Detect scam calls using Go with the LLM Gateway and Twilio

A tutorial demonstrates how to build a roughly 250-line Go service that uses Twilio Media Streams to fork live phone-call audio, AssemblyAI’s streaming speech-to-text API to transcribe it in real time, and the AssemblyAI LLM Gateway to classify the completed transcript as a scam, suspicious, or legitimate call. It explains configuring a public ngrok endpoint and Twilio webhook, preserving Twilio’s native 8 kHz mu-law audio format, and batching five 20-millisecond audio frames into 100-millisecond chunks because the transcription API rejects shorter messages. The application maintains a separate transcript buffer and WebSocket session for each call, stores only finalized formatted speech turns, flushes remaining audio and waits for transcription termination so the final utterance is not lost, then sends the transcript to a configurable LLM with a prompt designed to identify fraud indicators such as urgency, impersonation, requests for codes or payments, and secrecy. It also includes JSON-output fallback handling for models without structured-response support, troubleshooting guidance for common WebSocket, audio-format, and tunnel failures, and suggestions for extending the prototype with mid-call alerts, speaker labels, persistent audit records, or automated intervention.

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