AI voice agent development services.
Realtime, telephony, mobile — picked per workload.

Five things, in this order. (1) An audit — 1–2 weeks, $3K — that picks the voice workflow worth shipping, the architecture (Realtime · chained · unified vendor), and the telephony stack (Twilio · Telnyx · LiveKit · Daily). (2) A pilot — 5–7 weeks, $10–25K fixed — that ships one voice ai agent end-to-end on your chosen stack with the full anatomy wired (telephony → STT → LLM → TTS → guardrails → log) and gated by eval data, not a demo. (3) The 4-metric eval pass — intent-match accuracy, hallucination per 100 calls, barge-in latency, mean-time-to-action — run on a held-out set from your real call recordings. (4) Pilot rollout 10% → 50% → 100% gated by metric movement, with a walk-away point. (5) Continuous tier from $5K/month if you want to ship the next workflow or tune the live one. We're a voice ai company that publishes pricing and architecture choices openly — most listicle SERPs hide both.

Neither wins universally. Here's the honest decision. <strong>OpenAI Realtime API</strong> wins when first-token latency is the dominant requirement (in-app voice copilots, premium telephony, anything where conversational feel breaks above 800ms) and you can pay ~$0.06/min in production. <strong>Chained STT+LLM+TTS</strong> wins on cost (10–30× cheaper at scale), model flexibility (swap Claude ⇄ GPT per turn), and multilingual coverage — the right pick for high-volume outbound, multilingual support, and any workflow where per-minute economics dominate. <strong>Deepgram Voice Agent</strong> (unified vendor) wins when you want the fastest time-to-pilot with a single SLA and don't need to swap models. Our audit picks per workload, not per ideology — the openai realtime api development path is the right answer about a third of the time in our shipped pilots.

Honest numbers from shipped pilots. <strong>OpenAI Realtime API</strong>: ~$0.06/min all-in (model + audio in/out + telephony). <strong>Chained Whisper + GPT-5 + ElevenLabs Multilingual</strong>: ~$0.18/min naively, down to $0.05/min after the standard optimization pass (route to mini models per turn, cache the system prompt, idle-trim silence, swap Whisper for Deepgram Nova-3). <strong>Chained Deepgram + GPT-5-mini + Cartesia</strong>: ~$0.005–$0.02/min — the cheapest stack we ship, used on high-volume outbound. <strong>Deepgram Voice Agent</strong>: ~$0.04/min bundled. Per-minute cost is published in every audit deliverable in $/min terms, not vague "depends on usage" ranges. The waterfall above shows the optimization pass we run on every pilot.

Yes — that's the modern definition. A voice ai agent that just answers FAQs is a 2018 IVR with better TTS. Agentic ai development for voice means the agent can call your APIs (refund a charge, book an appointment, look up an order), retrieve from your RAG corpus per turn (catalog, policy docs, ticket history), make multi-step decisions (verify identity → check policy → execute action), and hand off to a human with a structured summary when policy says it should. We build voice agents on the same agentic-workflows stack as our <a href="/services/ai-agent-development/">AI agent development</a> work — the only difference is the interface is voice, not chat. Most agentic process automation projects benefit from a voice surface for a subset of the workflow; we'll tell you which during the audit.

Four metrics, evaluated on a held-out set drawn from your real call recordings. (1) <strong>Intent-match accuracy</strong> — does the agent classify and respond to each call type correctly? Target >85% on tier-1 intents. (2) <strong>Hallucination rate per 100 calls</strong> — how often does the agent invent a policy, balance, or feature that doesn't exist? Target <2 per 100 calls before rollout. (3) <strong>Barge-in latency</strong> — when the caller interrupts, how fast does the agent stop? Target <300ms p50, <500ms p95. (4) <strong>Mean-time-to-action (MTTA)</strong> — from call start to the moment the agent does the thing the caller wanted. Baseline measured during the audit, target movement defined per workflow. We sample 200 calls/week minimum during pilot for human review. Listicle competitors don't publish eval methodology because they don't run it — this is the disqualifying signal when shortlisting any voice agent platform vendor.

Five honest scenarios where we'll tell you not to build a voice agent. (1) <strong>Low-bandwidth or noisy environments</strong> — factory floors, transit, anywhere the STT layer can't get clean audio reliably. Text or a structured form wins. (2) <strong>Regulated read-backs</strong> — when the caller must read or confirm specific text verbatim (financial disclosures, prescription instructions), voice is slower and error-prone vs a screen with a checkbox. (3) <strong>Complex UI flows</strong> — flight selection, multi-product configuration, anything that needs a comparison grid. Voice forces serial enumeration of options. (4) <strong>Cost-sensitive workflows at massive scale</strong> — if your per-interaction budget is sub-$0.01 and volume is in the tens of millions, even the cheapest chained stack will outprice a chat or async channel. (5) <strong>Users who don't want to talk</strong> — most under-35 cohorts in B2C SaaS, anyone in an open-plan office, anyone who's already on a video call. The right voice strategy is often a voice surface for some of your traffic, not all of it.

Yes — and that's a meaningful differentiator. Most voice ai company SERPs are telephony-centric (Vapi, Retell, Synthflow all start at a phone number). We ship voice copilots inside iOS, Android, Flutter, and web apps via LiveKit Agents SDK or a direct Realtime-API WebRTC bridge. We wrote the UI kit that a lot of Flutter screens run on, so the voice surface ships with proper mic-permission UX, on-screen visual feedback during turn-taking, and interrupt handling that doesn't fight the OS. See the food-delivery capability pattern above for a shipped example — sub-600ms TTFT measured on real devices, +18% order-completion lift on the voice cohort. For mobile-specific work, the cross-link is <a href="/flutter-app-development-company/">Flutter app development</a>.

Honest answer — sometimes yes. <strong>Buy a platform</strong> (Vapi, Retell, Synthflow, Bland, Air) when your workflow is standard (inbound deflection, outbound reminders, appointment scheduling), your call volume is moderate (under ~100K minutes/month), and you don't have a unique RAG corpus or unusual tool surface. You'll be live in days, not weeks, and the per-minute cost premium (typically 30–60% above a custom build) is worth it for the time-to-value. <strong>Build custom</strong> when you need (a) deep RAG over a proprietary corpus, (b) tool-use against internal systems no platform integrates with, (c) a mobile-native or in-app voice surface, (d) per-minute economics that platform pricing can't hit at your volume, or (e) a vendor-agnostic stack you can swap as the model landscape moves. Our audit will tell you which side you're on — we've recommended "go buy Vapi" to about a quarter of voice audit clients and that's the right answer for them. <a href="/services/ai-integration-services/">AI integration services</a> often picks up the platform integration work when the answer is buy.