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How decision intelligence improves customer service consistency in contact centers

TL;DR: Contact centers fail to deliver consistent service when routing infrastructure runs on static rules engines that cannot handle the complexity of real human conversation. Modern speech-to-text infrastructure addresses this by processing raw audio and feeding structured outputs to your CRM, using machine learning to analyze intent, sentiment, and speaker characteristics. Transcription accuracy sets the ceiling for every downstream action: a wrong word silently corrupts a CRM entry, a missed intent misfires a routing decision, and a misread sentiment score delays escalation. This playbook covers how to build and deploy that architecture without blowing your latency budget or your unit economics.

Speech-To-Text

Real-time speech analytics for live agent assist

TL;DR: Live agent assist only works when the transcription layer delivers partial results fast enough for downstream NLP to process within a sub-second window. If the pipeline exceeds 1,000ms total, prompts arrive after agents have already spoken, which inflates Average Handle Time and erodes agent trust. This playbook covers the full real-time pipeline architecture, from streaming transcription through intent analysis to agent desktop rendering, and shows how contact centers can expand QA coverage from a 1-3% manual sample to 100% of interactions without adding headcount.

Speech-To-Text

How to identify prospect companies from sales call transcripts

TL;DR: Most product teams try to run LLM extraction on raw, undiarized transcripts and end up with CRM records polluted by the sales rep's own company names, tools, and competitor mentions. The fix is an async-first pipeline that separates speaker dialogue before any entity extraction happens. This guide walks through a working Python and Claude API pipeline using our async transcription, pyannoteAI Precision-2 diarization, and Solaria-3 or Solaria-1 depending on your language mix, so you extract clean prospect-side signals and sync accurate data to your CRM.

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: