Enterprise and on-premise speech-to-text: security, SLA, and data residency

TL;DR: If your speech-to-text vendor trains their models on your customers' audio by default, you're carrying a silent compliance liability into every product deployment. Building enterprise-ready voice products demands more than low Word Error Rates: it requires absolute control over data residency, strict compliance with frameworks like GDPR and SOC 2 Type II, and predictable cost models that don't collapse at scale. This guide covers what to verify before committing to an enterprise STT vendor, including whether you genuinely need on-premise infrastructure or whether cloud-native controls (ZDR, EU data residency, per-account isolation, DPA/BAA) already satisfy the same governance requirement, and explains exactly where we draw the line on data handling across every pricing tier. Gravite, a French call quality-monitoring platform transcribing 50,000 hours of audio per year, cut call quality review time by 93% on our cloud API, without managing a single piece of on-premise infrastructure.

Agentic AI in the contact center: autonomous agents and the STT layer

TL;DR: Autonomous contact center agents fail when their STT layer fails. Transcription errors do not stay contained to the transcript, and a misheard account number, a missed compliance phrase, or a wrong speaker attribution propagates into every downstream system that acts on it. For operations leads deploying agentic AI, the decisions that determine whether automation holds or collapses under production conditions are: which STT model fits which workflow, how accuracy requirements shift across deployment stages, and why STT selection is a compliance decision as much as a product one.

Adding real-time streaming transcription to an async STT pipeline: a build guide

TL;DR: Adding real-time transcription to an existing async pipeline does not require a rewrite. The production pattern is hybrid: stream audio to Solaria-1 via WebSocket for sub-103ms partials and approximately 300ms end-to-end final latency, while buffering the same audio for Solaria-3 async processing with full diarization and entity extraction. The engineering work is WebSocket lifecycle management, buffering, VAD (Voice Activity Detection) configuration for turn-taking, and deduplication logic. This guide covers each layer with code examples and latency budgets.

Voicebot for call centers: how speech-to-text powers automated phone agents

TL;DR: A voicebot is only as effective as its underlying speech-to-text layer. Two requirements determine whether an automated phone agent holds up at production scale: partial transcript latency within a 300ms total pipeline budget, and production-grade accuracy under real telephony conditions, such as noisy, accented, codec-compressed audio. When the STT layer is slow or inaccurate, every downstream system inherits the error: wrong transcripts corrupt CRM records and misroute callers. This playbook covers the latency budgets, accuracy thresholds, and cost models that determine whether a voicebot improves or erodes your operational metrics.

Speech-to-text normalization for French, German, and Spanish

TL;DR: Inverse Text Normalization (ITN) is where multilingual STT pipelines silently break downstream: when a French speaker says "quatre-vingt-dix-sept euros," your pipeline needs to output "97 €," not a seven-word string that breaks your CRM insert. French vigesimal counting, German unit-before-tens reversal, and Spanish gender-agreement each require distinct deterministic rules. A single shared ruleset produces systematic errors in at least two languages. This article covers ITN mechanics for French, German, and Spanish, compares WFST and hybrid architectures, and explains why pipeline-level normalization matters. For European business audio running through these pipelines, Solaria-3 achieves 6.4% WER on Earnings22 financial calls, the only model under 7%.

Speaker re-identification across recurring meetings

TL;DR: Single-session diarization assigns temporary labels like "Speaker 0" or "Speaker 1" that reset with every new meeting. To build a meeting assistant users actually return to, you need persistent speaker re-identification that maps voice embeddings to the same participant across every session in a recurring series. Our async pipeline combines pyannoteAI Precision-2 for diarization with Solaria-3 (European business audio) or Solaria-1 (multilingual coverage and real-time streaming), delivering on average 3x lower DER than alternatives and giving product teams the accurate speaker segments needed to build this without adding months of engineering overhead.

Add speech-to-text to a Pipecat voice agent

TL;DR: In a natural voice agent conversation, anything over 500ms end-to-end feels stilted. Transcription latency is the first line item in that budget, and it sets the ceiling for everything downstream. This guide walks through wiring Solaria-1 into a Pipecat pipeline, tuning VAD thresholds, and handling failure modes, so the STT layer is a decision you can revisit without a rewrite. Because each component is independently swappable, choosing your STT provider is an architectural constraint you control, not one the framework imposes.

Add speech-to-text to a LiveKit voice agent

TL;DR: Voice agent latency comes from every layer, but the LLM accounts for the largest share of your total budget. Self-hosting open-source STT models burns GPU budget on cold-start delays and accuracy that degrades on accented speech. Integrating our Solaria-1 streaming API with LiveKit gives you partial transcripts in under 103ms and final transcripts at approximately 300ms, with true code-switching across 100+ languages. This guide delivers production-ready Python and Node.js code to connect LiveKit's audio egress to Gladia, configure silence detection, and drive natural turn-taking using live partials.

Add speech-to-text to a Recall.ai meeting bot

TL;DR: Recall.ai handles the platform-level complexity of joining Zoom, Meet, and Teams calls while we handle transcription, diarization, and enrichment. This guide walks through the full integration: spawning a bot, routing recorded audio to our async API, enabling pyannoteAI Precision-2 diarization, and mapping speaker labels to participant names using timestamp overlap. Choose Solaria-3 for post-meeting accuracy on English and European business audio, Solaria-1 for real-time captions or broad language coverage. On Growth and Enterprise plans, your audio is never used to train our models.