A Project Management SaaS Out-Cites Three Category Leaders Inside ChatGPT's Recommendations
When buyers started asking ChatGPT and Perplexity which tool to use for distributed teams, this platform wasn't part of the answer. Six months later, it was the answer most often given.
The Challenge
The product itself wasn't the problem. Internal usage data showed strong retention, a healthy NPS score, and a sales team that closed deals reliably once they got a demo on the calendar. The problem was getting to that demo in the first place.
By early 2025, a meaningful share of the platform's prospective buyers had changed how they shopped for software. Instead of typing "project management software" into Google and wading through ten listicles, they were asking ChatGPT or Perplexity a much more specific question - something closer to "what's the best project management tool for a 30-person remote team that needs async standups." And the answer they got back, consistently, named three other platforms. Two of them had fewer users than this one.
The root causes were unglamorous. The comparison pages on the site were built around feature checklists rather than the workflows buyers actually cared about. Product schema markup was inconsistent across plan pages, which made it harder for AI crawlers to confirm basic facts like pricing tiers and integrations. And the platform had no original research that gave outside publications, or AI models, a reason to cite it as an authority rather than just another vendor with an opinion about itself.
The Approach
Before writing a word of new content, we audited how clearly the platform's own site explained itself to machines. That meant correcting SoftwareApplication schema across every pricing and feature page, fixing canonical conflicts between marketing pages and help-center articles answering the same questions, and adding structured FAQ markup to the twelve pages buyers compared most often before purchase.
We rebuilt the platform's three highest-traffic comparison pages from scratch, replacing static feature tables with real scenarios: how a distributed team runs async standups inside the tool, how client reporting actually gets handled, what a sprint handoff between time zones looks like in practice. AI engines tend to cite whichever source answers the underlying question most concretely, not the one with the longest feature list.
We fielded a proprietary survey of 412 remote team leads on async work habits, built a short methodology page explaining exactly how the data was collected, and pitched the findings to seven publications that AI models already treat as credible sources in this category. Three picked it up, generating links and citations the platform couldn't have earned through outreach alone.
From week eight onward, we ran a fixed set of 34 buyer-intent prompts against ChatGPT, Perplexity, and Google AI Overviews every week, logging which competitor was getting cited and why. When a rival's answer pulled from a specific blog post or help doc, we could see it and respond directly instead of guessing at what AI models found credible.
The Results
Six months in, the platform had gone from absent to dominant in the exact moment that matters most - the point where a buyer asks an AI assistant which tool to actually use. Across the 34 tracked prompts, the brand's AI citation share climbed from effectively zero to 61%, and it now ranks first in head-to-head AI Overview comparisons against 34 named competitors.
The downstream effect showed up exactly where the sales team needed it. Organic demo requests grew 212% over the engagement period, with the sharpest increase concentrated in the two months following the survey's publication. By month six, this category of buyer-intent traffic accounted for a larger share of new trial signups than either paid search or outbound.
We'd been winning on product for years. We just weren't winning the moment someone asks an AI assistant which tool to trust. That's the gap this closed.
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