Voice AI

Voice AI Agent Development

Voice agents that handle interruption like a human would — barge-in, backchannel audio, and one agent core across every channel.

Most voice AI demos work in a quiet room with one polite caller. The moment a real user interrupts mid-sentence, asks a follow-up before the agent finishes talking, or moves from a chat thread to an actual phone call, the illusion breaks — the agent talks over them, stalls mid-answer while retrieval runs, or starts over as if the conversation never happened.

We build AI-assisted, voice-enabled knowledge assistants and retrieval systems. Streaming STT and TTS run with real barge-in: the moment a caller speaks, in-flight speech and generation cancel against a ~100ms target, instead of finishing the current sentence first. On turns that need retrieval, backchannel audio masks the gap so the agent sounds like it's listening rather than stalling. And it's the same agent regardless of channel — one core carries the retrieval, persona, guardrails, and memory across text, voice-note, and live-call transports; only the transport and turn-taking logic change underneath it.

Transport is a swappable seam, not a foundation poured in concrete: we start on WebSocket, which needs no media server, and WebRTC is a bounded swap when browser-based calling is needed later — a transport change, not a rewrite of the agent underneath it. Cost is architected in from the start, not bolted on after a spend spike — per-tenant concurrent-call caps, a hard spend cap, a kill switch, and session timeouts, the same discipline we apply to every other engineering guarantee.

The problem we solve

Voice AI pilots that work in a demo and break on a real call — talking over an interrupting caller, stalling mid-answer while retrieval runs, or losing the thread the moment a conversation moves from chat to a live call.

What you get

Streaming STT/TTS with real barge-in — in-flight speech and generation cancel against a ~100ms target the moment the caller speaks
Backchannel audio masks retrieval latency on grounded turns, so the agent sounds like it's listening, not stalling
One agent core across text, voice-note, and live-call transports — the same retrieval, persona, guardrails, and memory; only transport and turn-taking differ
Transport built as a swappable seam — WebSocket first with no media server required, WebRTC a bounded swap when browser calling is needed
Cost architected in from day one — per-tenant concurrent-call caps, a spend cap, a kill switch, and session timeouts
Provider-agnostic model and STT/TTS layer — swap vendors without rewriting the pipeline

What we deliver

  • Working voice agent deployed to your infrastructure
  • Cost controls configured — concurrency caps, spend cap, kill switch, session timeouts
  • Numbered ADRs recording every architectural decision and deviation
  • Security review across every surface before handover
  • Runbook and 30-day warranty

Who it's for

  • Founders who need a voice agent that survives a real caller interrupting it, not a chatbot with text-to-speech bolted on
  • Teams with an existing text agent who need the same retrieval, guardrails, and memory on a live phone call
  • Companies whose voice pilot broke on interruption handling or ran up cost nobody had capped

Indicative investment

Transparent ranges so you can plan. Final scope and quote are confirmed on your scoping call.

PackageFromTimeline
AI MVP Build$5,0004 weeks
Custom Platform$13,0006–10 weeks

Frequently asked questions

How is this different from a chatbot with text-to-speech added on?

It's a different architecture, not a different skin. One agent core carries the retrieval, persona, guardrails, and memory across text, voice-note, and live-call transports — the same agent whether a user is typing or calling. Only the transport and turn-taking logic change underneath it.

What happens when the caller talks over the agent?

The in-flight speech and generation cancel — that's real barge-in, working against a ~100ms design target, not a fixed pause the caller has to wait out. On turns that need retrieval, backchannel audio covers the gap so the pause doesn't read as the agent going silent.

Do we need WebRTC or a media server to get started?

No. We start on WebSocket, which needs no media server. WebRTC is a bounded swap if you later need in-browser calling — a transport change, not a rebuild of the agent underneath it.

How do you stop a busy day from blowing through our budget?

Cost is architected in, not monitored after the fact: per-tenant concurrent-call caps, a hard spend cap, a kill switch, and session timeouts are configured before launch, not added after an invoice surprises you.

How fast can you ship a voice agent?

An AI MVP Build starts at $5,000 and runs four weeks for a working voice agent in front of users. A custom, multi-tenant voice platform starts at $13,000 and runs six to ten weeks.

Ready to scope your voice ai project?

Start with a scoping call. You leave knowing the timeline, the fixed price, and whether it's a fit — before you commit to anything.

Book a scoping call