Data residency for voice and transcription data: EU, US, and AI compliance

TL;DR: Storing call recordings in an EU S3 bucket does not make your voice pipeline compliant if a US-based transcription API processes those files during inference. Data residency, data sovereignty, and AI processing represent distinct compliance considerations. This article maps those legal distinctions and the cross-border risks introduced by Business Process Outsourcing (BPO) access and AI model training defaults. It also covers how our EU-hosted infrastructure with configurable residency addresses those risks at the pipeline level, without inflating cost-per-contact or degrading Average Handle Time (AHT).

Custom vocabulary for contact center transcription: product names, brands, and agent jargon

TL;DR: Generic speech-to-text models fail most often on the words that matter most in contact center operations: your product names, brand terms, SKUs, and agent scripts. QA scorecards, CRM records, and coaching workflows break before any LLM sees the transcript because the foundational transcription layer already mangled those critical terms. Custom vocabulary dictionaries solve this at the source by using phoneme-similarity matching to guide transcription toward the correct output. The article covers how phoneme-based matching differs from post-transcription find-and-replace, when to use vocabulary versus spelling correction, and how to build, prioritize, and maintain your domain dictionary through product catalog changes.

How Gravite reduced call quality review time by 93% with Gladia

Quality monitoring is one of the most time-consuming processes in any contact center operation. Traditionally, supervisors would manually listen back to recorded calls — a practice known as "picking" or shadow listening — to evaluate agent performance, flag compliance issues, and identify coaching opportunities. For large enterprises handling thousands of calls daily, the math simply does not scale.

Vonage call transcription: adding real-time speech-to-text to Vonage

TL;DR: Integrating our speech-to-text infrastructure with the Vonage Voice API replaces fragmented recording, transcription, and enrichment stacks with a single API. By routing Vonage WebSocket streams directly to our endpoint, contact centers achieve approximately 270ms real-time latency for live agent assistance, or use post-call batch processing for automated QA scoring. Streaming is the right choice for live superviso. Async is the right choice when speaker-attributed QA scoring and full call context matter more than latency.

Key data extraction: accurately extracting names, account numbers, and intents from calls

TL;DR: Downstream contact center automation fails silently when the transcription layer misinterprets a name, transposes a digit, or attributes speech to the wrong speaker. Every QA scorecard, CRM entry, and coaching signal is ceiling-bounded by the accuracy of the layer beneath it. A wrong digit or phonetic name substitution propagates into every CRM field and compliance event that follows. Extraction precision is capped by transcription quality: Solaria-1 delivers on average 29% lower WER on conversational speech and 3x lower DER than alternatives, benchmarked across 8 providers, 7 datasets, and 74+ hours of audio.

Amazon Connect transcription: real-time speech-to-text for AWS contact centers

TL;DR: Contact centers using Amazon Connect struggle with high transcription costs and poor multilingual accuracy when relying on native tools. Routing audio via Kinesis Video Streams or S3 to Solaria-1 eliminates the Lambda 15-minute timeout risk and removes per-feature add-on costs. On conversational speech, Solaria-1 delivers on average 29% lower WER than alternatives, benchmarked across 7 datasets and 74+ hours of audio.

Custom vocabulary support in speech-to-text: how to teach the model your terms

TL;DR: Runtime vocabulary lists suit dynamic, frequently changing term sets; model fine-tuning is justified only for stable, fixed-domain vocabularies with unique acoustic conditions. The critical constraint is list length: adding terms that don't appear in your audio expands the decoder's search space and can degrade accuracy on entries that custom_spelling_config were already transcribed correctly. Measure improvement using keyword error rate on your specific entity set, not a global accuracy score, which masks failures on high-value terms. Update your vocabulary list on the same cadence as your product release cycle and prune any entries Solaria-1 already handles correctly.

Multilingual customer support: scaling global CX with real-time translation and transcription

TL;DR: Scaling global customer support without blowing out cost-per-contact requires more than a translation engine bolted onto a fragile stack. The real constraint is transcription accuracy: every mistranscribed word corrupts the downstream CRM entry, QA scorecard, and coaching output your operation depends on. Offshore BPO staffing can reduce costs by up to 65% compared to onshore agents, but only if your audio infrastructure handles accented speech and mid-call code-switching. This article breaks down the staffing-versus-technology trade-off, when AI translation holds up in production, and what the audio foundation needs to deliver.

Call center voice analytics: use cases, benefits, and how it works

TL;DR: Contact centers that rely on manual QA for call review typically sample only a small fraction of their total call volume, leaving the vast majority of audio unanalyzed. Voice analytics fixes this by converting raw phone calls into structured, LLM-ready data that feeds QA scorecards, CRM entries, and coaching workflows automatically. The catch is that telephony audio is uniquely hostile to standard speech APIs because narrowband codecs and packet loss break models trained on clean audio. This article explains the technical pipeline, the metrics that matter, and the infrastructure requirements that separate production-ready systems from vendor demos.