Stop Building Voice Agents from Scratch
Stitching together ASR, LLM, and TTS APIs sounds straightforward. In practice, it's months of edge-case engineering that a platform handles on day one.
The pitch for building your own voice agent is compelling: call an ASR API, pipe the text to an LLM, send the response to TTS, stream the audio back. Three API calls. A weekend project. Then you try it with a real caller and discover what the remaining 90% of the work looks like.
The iceberg of real-time voice
- Interruption handling — the caller talks over the agent. Do you stop generating? Keep going? The answer depends on whether it's a real interruption or a backchannel ('uh-huh'). This alone is a research problem.
- Turn detection — when has the caller actually finished speaking? VAD catches silence but misses pauses mid-thought. Endpointing helps but adds latency. Getting this wrong means agents that talk over callers or wait too long.
- Echo cancellation — the caller's device plays the agent's voice, which the microphone picks up and feeds back to ASR. Without proper echo handling, the agent hears itself and creates a feedback loop.
- Telephony edge cases — DTMF tones, call transfers, hold music, voicemail detection, conference calling, SIP protocol variations across carriers.
- Latency optimization — streaming responses, connection pooling, model warm-up, regional deployment. Each shaves 50–100ms, and you need all of them.
What to build vs what to buy
Build your conversation logic, knowledge base, and integrations — these are your differentiators. Buy the real-time audio infrastructure, turn detection, interruption handling, and telephony management — these are commodity problems that have been solved. A platform with a visual canvas for conversation design gives your team velocity on the parts that matter while handling the infrastructure complexity that doesn't make your agent uniquely valuable.
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