Call center transcription software: what enterprises should look for in 2026

TL;DR: Most contact centers evaluate transcription software using clean-audio lab benchmarks, then watch QA automation break down when BPO (Business Process Outsourcing) agents switch languages mid-call or phone-line noise degrades the signal. In 2026, the criteria that matter are real-world multilingual WER, all-inclusive per-hour pricing, and data sovereignty that holds up under GDPR and HIPAA audit. For enterprise teams, the highest-ROI evaluation step is testing on real BPO call samples rather than vendor demo audio, and asking every shortlisted provider for an all-in per-hour price with diarization, sentiment, and entity extraction enabled.

PII redaction for call recordings: how ingestion-level redaction keeps calls PCI compliant

TL;DR: Legacy pause-and-resume systems don't remove agents, local desktops, or telephony infrastructure from PCI DSS audit scope. Automated, ingestion-level PII redaction scrubs sensitive data before it reaches any database. By removing cardholder data at the ingestion layer, contact center platforms using automated redaction can potentially reduce audit complexity, cut agent handle time (AHT), and protect downstream CRM and LLM pipelines from corrupt data. The accuracy floor for reliable entity detection in PCI audits is significantly higher than for standard QA transcription, making STT model selection a compliance decision as much as a product one.

GDPR, SOC 2, and ISO 27001 speech-to-text: the contact center compliance and certification guide

TL;DR: When your contact center routes voice data through a transcription vendor, every certification gap in that vendor's stack becomes your compliance liability. Voice recordings qualify as personal data under GDPR Article 4, and processing them through uncertified APIs creates direct financial exposure. This guide breaks down what GDPR, SOC 2 Type II, ISO 27001, HIPAA, and PCI DSS each require of your audio infrastructure vendor and maps those requirements to the QA coverage rates and cost-per-contact metrics you manage daily. We hold GDPR, SOC 2 Type II, ISO 27001, HIPAA, and PCI DSS certifications, and never use customer audio for model training on Growth or Enterprise plan.

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.