Is AI transcription legal and safe for support calls?

TL;DR: AI transcription for support calls is legal in most jurisdictions, but consent rules are regional: 13 US states require all-party consent, while the EU's e-Privacy Directive and GDPR Article 6 require a documented legal basis to record at all, with countries like Germany treating unconsented recording as a criminal offense. Storage, redaction, and deletion add a separate layer under GDPR, UK GDPR, CCPA, and PCI DSS. The biggest gap in most CCaaS stacks is what the transcription vendor does with audio after the call, including whether it retrains models on your calls by default. On Growth and Enterprise plans, Gladia never trains on customer audio, no opt-out required; PII redaction is optional and must be explicitly configured.

How to choose the right automation use case in a contact center

TL;DR: Many contact center teams explore automation by considering customer-facing chatbots first, but that's often the highest-risk path: bot failures are visible, brand-damaging, and expensive to fix once deployed. A four-factor framework (volume x repetition x accuracy threshold x ROI timeline) tells you where automation actually pays off. Post-call async workflows deliver measurable returns inside a single sprint with zero customer-facing exposure. Start there, not with deflection. Aircall cut transcription time by 95% and now processes over 1M calls per week using Gladia's async STT API.

Building real-time multilingual ASR with code-switching

When a speaker switches languages, traditional models keep outputting the previous one for several hundred milliseconds before catching up, producing garbled text and inaccurate timestamps. The obvious fix is a large multilingual model. But those are expensive to run, awkward to deploy on-device, and still stumble on fast switches.

Factors affecting the accuracy of speech-to-text transcripts

TL;DR: Production STT accuracy fails not because of model benchmarks, but because of the gap between studio evaluation audio and the messy, multilingual, overlapping speech real users produce. Four root causes drive that gap: input audio quality, speaker traits (accents, code-switching, and overlap), domain vocabulary deficits, and model training data diversity. WER alone doesn't capture production risk. Semantic accuracy and Diarization Error Rate matter just as much when CRM syncs, coaching scores, and AI summaries all depend on what the transcript gets right. Solaria-1 delivers on average 29% lower WER on conversational speech and 3x lower DER compared to alternatives, benchmarked across 7 datasets and 74+ hours of audio with open, reproducible methodology.

Business call transcript analysis techniques for sales and support teams

TL;DR: Upstream transcription errors compound through every downstream system: LLMs, sentiment models, and CRM pipelines are only as reliable as the transcript they process. Core conversation intelligence techniques, including sentiment scoring, BANT extraction, objection mining, and talk-ratio analysis, all depend on transcription quality. Async/batch processing provides full conversation context, making it the right default for post-call workflows.

How AI contact centers determine caller intent

TL;DR: Caller intent routing fails at the transcription layer long before it fails at the NLU layer. If ASR misreads "cancel" as "candle" due to background noise or a non-native accent, no downstream classifier recovers the routing decision. This article covers the full intent pipeline: ASR, NLU, classification, and routing execution, the latency budgets that constrain real-time systems (~700ms total), and the audio conditions that break most production deployments.

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.