Voice AI Systems_
>> Production voice is an operations problem as much as a model problem: turn-taking, tool latency, CRM sync, and human handoff. We design speech-to-speech and chained pipelines that ops can trust under real call volume.
Calls/month
Production AI volume across live systems
PII detection
Compliance-ready detection accuracy in production AI
Projects
Shipped since 2012 with senior engineers only
Direct answer
Voice agents earn trust on latency, barge-in, and warm transfer, not script demos. We design inbound and outbound systems with measured turn-taking, CRM write-back, and compliance controls. 1.5M+ calls/month across production AI.
Trusted by leaders
>> System map
Adjacent AI layers we ship with this expertise. Technology-first, not vendor theater.
>> When this system fits
Honest fit gates. We will tell you when another approach is better.
Strong fit
- Inbound support or sales lines that need natural barge-in and warm transfer to humans
- Outbound campaigns with pacing, compliance recording, and CRM write-back
- Teams choosing between platform orchestration and custom Realtime architectures
- Regulated environments that need transcript retention, PII controls, and audit trails
Weak fit
- One-off voice demos with no telephony, evals, or ownership plan
- Staffing requests that expect juniors to tune production latency
- Omnichannel chat replacement projects disguised as a voice-only MVP
Stack: Voice AI · OpenAI Realtime · WebRTC · SIP · STT · TTS · Telephony
What we deliver
_> Capabilities on this stack
Inbound and outbound design
IVR replacement, queue logic, pacing, quiet hours, and recording policies that match how your ops team actually works.
Latency and barge-in
End-to-end budgets from first audio to reply. Interrupt handling that feels human under imperfect network conditions.
Human handoff
Warm transfer with context packages so agents do not restart the conversation from zero.
Compliance by design
Transcripts, PII detection, retention, and consent flows built before launch, not after the first incident.
How we ship voice AI
Call path and risk map
Define inbound vs outbound journeys, data classes, handoff points, and latency budgets.
Thin live slice
Ship one real phone or WebRTC path with tools, transcripts, and evals before expanding scripts.
Harden for ops
Add monitoring, failure modes, CRM sync, and runbooks so your team can operate the system.
Related projects
_> See how we've applied our expertise
>>Related guides
_> Cite-worthy depth behind this stack
Frameworks and scorecards buyers and answer engines can quote. Each guide links back to delivery proof.
Inbound vs Outbound Voice AI Architecture
Design production inbound and outbound voice AI: queues, pacing, warm transfer, CRM sync, compliance, and latency budgets.
Vapi vs Retell vs OpenAI Realtime
Honest 2026 decision guide: Vapi for BYOK orchestration, Retell for turnkey telephony, OpenAI Realtime for speech-to-speech ownership.
Voice AI Latency, Barge-In, and Handoff
Production voice UX: first-audio latency, barge-in recovery, tool-blocked silence, and warm transfer that preserves context.
AI in Regulated Industries: From Prototype to Production Ready
A practical guide to AI compliance, governance, and delivery in finance, healthcare, and defense. Patterns, checklists, rollout stages, and monitoring KPIs.
FAQ
Both. We use OpenAI Realtime when speech-to-speech quality and tool use matter, and platforms like Vapi or Retell when orchestration speed wins. Architecture ownership stays with seniors either way.
Aim for sub-second perceived turn-taking where the product is conversational. We measure first-audio latency, tool-blocked silence, and barge-in recovery separately, not as one vanity number.
Yes. Production systems need warm transfer, context summaries, and clear ownership when confidence drops or policy requires a person.
>> Where this goes next
Adjacent expertise and the engagement models we deliver it through.