Unlock cutting-edge AI/ML research for enterprise voice tech
We investigate and innovate solutions to complex challenges in speaker recognition, liveness detection, and AI–empowering enterprise contact centers to deliver smarter, more secure customer experiences.
Bridging the language gap between humans, AI, and enterprises
We're advancing AI research in linguistics and systems to develop scalable solutions that excel in real-world complexity. Our advanced models help businesses enhance CX, improve automation, and bolster defenses against identity-based attacks, paving the way for more secure, efficient interactions.
Addressing AI-driven threats to telecom: Insights from FCC's CSRIC IX panel
AI/ML can reshape telecommunication networks, but it also introduces complex security risks. To address this, the U.S. FCC chartered a working group under the 9th CSRIC. Co-chaired by FreeClimb's Chief Data Scientist Dr. Vijay K. Gurbani, the group will assess AI/ML's impact on threat surfaces and develop strategies to protect the nation's telecom infrastructure.
Better spoof detection for synthetic voices
Voices can be cloned with just 30 seconds of audio, creating challenges for systems and agents to distinguish real from fake. Our advanced model quickly adapts to new voices and changing conditions–even with limited training data–providing businesses with more reliable protection against AI-driven voice fraud.
Our focus areas
Bespoke automatic speech recognition
Custom ASR models designed for your specific domain or application.
Conversational AI and intelligent agents
Human-like virtual agents that understand and resolve customer needs.
Enterprise use of large language models
Surface insights, automate workflows, and drive better decision-making.
Be the first to know about our latest innovations
Join us at upcoming events where we share our latest research findings and innovative solutions on speaker recognition, liveness detection, and LLM integration in enterprise systems.
From research to real world
Enhancing transcription accuracy
ASR systems often struggle to accurately transcribe audio, especially in noisy environments. By leveraging LLMs, we select the optimal ASR output, reducing word error rates and improving transcription quality.
Going beyond the surface of sound
Using sparse autoencoders (SAEs), we uncover hidden features in audio data to enhance speech recognition and voice authentication systems, enabling businesses to break down complex data into insights.
Boosting voice authentication in multi-speaker settings
Traditional systems have difficulty differentiating speakers in group settings. Our new technique increases speed and accuracy of multi-speaker detection–enabling better voice biometrics, diarization, and forensics.
FreeClimb Research FAQs
FreeClimb’s AI/ML research focuses on solving complex challenges in enterprise voice technology, including automatic speech recognition (ASR), speaker recognition, liveness detection, conversational AI, and large language models (LLMs). Its research has been recognized by the IEEE and at several conferences.
FreeClimb develops advanced AI and machine learning solutions that improve voice accuracy, automation, and security in real-world environments. Research areas include enhancing transcription quality, detecting synthetic voices, improving voice authentication, and enabling more natural AI-powered customer interactions.
Bespoke automatic speech recognition (ASR) models are customized speech models trained for specific industries, applications, or business terminology. By adapting models to unique language patterns and use cases, organizations can improve transcription accuracy, reduce errors, and create more effective voice experiences.
FreeClimb researches how large language models and AI agents can improve customer interactions through better natural language understanding, automation, and decision-making. Our work explores how LLMs can help contact centers deliver more personalized, efficient, and intelligent experiences.
FreeClimb researches technologies that help protect voice communications from emerging threats such as synthetic voice fraud and identity-based attacks. Our work in speaker recognition and liveness detection helps organizations improve authentication and distinguish real voices from AI-generated ones.
FreeClimb combines research expertise with decades of telecommunications engineering experience to develop practical solutions for enterprise environments. Our teams evaluate emerging AI technologies and apply them to real-world challenges in customer experience, automation, and communication security.
FreeClimb shares research findings through published papers, reports, presentations, and industry events. Explore our research library to learn about advancements in voice AI, ASR, LLMs, speaker recognition, and enterprise communication technologies.