How decision intelligence improves customer service consistency in contact centers

TL;DR: Agent variance and inconsistent CX stem from a broken data pipeline, not a missing AI model. Decision Intelligence (DI) governs routing and coaching by turning raw conversation data into standardized, automated actions across every shift and agent tier, but DI is a garbage-in, garbage-out system. High word error rates in the transcription layer corrupt intent classification, sentiment scores, and routing decisions before the model ever fires. The capture layer is the bottleneck, not the LLM. Gladia's Solaria-1 model delivers on average 29% lower WER and 3x lower DER than alternatives on conversational speech, giving DI systems the accurate, multilingual input they need to produce consistent outcomes at scale.

What information should a customer support call capture for CRM and product analytics?

TL;DR: Support calls hold the richest source of product intent in your stack, but unstructured audio keeps that data invisible until someone manually tags it. A complete call record should capture caller identity, problem severity, previous attempts, resolution steps, voice of customer, follow-up commitments, and metadata, then flow into your CRM and product analytics without agent effort. Routing async transcription into a structured LLM prompt extracts all of it automatically. Downstream quality depends entirely on the transcription layer: high WER silently corrupts every CRM entry and analytics signal that reads from it.

Modernizing contact center architecture with AI agents and transcription

TL;DR: If you're rebuilding your contact center around AI agents, one architectural decision determines everything else: what sits beneath the AI layer. Transcription is that foundation. Every intent classification, routing decision, agent action, and QA score inherits the ceiling set by what the xSTT layer captures. This article walks through the architectural shift from legacy stack to AI-native design, covering where transcription breaks and why. It covers how platforms like Aircall and Selectra restructured their data layer so downstream.

Top 6 agentic features in meeting assistants

Meeting assistants have spent a decade getting better at recording and almost no time getting better at acting. That is finally changing. A new generation of tools is crossing the line from capture to action: they look things up mid-call, intervene when something required is missing, draft the follow-ups, reason across your meeting history, expose meeting context to other AI tools, and synthesize hours of conversation into formats you can actually consume.

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.

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.

Call center note-taking tips: how to capture better support conversations

TL;DR: Manual call notes split agent attention and introduce errors that corrupt downstream systems. Structured documentation covering account ID, intent, steps attempted, sentiment, and commitments is the minimum viable baseline. Scaling that standard means replacing manual shorthand with our async API, which returns speaker-labeled, LLM-ready output in a single call, processing approximately one hour of audio per 60 seconds, with no customer audio used for model retraining on Growth and Enterprise plans.

Custom vocabulary vs. custom spelling: which one to choose for better transcripts

Even the most advanced speech-to-text systems make mistakes when they hit brand names, technical acronyms, or non-standard pronunciations. For call centers and customer service platforms, these aren't minor glitches: they break workflows, misrepresent customer needs, and erode trust on both ends of the call.