Best tools for reducing after-call work with automated transcription

TL;DR: After-call work compounds at scale: documentation, CRM updates, and disposition coding consume agent capacity. The architectural choice is binary: buy a packaged CI platform for fast deployment or build on an STT API for lower TCO, multilingual accuracy, and flexible LLM routing. The right choice depends on call volume, language requirements, and whether your team needs to own the pipeline.

AI call analytics platforms vs. STT APIs: which is right for multilingual transcription?

TL;DR: Multilingual transcription at scale reveals complexities beyond English benchmarks: while some modern platforms handle code-switching, many models still treat language switches as error states, producing high WER and transcript degradation. Some platforms charge separately for diarization, sentiment, and summarization features. Gladia's Growth plans bundle those features at the base rate. Gladia's Solaria-1 model is benchmarked on conversational speech, with native code-switching across 100+ languages.

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.