The PeachDesk blog
Guides on voice AI economics, architecture, security, and developer surfaces. Every figure carries a source and a date; every trade-off is stated plainly. Part of the Resources library, next to the voice AI glossary.
Agentic voice AI defined: a voice agent that plans and executes multi-step work on a live call through tool use. How it differs from IVR and voice bots.
A procurement-ready voice AI security and compliance checklist: the questions to ask vendors, the evidence to demand, and where PeachDesk stands.
Voice AI latency explained: the response-time budget across transport, STT, LLM, and TTS, why endpointing makes agents feel slow, and how to cut it.
Practical voice AI testing: text chat, simulated callers, browser voice, and real calls, with the metrics and checklist that catch failures before launch.
The four metered components inside every AI call, dated market figures from Vapi, Retell AI, Bland, and Synthflow, and how per-stage sourcing changes the math.
Ears, brain, and voice: the STT, LLM, TTS pipeline, cascaded versus speech-to-speech models, and the orchestration layer that makes agents operable.
What the Model Context Protocol is, and what it means when a voice AI platform exposes agent building as tools an AI assistant can drive.
The reminder call flow: API trigger or CSV campaign, confirmation capture, a rebook path on decline, webhook write-back, and per-reminder cost math.
Detect the miss by inbound webhook, call back within moments, qualify the caller, then book or transfer, with an after-hours answering variant.
Why metered resale compresses agency margin, and the three levers that put the cost floor, the attribution, and the retail price in your hands.