Audit
Voice stack architecture
We review STT, LLM, TTS, orchestration, streaming, and the reasons why the agent sounds unnatural or unstable.
Voice AI Consulting
for production, not demos
Consulting for teams building or already operating voice agents that need to bring architecture, latency, and dialogue quality up to production grade.
The service fits teams that already launched a voice agent or are preparing a rollout and want to reduce risks around architecture, latency, integrations, and dialogue quality.
We join as a practical engineering team and help bring the voice pipeline to a working level: fixing system issues in prompts, orchestration, telephony, monitoring, and operations.
600-900 ms
Target TTFB for live dialogue
Voice stack
Work with modern voice stacks
SIP + CRM
Telephony and process integrations
Audit -> Prod
From diagnosis to rollout
What we review first
We audit the full chain, not just one component
Audit
Latency and dialogue liveliness
We inspect TTFB, pipeline delays, interruptions, barge-ins, and bottlenecks across regions, models, and network calls.
Audit
Integrations and production processes
We check telephony, CRM, internal systems, escalation flows, and how the agent behaves under real production constraints.
Engagement format
Audit of an existing solution
Useful when an agent is already live but runs into quality, latency, scenario unpredictability, or integration complexity.
Engagement format
Targeted optimization
We can work on a specific issue: latency, prompt design, regressions, voice, monitoring, SIP, or interruption handling.
Engagement format
Knowledge transfer to your team
We run workshops and help build an internal practice for operating voice agents, not just closing isolated tickets.
Outcome
More natural dialogue pace
The team gets a more predictable agent response and tighter latency control in live voice scenarios.
Outcome
A clearer, more manageable voice stack
Architecture, models, and the pipeline are shaped around the real project constraints instead of a generic demo setup.
Outcome
Lower scaling risk
Quality control via metrics, tracing, and regression scenarios helps avoid expensive mistakes as volume grows.
How we work
A short cycle from audit to implementation
01
Diagnosis and goal setting
We capture quality, latency, security, on-premise, SLA, and integration constraints.
02
Audit and roadmap
We show exactly where quality is being lost, what slows the pipeline, and which fixes will matter first.
03
Implementation of selected blocks
We help with architecture, models, prompts, tests, monitoring, and integrations at the required depth.
04
Handover and support
We document decisions, transfer knowledge, and support the team in production when needed.