Case Study: How a SaaS Startup Tripled AI Citations in 90 Days

The starting point

The company is a B2B SaaS startup in the project management space. Twelve employees, $1.2M ARR, growing 8% month-over-month. Their organic traffic was healthy (40K monthly visits from Google) but they were almost invisible in AI answers.

We ran the AI Visibility Checker on their domain across 20 category prompts (e.g., "best project management tool for remote teams", "alternative to Asana for small agencies"). Their citation share was 8%. For context, the category leader was at 41%, and three competitors were between 20% and 30%.

The startup had good content — a 60-post blog, a polished docs site, and a few comparison pages. The problem was not content quality. The problem was citation-worthiness. Their pages were well-written but structured poorly for LLM extraction.

The 90-day plan

We broke the 90 days into three 30-day phases. Phase 1 was technical: crawlability, schema, llms.txt. Phase 2 was content structure: rewriting the top 20 pages for citation-worthiness. Phase 3 was entity authority: off-site corroboration and relationship building.

Each phase had a single owner and a weekly check-in. The total time investment was about 30 hours per week across the team, with one full-time content person doing most of the rewriting.

Phase 1: Technical foundations (days 1-30)

The first 30 days focused on the technical fixes that unlock AI citations. None of these fixes are glamorous, but they are prerequisites for everything else.

• Day 1-7: Ran the Robots.txt Tester and discovered GPTBot was blocked by a Cloudflare rule. Allowed GPTBot, ClaudeBot, and PerplexityBot explicitly.
• Day 8-14: Generated and shipped an llms.txt file listing the 15 most important pages. Used the free llms.txt Generator.
• Day 15-21: Added FAQ schema to the top 10 blog posts and 3 product pages. Used the free Schema Generator. Validated with Google Rich Results test.
• Day 22-30: Added Organization and Breadcrumb schema site-wide. Added visible "last updated" dates to all blog posts and refreshed the top 20 posts.

The Phase 1 result

After 30 days, citation share moved from 8% to 17% — a 9-point lift. The biggest single contributor was FAQ schema, which alone added 5 points. The llms.txt file and last-updated dates added 4 points combined. Allowing GPTBot was a prerequisite; without it, none of the other fixes would have mattered.

The 17% was measured on day 30, but the trend was still rising. The fixes take time to propagate as LLMs re-index. By day 45, citation share had climbed to 19% with no additional work — the Phase 1 fixes were still compounding.

Phase 2: Content rewrites (days 31-60)

Phase 2 was the heaviest lift. We rewrote the top 20 pages using the inverted pyramid structure described in our content strategy guide. Each page got: a direct answer in paragraph one, a 3-5 item key takeaways list, 5-7 sections with question-shaped headings, and a sources list at the bottom.

The rewrites were not about adding content. Most pages got shorter. The goal was extractability, not depth. We cut intros, removed anecdotes, and moved context below the answer.

The rewrites took 4 weeks of one content person's full-time effort. We prioritized by search volume + existing AI citation share, so the highest-traffic pages got rewritten first.

The Phase 2 result

After 60 days, citation share moved from 17% to 24% — another 7-point lift. The rewrites contributed 5 of those points; the remaining 2 came from continued compounding of Phase 1 fixes.

The biggest surprise was how fast the rewrites affected citations. Unlike schema, which takes weeks to propagate, content rewrites showed up in AI citations within 7-10 days. LLMs re-fetch popular pages frequently, and the new structure made them immediately more citation-worthy.

Phase 3: Entity authority (days 61-90)

Phase 3 was the hardest to measure but produced the most durable lift. We focused on three things: getting the company into Wikidata, getting cited in two industry publications, and co-publishing a benchmark report with a recognized industry analyst.

The Wikidata entry was the easiest. The company met the notability threshold (multiple press mentions, a Crunchbase profile, a Wikipedia mention in a related article). We created the entry, added 8 statements with references, and within 3 weeks it was stable.

The two publication citations required outreach. We pitched a guest post to one publication and got quoted in a roundup article in another. Each took about 10 hours of effort from the founder.

The benchmark report was the heaviest lift. We surveyed 200 customers about their project management usage, published the results, and sent it to 5 industry analysts. One picked it up and cited it in a report.

The final result and what we would do differently

After 90 days, citation share was 27% — up from 8% at the start. The Phase 3 lift was 3 points, smaller than Phases 1 and 2, but it was the most durable. Six months later, citation share had climbed to 34% with no additional work — the entity authority fixes were still compounding.

If we did it again, we would start Phase 3 in parallel with Phase 1. The Wikidata entry and publication outreach take weeks of waiting, so there is no reason not to start them on day 1. We would also rewrite the top 20 pages before adding FAQ schema — the schema is more impactful when it sits on citation-worthy content.

The single highest-leverage action was unblocking GPTBot. It is a 5-minute fix that the team had been putting off for months because they assumed Cloudflare was handling it. If you take one thing from this case study, run the free Robots.txt Tester today.