Why?
After building real-time apps in fintech and healthcare, I noticed that most “AI voice” projects never make it to production — not because the models are weak, but because latency, integrations, and rollout safety are under-engineered.
Most teams can make a demo; very few can keep it stable under 250–300 ms p95 barge-in latency.
EchoStack is an attempt to codify what production actually requires — including latency budgets, configuration blueprints, and a controlled deployment workflow.
What it does?
Each “playbook” is a deployable Voice-AI setup that includes:
Preflight → Plan → Apply (Blue) → Smoke test → Switch (Green) → Rollback
Latency-audited pipelines (ASR, LLM, TTS budgeted at p95 < 300 ms)
Exportable configurations for no-code and code-based integrations
KPI tiles for outcomes (AHT, bookings, self-serve rate)
Two early examples:
After-hours Answering — handles missed calls, FAQs, and escalations
Lead Qualifier → Auto-Book — filters inbound leads and books meetings automatically
Status
Early Access only — no public demo yet. We’re currently validating latency budgets, rollout safety, and adapter design (CRM, telephony, calendar).
Feedback I’d love
Are these latency targets realistic in your experience?
How do you test reliability in real-time LLM pipelines?
What signals do you track before rolling out voice changes to prod?
Full details: https://getechostack.com/playbooks
solomonayoola•3mo ago