API Comparison Table

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Speech-To-Text

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.

Speech-To-Text

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.

Speech-To-Text

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.

Gladia selected to participate in the 2024 AWS Generative AI Accelerator

Published on Sep 18, 2024
Gladia selected to participate in the 2024 AWS Generative AI Accelerator

We’re proud to announce that Gladia has been selected for the second cohort of the AWS Generative AI Accelerator, a global program offering top early-stage startups that are using generative AI to solve complex challenges, learn go-to-market strategies, and access to mentorship and AWS credits.

This opportunity will help Gladia build, train, test, and launch products such as agent assistance for contact center platforms, sales enablement tools and AI meeting assistants, and enable voice-first platforms to deliver more value to their users across borders.

“The new generation of startups is at the forefront of a transformative new wave, pushing the boundaries of what’s possible with artificial intelligence while bringing exciting new solutions to market,” said Jon Jones, Vice President of Go-to-Market at AWS and executive sponsor of the program.
“Expanding the cohort for our Generative AI Accelerator is a testament to the potential we see for startups to usher in new innovations for customers in an increasingly AI-driven world. AWS is committed to fostering groundbreaking technologies and supporting visionary founders on their journey to solve the world’s biggest challenges.”

Gladia is one of 80 global startups from around the world selected for the program, and we’ll attend and showcase our solutions to potential investors, customers, partners, and AWS leaders in December at re:Invent 2024 in Las Vegas.

For more information on the Generative AI Accelerator, visit AWS Generative AI Accelerator.

About Gladia

Gladia provides a speech-to-text and audio intelligence API for building virtual meeting and note-taking apps, call center platforms, and media products, providing transcription, translation, and insights powered by best-in-class ASR, LLMs, and GenAI models.

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