Voice agent development company shipping production voice AI.

A voice agent development company builds production phone-based AI systems — the speech-to-text, dialog-management, text-to-speech, and telephony layers wired together with evals, guardrails, and observability, not just a demo that works on one test call. The work spans pipeline architecture (Twilio, LiveKit, Deepgram, ElevenLabs), dialog-manager design, tool calling into your CRM or billing system, compliance controls (HIPAA, SOC 2), and the audit logging that makes a voice agent operable in production.

Latency and interruption handling are the core differences. A text chatbot can take a second or two to respond without anyone noticing. A voice agent has to respond inside a sub-1-second turn budget or the caller assumes the line dropped, and it has to handle barge-in — a caller talking over the agent mid-sentence — gracefully instead of ignoring it or restarting. Voice also adds a telephony layer (SIP trunking, DTMF, call transfer) and a text-to-speech layer that a chat interface doesn't need. The dialog-manager logic and tool-calling patterns are otherwise similar to a well-built chat agent.

Sub-1-second p95 turn latency end-to-end — user-stop-speaking to agent-start-speaking — is our standard target and the number we hit on the production insurance reference build. The budget breaks down roughly as 200-300ms for streaming STT first-token, 300-500ms for LLM first-token, and 150-200ms for TTS first-byte. We instrument latency per turn on every production call and ship the numbers to your observability stack. For use cases that need sub-500ms (telehealth triage, sales objection handling), we use realtime APIs and tell you up front what tradeoffs that path involves.

Twilio Programmable Voice for telephony, LiveKit for WebRTC and real-time media, Deepgram Nova-3 or AssemblyAI for streaming speech-to-text, ElevenLabs or Cartesia for text-to-speech, and Claude Sonnet, GPT-4o, or GPT-4o-mini realtime for the dialog manager — benchmarked per task on your data, not picked by vendor loyalty. Self-hosted Llama 3 on vLLM is supported inside your VPC for zero-egress requirements. Eval and observability run on Braintrust, Langfuse, or OpenTelemetry.

Four layers. A refusal layer rejects out-of-scope requests and hands off to a human rather than guessing. Confidence-based escalation routes uncertain turns to a live agent before the caller gets frustrated. An eval harness gates every prompt or model change against a golden set of real call scenarios before it ships. And every turn — transcript, model prompt, tool call, tool result, TTS output — is audit-logged, so a bad call is a forensic review, not a mystery.

Yes, and it's in scope by default rather than a phase-two integration. The dialog manager calls typed tools mid-conversation — opportunity creation, contact update, call-note write, calendar event — validated against your schema with idempotency keys so a network retry never creates a duplicate record. We've shipped this pattern against Salesforce, HubSpot, and custom systems enough times that it's a configuration exercise, not a discovery phase.

Most v1 voice agent engagements at Aiinfox land between $30,000 and $140,000 fixed-price for a focused build — an outbound campaign, an inbound deflection flow, or a HIPAA-aligned healthcare voice agent. Larger multi-quarter engagements with custom fine-tuning, bespoke evals, multilingual voices, and integration into a regulated platform typically reach $180,000 to $320,000. Pricing arrives in writing within 72 hours of the discovery call — no timesheets, no scope-creep invoices.

Six weeks from kickoff to a working v1 is the target. Week one scopes the eval set and call flows. Weeks two through four build the pipeline with typed tool calls and guardrails. Week five is hardening and red-teaming against edge-case calls. Week six ships to real traffic on a controlled ramp. If we miss the six-week mark for reasons on our side, the overrun cost is on us.

Yes. We deploy inside your AWS, Azure, or GCP VPC, to on-prem hardware for regulated workloads, or to our managed cloud. Self-hosted Llama 3 on vLLM is supported for the dialog manager when third-party inference APIs aren't an option, and we'll scope the STT/TTS layer to match — self-hosted Whisper and an open TTS model for the strictest zero-egress engagements.