Building Assist: zero to voice agent

What it actually takes to ship an AI product

Assist started with a simple observation: most businesses still answer the phone manually, and most of those calls are repetitive. Appointment confirmations, opening hours, routing questions. Work that doesn't need a human, but still eats human time.

So we built an AI telephonist. A voice agent that picks up, understands the caller's intent, and handles it — or routes it to the right person when it genuinely needs one.

Getting from idea to production was not a straight line. The first challenge was latency. Voice is unforgiving in a way that chat isn't. If a response takes three seconds, the caller thinks the line is dead. We had to be obsessive about pipeline speed: transcription, inference, text-to-speech, all of it.

The second challenge was handling the unexpected. Real callers don't follow scripts. They ramble, they change topic mid-sentence, they have accents, they have bad microphones. The agent had to be robust to all of it while still sounding natural.

What I learned building Assist: shipping an AI product is mostly an engineering problem, not an AI problem. The models are good. The hard work is the plumbing around them — the error handling, the fallbacks, the logging, the integrations with whatever system the client already uses.

If you're thinking about building something in this space, start narrower than you think you should. Pick one industry, one call type, and nail it before you generalize.