Use case
Social listening that returns data, not dashboards
Audience conversation from Instagram, TikTok, Reddit, X, and YouTube, clustered into themes and delivered as JSON your scripts and agents can use.
The short answer
Waldo does social listening by tracking audience conversation across Instagram, TikTok, Reddit, X, and YouTube, clustering the verbatims into themes, and returning them through an API and MCP server. Tracked audiences refresh every 24 hours; Discover endpoints query the platforms live. You get the actual quotes, with links back to every source.
The job to be done
Listening is supposed to answer one question: what are real people saying about the brand, the category, and the moment, right now. The tools built for it answer a different one. Dashboards compress sentiment into a line chart, boolean queries miss how people actually phrase things, and the verbatims that matter sit three exports and a pivot table away from the person who needs them.
The pain compounds when listening has to feed something downstream: a weekly comms report, an agent that drafts responses, a brief that quotes customers in their own words. A dashboard screenshot cannot feed a cron job. The listening layer needs to be queryable, current, and attached to its receipts, which is a data problem, not a charting problem.
How Waldo does it
Activate what you need to hear
Tell Waldo which brands and audiences to track. From then on the conversation around them refreshes on a 24-hour cycle, and everything captured is stored permanently, text, images, video, and metrics, even if the original post gets deleted.
Pull clustered verbatims
GET /audiences/{id}/conversations returns the conversation grouped into themes with the raw quotes attached. Per call you choose raw source material when you need the language, or scored analysis when you need the shape of it.
Go live for anything untracked
Discover endpoints query the platforms at request time, so a question about a community you have never tracked gets a fresh answer instead of an empty chart.
Feed it downstream
The same feed reads over MCP inside Claude, ChatGPT, or Copilot, and over the API for crons. A morning job can diff the themes, pull the new quotes, and post the shifts to Slack before standup.
The endpoints
/audiences/{id}/conversationsAudience verbatims clustered into themes, refreshed dailyMCP: audience_insightsThe same conversation data inside Claude, ChatGPT, or CopilotDiscover endpointsLive platform queries for anything not yet trackedEnrichment endpointsPost, profile, and transcript lookups at request timeQuestions teams ask
Which platforms does listening cover?
Tracked audiences span Instagram, TikTok, Reddit, X, YouTube, Meta, and LinkedIn, plus the open web. Discover and enrichment endpoints hit the underlying platforms live for anything outside the tracked set.
What happens when a post is deleted?
It stays in your record. Everything Waldo captures is stored permanently, text, images, video, and metrics, so the quote you flagged in March is still there for the June report even if the author removed it.
How is this different from a listening dashboard?
Dashboards render conclusions; Waldo returns the data underneath them. Every output links to its sources, the same queries run over API and MCP with the same auth, and nothing you build is trapped in a vendor’s UI.
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