A remote-team project-management brand noticed its citation share was thin on one specific query cluster: async standups. Source flagged the gap, Solve drafted a landing page against it same day, and within a few weeks the brand went from uncited to the top-cited answer on that query across two of three platforms tracked.
The signal
Source’s LLM API Response Collector ran the brand’s standard query set across Claude, Gemini, and ChatGPT. One query — “async standups for remote teams” — came back with zero citations for the brand and heavy citations for two direct competitors.
The draft
- Solve pulled the signal and the competitor language cited in the same responses
- It drafted a landing page answer-first: “Skip the Meeting. Keep the Standup.” as the h1, with the core claim in the first two sentences
- The draft linked out to a comparison page and a “how it works” explainer — both cluster pieces already in the content graph
The result
| Metric | Before | After 3 weeks |
|---|---|---|
| Citations on this query | 0 | 12 |
| Brand visibility (this cluster) | 0% | 41% |
| Ranking vs. named competitors | Not cited | #1 |
Full instrumentation for a case study like this lives in Operate — visibility and ranking, tracked automatically, not stitched together after the fact.