Competing for the answer in a category framed by giants.
Chanty is the team-collaboration platform used by 75,000+ companies — messaging, video calls and task management, positioned squarely against Slack and Microsoft Teams. Which is exactly the problem when a buyer asks an AI "what's the best team chat app": the answer is framed by the giants' story. This is how Chanty uses Mavel to change whose frame the models tell.
Mentioned sometimes. Recommended rarely.
Team chat is one of the most brutally consolidated categories in AI answers. Ask any engine for "the best team communication tool" and the answer is built on a frame the market leaders wrote: Slack for startups, Teams for Microsoft shops — and everyone else as a footnote.
For a challenger like Chanty, the classic playbook — more listicle placements, more mentions — moves the mention count, not the recommendation. The models already knew Chanty existed. They just told the wrong story about it: an also-ran in Slack's frame, instead of the simpler, more affordable platform with built-in task management that wins its actual segment.
Fix the frame, not the mention count.
- Map the prompt universe. Mavel scoped the real questions buyers ask across the category — comparison prompts, use-case prompts, "alternatives" prompts — and tracked them daily across all 8 engines.
- Read the adopted frame. The perception-gap analysis showed how each engine actually describes Chanty versus how Chanty positions itself — where the story was outdated, generic, or borrowed from a competitor's framing.
- Trace the sources. Answer citations revealed which sources each engine leans on for the category — and which of them carried the wrong story.
- Run the action loop. Prioritised actions — what to publish, what to correct at the source, where to build consensus — with the effect measured engine by engine as models re-crawled.
Your category has a frame too.
Run the free GEO report and see whose story the models tell about you — before your next quarter is framed by someone else.