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.
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.
Translation
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.
Transcription
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
Customized
for your needs
Transcription
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.
Diarization
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.
We initially attempted to host Whisper AI, which required significant effort to scale. Switching to Gladia's transcription service brought a welcome change.
Read more
Speech-To-Text
Key techniques to improve the accuracy of your LLM app: Prompt engineering vs Fine-tuning vs RAG
Large Language Models (LLMs) are at the forefront of the democratization of AI and they continue to get more advanced. However, LLMs can suffer from performance issues, and produce inaccurate, misleading, or biased information, leading to poor user experience and creating difficulties for product builders.
Speech-To-Text
Keeping LLMs accurate: Your guide to reducing hallucinations
Over the last few years, Large Language Models (LLMs) have become accessible and transformative tools, powering everything from customer support and content generation to complex, industry-specific applications in healthcare, education, and finance.
Case Studies
Transforming note-taking for students with AI transcription
In recent years, fuelled by advancements in LLMs, the numbers of AI note-takers has skyrocketed. These apps are increasingly tailored to meet the unique needs of specific user groups, such as doctors, sales teams and project managers.