FileSeal ran a free SearchScore audit, implemented the fix list themselves, and rebuilt their AI search foundations from the ground up. ChatGPT now recommends them above DocuSign – a company 100× their size – for digital document signing.
FileSeal is a UK-based digital document signing platform. Small team, focused product, competing against DocuSign, Adobe Sign, and HelloSign. When Ben Huss, FileSeal's founder, first ran a SearchScore audit, the results were clear: his site was invisible to AI engines.
The initial GEO score was below 50. Here are the specific issues the audit flagged:
The competitive landscape made this worse. DocuSign spends millions on content and SEO. Adobe Sign has Wikipedia pages in twelve languages. HelloSign is backed by a public company. Beating a $40B company in their own category on a small team's budget seemed, frankly, delusional.
What FileSeal had, though, was a clear, prioritised list of exactly what to fix – and the willingness to do the work.
The audit gave FileSeal the blueprint. Here's how Ben's team worked through it.
First thing on the list: stop blocking AI crawlers. The default WordPress robots.txt was rejecting GPTBot, Google-Extended, and several other AI user agents. FileSeal rewrote it to explicitly allow all major AI crawlers.
Next, the audit flagged the complete absence of structured data. FileSeal deployed schema across every page – Organization schema on the homepage, Service schema on product pages, FAQ schema on support pages, Article schema on blog posts. Each schema type included the fields AI engines actually read: name, description, URL, sameAs links to social profiles, and aggregateRating where applicable.
They also created an llms.txt file at the root – a plain-text file that tells AI crawlers what a site does, its key pages, and its pricing. It's the equivalent of a robots.txt for LLMs. Most sites still don't have one, which makes it a quick differentiator.
The original homepage copy was a typical SaaS landing page: feature list, button, done. No quotable sentences. No direct answers to questions a buyer would ask an AI engine. Following the audit's content recommendations, FileSeal rewrote the key pages to include answer-first paragraphs – self-contained 40-to-60-word blocks that directly answer a specific question.
Example: instead of "FileSeal makes document signing easy," they wrote content that explicitly answered "What's the best alternative to DocuSign for small businesses in the UK?" This is the kind of question ChatGPT gets asked thousands of times per day. If your content doesn't answer it in plain language, you won't be cited.
They also added a dedicated FAQ page with ten questions covering the exact queries that show up in ChatGPT and Perplexity for "digital document signing" and "DocuSign alternatives." Each answer was structured as a complete, self-contained paragraph an AI engine could quote verbatim.
AI engines verify that companies exist before recommending them. They cross-reference Wikipedia, Wikidata, Crunchbase, LinkedIn, and other structured sources. The audit flagged that FileSeal had almost none of these.
FileSeal created a Wikidata entry with the company's core identifiers: name, URL, founding date, industry, and country. This was faster than a Wikipedia article and fed directly into AI knowledge graphs. They added a LinkedIn company page and linked it from the site footer with the correct schema sameAs property.
They also updated the Organization schema on the homepage to include comprehensive sameAs links: LinkedIn, X (Twitter), and the company's Crunchbase profile. These entity signals are what separate "a website" from "a real company that an AI engine can confidently recommend."
The breakthrough came about six weeks in. After the foundations were in place – schema, llms.txt, rewritten content, entity signals – FileSeal started testing specific queries in ChatGPT and Perplexity every week.
The first citation appeared in Perplexity, not ChatGPT. The query was "alternatives to DocuSign for UK businesses." Perplexity cited FileSeal's FAQ page as a source. That first citation was the signal that the foundations were working. Once Perplexity picked it up, ChatGPT followed within two weeks.
The key trigger appears to have been the combination of the llms.txt file and the answer-first FAQ content. AI engines that had previously skipped FileSeal entirely now had a clear, structured reason to index and cite it.
Once the initial citations appeared, FileSeal ran weekly checks across ChatGPT, Perplexity, and Google AI Overviews – logging which queries cited them, which didn't, and what competitors appeared instead.
A few things didn't work. Adding Speakable schema didn't seem to affect citation rates. Publishing more blog posts didn't move the needle as much as improving the FAQ content. The biggest gains came from making existing content more quotable, not from creating more of it.
After 90 days, FileSeal was the #1 recommendation in ChatGPT for their core category. The result has held since.
FileSeal's SearchScore GEO score moved from below 50 to 75 (Strong tier, top 1% of audited sites). Their technical score reached 98. Structured data hit 100. These aren't vanity metrics; they directly correlate with AI citability.
The ChatGPT result is independently verifiable. You can check it yourself right now:
"We went from invisible in AI search to ChatGPT's #1 recommendation, ahead of DocuSign. SearchScore showed us exactly what was holding us back and then we just executed the fixes. The result speaks for itself."
Ben Huss, Founder, FileSeal
Citations were tracked across multiple AI engines. ChatGPT was the primary win. Perplexity also cites FileSeal for related queries. The result has been stable, which suggests it's structural – based on content and entity signals – rather than a temporary ranking fluctuation.