The challenge with voice AI isn’t the model—it’s everything around it.
Production-Ready, Not a Demo
Most voice AI demos are not over telephony. Real production is different.
What Actually Matters
1. Voice Activity Detection is Everything
Knowing when the user stopped speaking is harder than it sounds. We built multiple VAD engines optimized for different scenarios — aggressive for fast conversations, conservative for noisy environments, adaptive for everything in between.
2. Full Call Lifecycle
Initiation, connection, active conversation, evaluation, termination, archival. Each phase has its own failure modes. Connection handling, retry logic, state management.
Built-in Intelligence
Every call automatically generates:
- Call summaries — what happened in the conversation
- Structured outcomes — configurable per campaign (lead qualified? payment committed? callback requested?)
- Full transcripts — for compliance and training
This isn’t bolted on. The AI extracts this data as part of the conversation flow.
Enterprise-Grade Operations
- Capacity management for concurrent calls
- Campaign-level customization (prompts, voices, languages, evaluation criteria)
- Webhook-based archival for CRM integration
- Provider-agnostic telephony — onboard new telcos without rewriting
Prompting & Context Engineering
LLM costs add up fast in voice. Every turn is a new API call.
We obsess over prompt efficiency — minimal system prompts, structured context injection, aggressive pruning of conversation history. The goal: get the same quality response with fewer tokens.
Agent design matters too. Single-purpose agents with tight scopes outperform general-purpose ones in both latency and cost.
The Cost Reality
Voice AI platforms charge per minute. At scale, this destroys unit economics for high-volume use cases like collections or lead qualification.
Self-hosted infrastructure with efficient audio processing (WASM-based, no GPU required) changes the math entirely.