Many organizations are in the process of evaluating generative AI and its potential benefits for their business operations. This heightened interest is driven by the growing recognition of AI's transformative capabilities across various industries.
However, while some organizations have successfully identified ways to leverage AI, many find this task to be a daunting one. The challenges lie in deploying a suitable large language model, or LLM, and designing an effective user interface for it.
Deploying a suitable LLM involves various complexities, including selecting the right model architecture, fine-tuning it for specific tasks, and ensuring scalability and efficiency. The deployment process requires computational resources and expertise, making it a hurdle for organizations lacking in-house AI capabilities.
Also, understanding which structured or unstructured data is usable for AI applications is another challenge. Organizations possess vast amounts of data, and sifting through it to identify valuable information that can enhance AI systems can be a complex and time-consuming endeavor. It requires a deep understanding of data analytics, data governance and data quality assurance.
Enter Rackspace.
Rackspace has introduced a game-changing AI/ML solution to easily leverage available data sources to create new insights, increase productivity and enable better customer outcomes in the form of Rackspace Intelligent Co-worker for the Enterprise, or ICE.
ICE operates by drawing conclusions and producing text content using an exclusive database of meticulously curated enterprise data stored within a secure and private enterprise platform. When paired with streamlined LLMOps and meticulous fine-tuning, ICE enables employees to engage in constructive and secure dialogues with an AI assistant that aligns with the values and objectives of their company.
Rackspace ICE leverages AI to simplify repetitive tasks, spot promising leads, and furnish real-time contextual insights for highly personalized customer interactions. Additionally, it has the capacity to significantly reduce the time needed for the creation of compelling customer presentations and proposals by harnessing and amalgamating extensive sets of structured and unstructured data.
For instance, ICE can employ data from sales and financial systems to pinpoint potential leads and merge information from proposals, brochures, data sheets, white papers, knowledge bases and document repositories to generate tailored presentations and proposals tailored to each customer's unique needs.
“ICE is a game-changer for businesses looking to harness their collective data, providing more productive and efficient, and ultimately deliver better customer outcomes,” said Nirmal Ranganathan, FAIR Chief Architect, Rackspace. “Besides the flexibility, the time from concept to value is remarkably quick, ensuring time is spent on data that can render results.”
Developed by the Foundry for AI by Rackspace, or FAIR, as an enterprise-centric AI solution, ICE may represent the future of AI integration within workplaces – offering enterprise-grade generative intelligence that can be trusted.
Edited by
Alex Passett
