I ran 198 real buyer questions through five AI engines, then validated against an independent dataset, to find out who actually gets cited for web scraping. The scoreboard surprised me twice — once when my own headline turned out to be wrong.
Rank trackers tell you where you stand on Google. Nobody can tell you where you stand inside ChatGPT, Perplexity, or an AI Overview — which is inconvenient, because that's where your buyers moved.
So I built the tracker. Then I pointed it at a category with real money in it: web-data tooling — Firecrawl, Apify, Bright Data, ScrapingBee, and 22 other vendors who all describe themselves as the infrastructure layer for AI. Fine. Let's ask the AI.
To be clear: Firecrawl didn't ask for this. I picked them because they're the category's momentum story — the "everyone's favorite scraping tool for LLMs" — which makes them the perfect test of a question I keep asking clients: does AI-era buzz convert into AI-era citations?
Mostly yes. Partly no. The "no" is where it gets interesting.
The method (so you can argue with it)
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198 queries a developer evaluating web-data tooling actually asks, across 8 intent clusters, each scored
intent_weight × revenue_proximity. Not "what is web scraping" filler — comparison, pricing, jobs-to-be-done, "best X for Y" queries. - 5 surfaces: Perplexity-Sonar and GPT-4o-search (both return real citations), Claude and Gemini (no live browsing — they measure framing, who the model names unprompted), and a live web-SERP layer. 198 × 5 = 990 cells, zero errors.
- Independent validation: DataForSEO's LLM-mentions corpus (~10,000 mentions, ~90% of them Google AI Overviews), plus live Google SERPs and Ads volume/CPC data. Two datasets that don't know about each other.
- Counting is query-coverage — a vendor scores max once per query per surface, so one heavily-cited listicle can't inflate anyone's numbers.
None of this is vibes. Re-run scripts exist.
Finding 1: "Who leads AI citations" depends entirely on which AI you ask
My first headline was: Firecrawl is the #1-cited web-data vendor. True on Perplexity — 25%, comfortably ahead of Apify at 15%.
Then the independent dataset arrived and corrected me. On Google AI Overviews, Firecrawl is #3 at 20%, behind ScrapingBee (31%) and Bright Data (28%) — the two incumbents with a decade of content and backlinks behind them. On ChatGPT-search, Firecrawl is in a three-way tie for #3 at 14%, behind Apify at 21%.
Same vendor. Same month. #1, #3, and #3 — depending on the engine.
The lesson generalizes: any single-surface AI-visibility number is closer to marketing than measurement. Perplexity skews toward what developers currently recommend to each other. AI Overviews skew toward what has ranked on Google for years. If a vendor (or a tool selling you "AI visibility tracking") quotes you one number, ask which engine — the answer is the whole story.
Finding 2: 100% of the branded queries, ~0% of the moment before
Firecrawl wins effectively every branded query in the set — comparisons, "is it worth it," pricing, alternatives. It's named in 143 of 198 queries, the most of any vendor. When someone already knows the name, the engines have nothing but nice things to say.
The problem is the moment before. On unbranded, high-intent questions — the "how do I actually solve this" queries where a buyer doesn't have a shortlist yet — there are 44 clean gaps where a rival gets cited and Firecrawl simply isn't in the room.
Reputation: excellent. Discovery: leaking. These are different problems with different fixes, and almost everyone lumps them into one "brand awareness" line item.
Finding 3: The engines have quietly typecast the product
The 44 gaps aren't random. They cluster into one coherent theme: hard-target scraping — anti-bot evasion, Cloudflare, behind-login, scraping at scale without getting IP-blocked. On those queries, the engines route buyers to proxy specialists (Bright Data, Oxylabs, ScraperAPI) or to DIY Playwright tutorials. Structured e-commerce extraction goes to Apify and Octoparse.
The numbers: Firecrawl's citation share is 12.6% on gap themes vs 23.7% on home turf. The engines have typecast it as "the URL-to-markdown tool for AI agents" and hand everything harder to someone else — regardless of what the product can actually do.
This is the part I'd tattoo on every DevTools positioning doc: LLMs don't read your feature list. They compress you into one job. Whatever sentence the internet repeats about you most often is your product, as far as the engines are concerned.
Finding 4: The AI gap is an SEO gap wearing a trenchcoat
Here's the root cause, and it's almost boring: on 6 of the 9 gap keywords, Firecrawl is absent from Google page 1 entirely. The AI-citation gap mirrors the organic gap nearly one-to-one.
Engines can't cite what doesn't rank. For all the talk about GEO being a new discipline, the supply chain underneath is mostly the old one. Which means the fix is content and ranking — not product, not press releases, and definitely not adding "AI-powered" to the homepage again.
Bonus demand-math: the gap keywords look worthless in a volume report — "best web scraping api" gets 140 searches a month. It also carries a $40 CPC, which is the market telling you exactly what a citation there is worth. Weight AI-visibility work by CPC and intent, not volume, or you'll deprioritize the only queries that pay.
Finding 5: The citation supply chain is not your blog
Across the gap-theme SERPs and the LLM corpus, the sources engines actually quote are Reddit (27 appearances), GitHub (12), Medium (9), YouTube, and a handful of indie listicles — consistently ranked above any vendor's own blog. In the LLM-mentions corpus, YouTube and Reddit are the single most-cited domains on most queries.
You don't out-write this from your own domain. The work is being genuinely useful in the places engines read: answering the Cloudflare question on r/webscraping, shipping the runnable GitHub cookbook, getting onto the listicles that own the $15–40 CPC heads. Unglamorous, compounding, and almost nobody's job description.
What this means if you run a DevTools company
- Measure share-of-citation per engine, not "AI visibility." The per-engine split is the diagnosis. (Perplexity-strong + AIO-weak = community loves you, authority hasn't caught up. The reverse = the opposite problem.)
- Separate recall from discovery. Run branded and unbranded query sets independently. Winning your own name tells you nothing about the moment before.
- Find your typecast sentence. Ask the engines 20 unbranded jobs-to-be-done questions and read what job they give you. If it's narrower than your product, that's your positioning backlog.
- Fix the organic gap first. Page-1 absence and citation absence are the same defect.
- Budget for the supply chain. Reddit, GitHub, YouTube, listicles — third-party surfaces beat your blog for citations, every time we've measured it.
The harness behind this is reusable — 198 queries, 5 engines, independent validation, about a day of work to point at any category. I run it monthly. The first run is the painful one; after that it's a scoreboard.
Firecrawl, if you're reading: you're winning the engine developers trust and trailing the one everyone's mom uses. Defend Perplexity. Go win Google. You know where to find me.
Daria Dovzhikova runs GTM Labs — developer-first GTM for DevTools, security, and AI/ML startups, delivered as a human-in-the-loop agent fleet. The AI-visibility harness from this piece is the same one used on client work. gtm-labs.co
Top comments (1)
What I find most valuable is the distinction between reputation and discovery.
Many companies focus on strengthening their reputation among people who already know them, while the real growth opportunity often lives in unbranded, problem-oriented searches.
The same pattern appears in search, AI citations, communities, and even product adoption.
Winning your own name is not the same as winning the moment before someone knows your name.
That may be the most important insight in the entire analysis.