API Comparison Table

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Agentic AI in the contact center: autonomous agents and the STT layer

TL;DR: Autonomous contact center agents fail when their STT layer fails. Transcription errors do not stay contained to the transcript, and a misheard account number, a missed compliance phrase, or a wrong speaker attribution propagates into every downstream system that acts on it. For operations leads deploying agentic AI, the decisions that determine whether automation holds or collapses under production conditions are: which STT model fits which workflow, how accuracy requirements shift across deployment stages, and why STT selection is a compliance decision as much as a product one.

Speech-To-Text

Adding real-time streaming transcription to an async STT pipeline: a build guide

TL;DR: Adding real-time transcription to an existing async pipeline does not require a rewrite. The production pattern is hybrid: stream audio to Solaria-1 via WebSocket for sub-103ms partials and approximately 300ms end-to-end final latency, while buffering the same audio for Solaria-3 async processing with full diarization and entity extraction. The engineering work is WebSocket lifecycle management, buffering, VAD (Voice Activity Detection) configuration for turn-taking, and deduplication logic. This guide covers each layer with code examples and latency budgets.

Speech-To-Text

Voicebot for call centers: how speech-to-text powers automated phone agents

TL;DR: A voicebot is only as effective as its underlying speech-to-text layer. Two requirements determine whether an automated phone agent holds up at production scale: partial transcript latency within a 300ms total pipeline budget, and production-grade accuracy under real telephony conditions, such as noisy, accented, codec-compressed audio. When the STT layer is slow or inaccurate, every downstream system inherits the error: wrong transcripts corrupt CRM records and misroute callers. This playbook covers the latency budgets, accuracy thresholds, and cost models that determine whether a voicebot improves or erodes your operational metrics.

Ebook: Ultimate guide to using LLMs with speech recognition

Published on Jan 7, 2025

Large Language Models (LLMs) have enabled businesses to build advanced AI-driven features, but navigating the many available models and optimization techniques isn't always easy.

If you’re looking to combine speech recognition (STT) and LLMs for cutting-edge voice apps, look no further! Our ultimate guide is finally here, and it’s filled with valuable strategies and hands-on insights from our work with hundreds of audio-first companies and extensive interviews with experts in AI note-taking, sales enablement and customer support.

What you'll learn: