Use case

Call Centers

Customer experience with insight

Improve customer service, streamline operations, and ensure compliance with regulatory requirements. Gladia API provides valuable insights into customer behavior and needs, improves communication, and enhances call center security.

global tech support

Top features

Speech analytics

Analyze customer tone of voice tone and language patterns to identify sentiment and mood, providing call center agents with valuable insights into customer behavior and needs. Essential for companies dealing with a high volume of customer calls.


Transcribe customer interactions in real time and translate them into different languages, allowing call center agents to communicate with customers in their preferred language. Ideal for serving a global customer base.


Transcribe high volume of calls and get a written record of all talking points, decisions made, and action items. Essential for keeping track of customer interactions for compliance, training, or quality assurance purposes.

Quality monitoring, privacy, and compliance

Monitor and analyze call center interactions in real time to ensure compliance with regulatory requirements and quality standards. Our upcoming PII redaction add-on will identify and redact all personally identifiable data, like social security and credit card numbers.

Some stats on performance

boost in sales
saved processing calls
k $
gained in quarterly budget

for your needs


Gladia API utilizes automatic speech recognition technology to convert audio, video files, or URL to text format. It transcribes 1h of audio in less than 60s.


Based on a proprietary algorithm, automatically partitions an audio recording into segments corresponding to different speakers.

Topic classification

Refers to the process of categorizing content into one of the 698 predefined topic categories for content indexation.

Sentiment analysis

Determining the sentiment or opinion behind a piece of audio, such as a conversation or dialogue, using natural language processing.

Speech moderation

Allows to automatically identify and flag hate speech or other inappropriate and offensive verbal content according to pre-determined parameters.

Emotion detection

Our emotion recognition system is built upon the latest research and aims to accurately identify and distinguish between 27 human emotions.



Perfect for developers, early-stage startups, and individuals



(10h/month included)


Designed to grow with scaling digital companies



+ $0.144 / hour for live transcription


Custom plan tailored to the modern enterprise

Contact us

We initially attempted to host Whisper AI, which required significant effort to scale. Switching to Gladia's transcription service brought a welcome change.

Robin lambert, CPO LIVESTORM

Read more


Should you trust WER?

Word Error Rate (WER) is a metric that evaluates the performance of ASR systems by analyzing the accuracy of speech-to-text results. WER metric allows developers, scientists, and researchers to assess ASR performance. A lower WER indicates better ASR performance, and vice versa. The assessment allows for optimizing the ASR technologies over time and helps to compare speech-to-text models and providers for commercial use. 


OpenAI Whisper vs Google Speech-to-Text vs Amazon Transcribe: The ASR rundown

Speech recognition models and APIs are crucial in building apps for various industries, including healthcare, customer service, online meetings, and entertainment.


Best open-source speech-to-text models

Automatic speech recognition, also known as speech-to-text (STT), has been around for some decades, but the advances of the last two decades in both hardware and software, especially for artificial intelligence, made the technology more robust and accessible than ever before.