Real-time transcription for contact centers: what latency and accuracy thresholds matter

TL;DR: Real-time STT for contact centers requires sub-300ms latency to match the natural 200-300ms window of human conversation, but raw speed without accuracy breaks the product. The latency budget is cumulative: audio capture, STT inference, NLU, and TTS each consume a slice. Partial transcript stability matters as much as final output speed: IVR routing and agent assist act on intermediate text before the transcript locks, so instability at that layer compounds into wrong-queue transfers and missed coaching prompts.

What is OpenAI Whisper?

In September 2022, OpenAI quietly dropped something that changed the entire speech recognition industry: a model weight file on GitHub, free to download, free to run, free to modify. Within weeks, developers were running state-of-the-art transcription on their laptops. Within months, every speech-to-text vendor in the world was benchmarking against it.

Introducing Solaria-3: The most accurate speech-to-text model for European languages

Today we're releasing Solaria-3 – the new #1 among leading speech-to-text providers on business audio and conversational speech, delivering the strongest accuracy on real English customer calls of any model tested. It is our best model to date, which we trained for the audio our customers deal with in real life: calls with background noise, people talking over each other, teams switching between a few languages in one meeting.

Gladia integration recipes: connect calls to your CRM and workflow stack

TL;DR: Connecting call data to CRM and workflow tools requires accurate transcription at the base layer — downstream records are only as reliable as the words captured first. This guide covers four integration paths: Zapier for prototyping, Make.com for visual conditional routing, n8n self-hosted for high-volume privacy-sensitive workloads, and direct REST API for production infrastructure. Gladia's Solaria-1 model benchmarks at an average 29% lower WER and 3x lower DER versus alternatives.

How to build a customer support call flow (AI blueprint)

TL;DR: Traditional IVR systems route calls by button press and fail when callers switch languages mid-sentence. AI-augmented flows treat audio as a structured pipeline: async transcription handles the high-accuracy layer for diarization, post-call summaries, and CRM sync, while real-time transcription at sub-300ms latency enables the live agent assist layer covered in this guide. Sub-300ms latency ensures guidance arrives while conversations progress; higher latency reduces assist usefulness. Building in-house involves substantial infrastructure, DevOps, and maintenance costs.

Call transcription accuracy benchmarks: What contact centers should measure

TL;DR: Public STT benchmarks on clean English audio rarely predict how models perform on noisy, accented, multilingual contact center calls. To evaluate vendors properly, measure WER overall, WER per language and accent, DER, latency p50/p95/p99, and code-switching accuracy on your own production audio, not vendor test sets. Self-reported accuracy claims are meaningless without published methodology. Hidden per-feature fees for diarization and NER can compound significantly at scale compared to all-inclusive pricing models.

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.