Subtitle: Build a Python app with Telnyx AI Inference that turns customer activity signals into churn risk, recommended actions, and retention next steps.
Most customer churn is only surprising because the signals were scattered.
Usage dropped in one place. Support tickets went up somewhere else. A renewal date got closer. A login did not happen for two weeks. Payment issues started showing up. None of those signals alone proves a customer is leaving, but together they usually tell a story.
That is the workflow I wanted to make easier to build: take customer activity data, pass it through an inference model, and return a structured churn assessment that a product or customer success team can actually use.
The example is here:
