Voice agents fail in ways chat never does: latency users can feel, interruptions mid-sentence, transcription errors compounding into wrong answers, and per-minute costs that scale with usage. I've solved each of these in a live product — Yuvan, a voice-first AI math tutor used by paying schools — and I bring those patterns to your build.
What You Can Hire Me to Build
Production voice AI development — freelance, contract, or embedded
Realtime voice agents
OpenAI Realtime API over WebRTC — full-duplex conversation with voice activity detection and natural barge-in.
STT / TTS pipelines
Whisper, ElevenLabs, and pipeline stacks where you need control over voices, languages, or cost per minute.
Grounded voice + RAG
Voice agents that answer from your data — retrieval, semantic caching, and verification behind the voice.
Latency engineering
Latency budgets per pipeline stage, streaming everywhere, and the trade-offs that get you under 1.5s p95.
Cost optimization
Semantic FAQ caching, model right-sizing per turn, and usage-based cost modeling before you scale.
Stack selection
OpenAI Realtime vs Whisper+ElevenLabs vs Gemini Live — benchmarked on your workload, decided with data.
Engagement Models
Voice Agent Build
A production voice agent, end-to-end.
- Transport, pipeline, and grounding
- Latency and cost engineered in
- Evals and observability included
Stack Selection Sprint
Prototype and benchmark before you commit.
- 2–3 candidate stacks prototyped
- Latency / cost / quality benchmarks
- A data-backed recommendation
Latency / Cost Rescue
Your voice agent is too slow or too expensive.
- Stage-by-stage measurement
- Transport and caching fixes
- Model right-sizing per turn
Yuvan — my voice-first AI math tutor — runs OpenAI Realtime over WebRTC with sub-1.5s p95 latency, a math-verifier safety net, semantic FAQ caching, and multi-tenant RLS, live with 5 paying CBSE schools. Before choosing that stack I benchmarked OpenAI Realtime, Whisper+ElevenLabs, Whisper+Coqui, and Gemini Live on real student traffic.
Frequently Asked Questions
What does voice AI development involve?
Real-time speech in and out (OpenAI Realtime, or STT+LLM+TTS pipelines), WebRTC transport, voice activity detection, barge-in handling, latency budgets, and RAG grounding so the agent answers from your data.
Which voice AI stack should we use?
Depends on latency, cost, and control needs. I've benchmarked OpenAI Realtime, Whisper+ElevenLabs, Whisper+Coqui, and Gemini Live on real workloads — you get a data-backed decision, not a guess.
What latency can a production voice agent achieve?
Sub-1.5s p95 with OpenAI Realtime over WebRTC and careful pipeline design — that's what Yuvan runs at with real users.
Can you fix our voice agent's latency or cost?
Yes — WebRTC transport, semantic caching, model right-sizing, tighter VAD, and streaming. Optimization starts with measurement, then attacks the biggest line items.
Do you work with clients in the US and Europe?
Yes — remote-first, worldwide, with substantial US and European time-zone overlap.
Related Services
Hire an Agentic AI Developer →
The full agent system behind the voice.
AI Consulting →
Architecture reviews, Fractional CTO, advisory.
RAG Training →
Grounding and retrieval quality, in depth.
Agentic AI Training →
Prefer to build in-house? I'll train your team.