Convin, a leading AI-powered conversation intelligence platform for call center setups, recently launched its advanced Large Language Model (LLM), with 7 billion parameters. This model is specifically designed to improve the business output and resolve the unique challenges of customer-facing teams such as sales, support, and collections.

The contact center sector has consistently faced challenges with agent inefficiencies, such as extended after-call work, repetitive data entry, and conversation misunderstanding. Managers also face the risk of losing potential leads and the burden of manually processing vast amounts of data to derive customer insights. These inefficiencies have resulted in longer response times, poor first-call resolutions, unsatisfactory multilingual interactions, and lower customer satisfaction.

Convin's new LLM addresses these gaps and significantly outperforms leading models like GPT-3.5 by 40 percent and GPT-4 Turbo by 20 percent in accuracy. Trained on over 200 billion tokens and supporting 35+ Indian and South Asian languages, including codemixed variations, the model ensures precise transcriptions, zero to low hallucinations, and contextual understanding, making it ideal for diverse and global environments.  It reassures businesses of a critical advantage in delivering high-quality, culturally sensitive customer interactions.

Atul Shree, CTO, Convin says, “Convin’s domain-specific model is a game-changer for enterprise contact centers. Traditional language models often fail to deliver accurate results, but purpose-built models such as Convin LLM produce better results and are more accurate. Our goal is to empower customer-facing teams with actionable insights and real-time guidance, transforming how businesses manage customer interactions."

"By addressing major challenges such as agent inefficiencies, call centers can improve handling time, response time, and inconsistent customer experience. This streamlines processes and enhances customer satisfaction by providing precise, data-driven insights and predictive analytics. As a result, call centers realize a substantial cost reduction and new revenue generation,”  says Atul.

 

The process begins by identifying specific objectives related to inefficiencies in the contact center setup and selecting relevant data sources. Data is then collected and preprocessed to ensure high quality, including filtering, deduplication, and tokenization. Pre-training on this cleaned dataset helps the model understand linguistic patterns and adapt to different languages. Finally, the model undergoes fine-tuning with task-specific labeled data, refining its parameters to predict labels accurately and deliver optimal performance.