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Speech-To-Text

How decision intelligence improves customer service consistency in contact centers

TL;DR: Contact centers fail to deliver consistent service when routing infrastructure runs on static rules engines that cannot handle the complexity of real human conversation. Modern speech-to-text infrastructure addresses this by processing raw audio and feeding structured outputs to your CRM, using machine learning to analyze intent, sentiment, and speaker characteristics. Transcription accuracy sets the ceiling for every downstream action: a wrong word silently corrupts a CRM entry, a missed intent misfires a routing decision, and a misread sentiment score delays escalation. This playbook covers how to build and deploy that architecture without blowing your latency budget or your unit economics.

Speech-To-Text

Real-time speech analytics for live agent assist

TL;DR: Live agent assist only works when the transcription layer delivers partial results fast enough for downstream NLP to process within a sub-second window. If the pipeline exceeds 1,000ms total, prompts arrive after agents have already spoken, which inflates Average Handle Time and erodes agent trust. This playbook covers the full real-time pipeline architecture, from streaming transcription through intent analysis to agent desktop rendering, and shows how contact centers can expand QA coverage from a 1-3% manual sample to 100% of interactions without adding headcount.

Speech-To-Text

How to identify prospect companies from sales call transcripts

TL;DR: Most product teams try to run LLM extraction on raw, undiarized transcripts and end up with CRM records polluted by the sales rep's own company names, tools, and competitor mentions. The fix is an async-first pipeline that separates speaker dialogue before any entity extraction happens. This guide walks through a working Python and Claude API pipeline using our async transcription, pyannoteAI Precision-2 diarization, and Solaria-3 or Solaria-1 depending on your language mix, so you extract clean prospect-side signals and sync accurate data to your CRM.

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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