Infosys has unveiled two new SLMs designed specifically for the banking and IT operations sectors

29 Oct 2024

News
Infosys has unveiled two new SLMs designed specifically for the banking and IT operations sectors

Two new small language models (SLMs) created especially for the banking and IT operations industries have been released by Infosys, a leading worldwide provider of IT and consulting services. These models, namely Infosys Topaz BankingSLM and Infosys Topaz ITOpsSLM, are constructed with the cutting-edge NVIDIA AI Stack. 

According to Infosys, these models will give businesses industry-specific AI capabilities and effective, scalable solutions for businesses wishing to improve their AI integration.

NVIDIA's AI Stack powers the Infosys Topaz BankingSLM and ITOpsSLM models, which provide substantial benefits to businesses in need of customised AI solutions. Developed in Infosys's NVIDIA-focused centre of excellence, these SLMs leverage both general and specialised industry data that has been enhanced by Sarvam AI in collaboration with NVIDIA AI Enterprise and NVIDIA AI Foundry. 

In sectors with intricate data and operational requirements, the partnership aims to facilitate the quicker and more accurate deployment of AI. 

SLMs are trained on smaller, better-quality datasets than large language models (LLMs), claims Infosys.

These models seek to increase accuracy and decrease reaction times by fine-tuning data unique to banking and IT processes, assisting companies in more successfully implementing targeted AI applications.

The recently released SLMs are made to work in unison with Infosys's well-known platforms, including Infosys Finacle and Infosys Topaz. 

Through these interfaces, companies may apply AI to particular functional domains, including IT management and customer service, while maintaining procedures that meet industry standards. 

In order to ensure data security and compliance, Infosys also offers pretraining and fine-tuning services to businesses wishing to create unique AI models suited to their own data and business requirements.



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