The cancellation is decided long before it is clicked

Acquiring a new customer costs 5–25× more than retaining an existing one — every founder knows the stat. The harder problem is that by the time you SEE churn (usage drops, the renewal call goes south, the cancellation email lands), the customer made the decision weeks earlier. The decision happened in a conversation — a Slack thread, a support email, a public post on Reddit asking for alternatives. Detection has to move upstream of the metric.

Most churn-detection tools (ChurnZero, Vitally, Catalyst) score usage data — login frequency, feature adoption, ticket volume. That's a lagging indicator. By the time usage drops, the decision was already made. Conversation data — what customers are saying about you, in public and in private — is the leading indicator. But conversations don't fit neatly into a dashboard, which is why most tools ignore them.

The same engine reads churn signals, but how you describe your product decides whether it works. The judges ask one question: is this author a buyer of the product in front of them. Describe a tool that flags at-risk accounts and a leaving customer is not a buyer of it, because that tool is bought by your success team. Describe what the unhappy customer actually wants and the same messages come through. Same multi-stage filter (multilingual semantic scoring → urgency and specificity → sarcasm detection), same 3-judge swarm, same RL learning loop. Swap the anchor sentences from "I need a tool that..." to "I'm looking for an alternative to [your product]" and you've got an early-warning radar for churn. Configure a product profile in 10 minutes.

Two surfaces, one engine. The scout reads the public feeds you choose for churn signals: customers asking for alternatives, frustrated comments under your launch, complaints in industry forums. For private channels, signalpipe_score_signal accepts arbitrary text your AI agent reads — a Slack message from a customer saying the tool isn't working, an email asking about contract terms, a support ticket with frustrated tone. We don't connect to your channels; your agent does. We just score what it shows us.

Sample churn-risk anchor sentences (paste these when you create your product profile): "I'm looking for an alternative to [your product]". "Anyone else frustrated with [your product] pricing?". "Cancelling our [your product] subscription". "Thinking about switching from [your product] to something cheaper". "[Your product] is missing X feature we actually need". "Has anyone moved off [your product] recently?". The bracketed token is where you put your own product name.

Write the profile as a save desk, not as a detector. A product described as 'the team that fixes billing problems, matches a competitor price and arranges downgrades instead of cancellations', for 'existing customers who are frustrated, comparing alternatives or asking about leaving', catches the messages that matter: someone comparing your price to a competitor, someone on their third failed export, someone asking what the notice period is. Described instead as a tool that flags at-risk accounts, those same messages are read as not-a-buyer and dropped.

What it will not catch is silence. A customer who quietly stops using a feature, never complains and never writes in leaves no message to score. This reads conversations, so it sees the customers who say something; usage data still tells you about the ones who say nothing.