AI is revolutionizing financial services, but there are genuine risks to data security and decision accuracy. To maximize effectiveness, AI systems need access to sensitive data while keeping that data secure.

A merger or acquisition can trigger a data scavenger hunt that leaves everyone frustrated and exhausted. Mergers and acquisitions in the 21st century include data — a great deal of data.

Knowledge workers have access to more data than ever. Yet answering even a simple question can turn into a data scavenger hunt that consumes days or weeks of valuable time.

Retrieval Augmented Generation combines the power of large language models with the ability to retrieve information from vast data repositories. Companies should be wary of relying on single, centralized data repositories for RAG.

AI is transforming businesses by driving data-driven decisions, automating tasks, and opening new growth opportunities. Successfully integrating AI within your enterprise demands a strategic approach.

In the rapidly evolving domain of artificial intelligence (AI), data is the bedrock upon which intelligent systems are built.

AI infrastructure software is essential for deploying on-premises AI solutions. It operates as a middle layer between data, AI infrastructure, and large language models, ensuring end-users can harness AI without compromising data security.

Meta just launched Llama 3, claiming it as the most powerful open-source language model out there. It is a huge step forward for AI to understand and communicate almost like humans — so we decided to give it a SWIRL.

A Reader Large Language Model is an AI designed to understand and analyze text. Unlike LLMs that generate text, Reader LLMs focus on reading and making sense of existing language — for tasks like summarization, question answering, and re-ranking.