Build a Customer Health Agent That Spots Fires Before Humans Do
We’re building a true customer health agent that cross-references Slack, Linear, meeting transcripts, lifecycle data, delivery performance, response times, and tone shifts to spot fires days before a human would.
In under an hour, we’ll assemble a real multi-source agent that pulls from Slack, Linear, meeting transcripts, and historical account data to produce a unified weekly health report. The agent will:
• Analyze Slack, email, and message patterns across channels
• Pull project-health signals from Linear: velocity, blockers, escalations, delivery risk
• Read meeting transcripts to extract tone, urgency, and emotional cues
• Combine every signal into sentiment, engagement, trending, and churn probability
• Flag risks early, with real evidence
• Generate a clean, executive-ready weekly digest: health scores, risk indicators, project signals, relationship quality, predictive analytics, and recommended next steps
The outcome: a fully automated brief that tells you who’s healthy, who’s trending red, and exactly what to do next—without a CSM stitching together data from five tools.
Join Alex and Arman for this Human in the Loop speed build.