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How contact center AI improves efficiency: benchmarks and ROI

TL;DR: Manual QA teams review 1–5% of contact center calls; AI-powered platforms can score all of them, but only when the underlying transcript is accurate. WER and DER are the hidden bottlenecks: a wrong name, missed compliance phrase, or misattributed speaker corrupts every downstream system that reads the transcript, from routing and agent assist to post-call summaries and QA scoring. Our Solaria-1 model delivers on average 29% lower WER than alternatives on conversational speech and on average 3x lower DER (diarization error rate), covers 100+ languages including 42 that no other STT API supports, and handles the full audio pipeline (record, transcribe, enrich) in a single API.

Speech-To-Text

How to integrate AI into contact center performance monitoring

TL;DR: Most contact centers manually review only a small fraction of calls, leaving compliance breaches and coaching signals undetected. Scaling to 100% AI QA coverage means choosing between three integration patterns (CCaaS-native tools, add-on API layers, or a custom build), each determined by how well your speech infrastructure handles noisy, multilingual audio. For post-call monitoring, async batch transcription outperforms real-time on accuracy, diarization quality, and cost predictability at scale. The bottleneck is getting a reliable transcript from noisy call center audio, which is where Solaria-1 and all-inclusive per-hour pricing matter most.

Speech-To-Text

AI solutions for call centers without human translators

TL;DR: At an illustrative fully loaded offshore rate of $6–$15/hr, replacing BPO translation at 10,000 hours/month with Gladia's Growth plan brings the estimated cost from $80,000–$150,000 down to approximately $2,000/month, with diarization, translation, NER, and sentiment included at the base rate. Every downstream output is ceiling-bounded by STT accuracy: a single transcription error produces a wrong translation, a wrong CRM entry, and a wrong coaching score. Native code-switching support is the bottleneck most teams discover only in production. Solaria-1 covers 100+ languages, including 42 not available on any other STT API, with mid-conversation code-switching built in from day one.

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: