Voice AI & Realtime Agents
Realtime voice agents that listen, understand, and respond — grounded in your data and integrated with your systems.
Natural voice interfaces with sub-second latency
Voice changes the UX shape of AI. We build realtime voice experiences — intake flows, coaching conversations, support agents, drive-through ordering — that combine ASR, LLM reasoning, and TTS into a pipeline tuned for latency, interruption handling, and grounded responses.
Outcomes
Our approach.
Pick the transport
Twilio for phone, LiveKit or WebRTC for in-app, kiosk SDKs for physical. Transport decides the latency floor.
ASR + LLM + TTS pipeline
Streaming ASR, realtime LLM with barge-in, low-latency TTS. Every stage measured in ms, tuned together.
Ground the conversation
Same retrieval and tool-call patterns as text chat — just with tighter latency budgets and audio-aware state.
Observability & QA
Every call recorded, transcribed, scored. Failure modes (missed intents, silence, interruptions) surface as first-class metrics.
What you get.
Production-shaped, from day one.
// Realtime voice agent with tool calls + grounding
const agent = voice.create({
asr: "deepgram-nova-3",
llm: "gpt-realtime",
tts: "elevenlabs:rachel",
tools: [ehr.lookup, scheduling.book],
retrievers: ["patient_notes"],
latencyBudgetMs: 800,
bargeIn: true,
})
agent.on("turn", (turn) => trace.log(turn))
await agent.connect(twilioStream)A proven shape for this solution.
We adapt it to your cloud, data, and compliance requirements. Nothing here is boilerplate — every layer is justified by the numbers.
Where this shows up.
- Healthcare voice intake with structured capture
- Voice-based coaching check-ins and session notes
- Outbound and inbound support voice agents
- Drive-through, kiosk, and in-store voice ordering
What we use.
We’re not religious about tools. We pick what fits your constraints and team.
Shipped examples.
Healthcare patient data mapping & health information chat
Mapped and normalized patient data to power a grounded chat experience where patients can ask questions about their own health information — safely.
Coach session intelligence & program updates
Turned coaching session notes and history into structured program updates, progress summaries, and next-action recommendations.
What teams usually ask.
How low can latency go?
+
Well-tuned pipelines hit 500–800ms end-to-end on realtime APIs. We measure each stage — network, ASR, LLM, TTS — and optimize the bottleneck.
Can voice agents do tool calls mid-conversation?
+
Yes — lookup, booking, updates, handoff. The hard part is doing them without breaking the flow, which means streaming partial responses while tools execute.
What about accents, noise, and domain vocabulary?
+
Pick ASR per domain, add custom vocabulary / keyword boosting, and validate on your users' real audio — not clean benchmarks.
Related solutions.
Conversational AI & Chat Lookup
Production-grade chat systems that answer from your sources with citations, guardrails, and session memory.
Agents & Workflow Automation
Agentic workflows that read, write, and act across your existing tools — with human-in-the-loop where it matters.
Conversation & History Intelligence
Turn chat transcripts, call logs, and session history into structured insight, alerts, and product feedback.
Ready to accelerate your tech growth?
Schedule your free consultation today and let's discuss how we can help your business scale efficiently.
