data point. To understand what your product is, who it's for, and why it matters, AI needs to have encountered it in multiple independent contexts: a Reddit thread, a comparison post, a newsletter mention, a blog review. Not just your own marketing copy.
Figma is recommended constantly not because Figma's website is better — it's because ten thousand public threads, tutorials, and comparisons all mention Figma by name. Your tool might be objectively better. But AI doesn't know that if it's only seen it in one place.
Get your product mentioned by name in 3–5 public places outside your own website. One Reddit thread in a relevant community. A comparison post on Medium. A mention in someone else's newsletter roundup. Each one compounds.
When someone asks AI "what's the best tool for freelance project management," AI answers based on which products it associates with that category. If it doesn't associate your product with that category — even if you built it specifically for freelance project management — you won't appear.
Category association comes from how you're described in public content. Not how you describe yourself on your homepage. If every mention of your tool says "minimalist productivity app," AI files you under "productivity" — not "freelance project management." Those are different queries. Different recommendations.
Write one comparison post targeting your exact category: "[Your Tool] vs [Competitor] for [specific use case]." This single piece of content teaches AI exactly what category you belong in, who you're competing with, and which queries you should appear in.
When AI recommends tools for a given use case, it typically names 3–5 products together. The products that appear in that list aren't random — they're the ones that already co-appear in training data. If a product is mentioned alongside Notion in five hundred threads, AI starts to associate them. When someone asks for Notion alternatives, that co-appearing product has a much higher chance of showing up.
Most indie products have zero co-recommendation signal. They exist in a vacuum. AI has never seen them mentioned alongside anything else in their category, so it has no context for when to surface them.
Write content that explicitly compares or groups your product with 2–3 category leaders. "Tools like Notion, Coda, and [Your Product] are all worth considering for..." This is how you get added to the mental shortlist AI maintains for your category.
We see this constantly: "The Freelance Bundle" on Gumroad, "Freelance Starter Kit" in email newsletters, "my template pack" in Reddit posts, "FL Bundle" in Twitter threads. The founder knows these all refer to the same thing. AI does not.
Every variation fragments your signal. Instead of building up recognition for one consistent product name, you're splitting your signal across four ghost products that AI can't connect. None of them reaches the threshold of recognition.
Pick one name. Use it exactly, every time, everywhere. Your exact product name in the first line of every public description, post title, and forum mention. Consistency is how AI learns to associate all of that signal with one thing: you.
88% of AI citations don't overlap with Google's top 10 results for the same query. These are two independent systems with different signals. Your Google ranking — however good it is — contributes almost nothing to your AI visibility.
Backlinks matter for Google. Named mentions in diverse public content matter for AI. Keyword density matters for Google. Semantic context and category association matter for AI. You can rank #1 on Google and be completely invisible to every AI system. It happens constantly.
Treat AI visibility as a separate channel with separate metrics. Measure your AI recognition score. Track which engines know you and which don't. Build signal deliberately for this channel — it won't come from your SEO work automatically.
The common thread: AI visibility isn't built by having a great product page. It's built from the accumulated public signal about your product across the open internet. Most founders haven't built any of that signal yet. The good news is that even small moves — one Reddit thread, one comparison post, one consistent name — compound quickly once you know what you're measuring.
Where to start
Before fixing anything, measure where you actually stand. Most founders don't know which of these mistakes apply to them — or how severe the problem is.
pickedby.ai gives you a score from 0 to 100 across four dimensions: direct recognition, category ranking, co-recommendation graph, and web authority. In about 10 seconds, you'll see exactly which dimension is your bottleneck — and what to prioritize first.
FIND YOUR BIGGEST AI VISIBILITY MISTAKE
Check your score across all four dimensions — recognition, category, co-recommendation, web authority. Free, ~10 seconds, no signup.
CHECK MY SCORE — FREE ▶▶One more thing: the founders who fix these problems now are building a compounding advantage. Every public mention, every consistent name usage, every comparison post is data that feeds into future AI training. The ones who start in 2026 will have a head start that's genuinely difficult to close later.
See how the four dimensions work in detail: GEO is the new SEO — here's what it actually measures →