As one of the world’s top-20 research facilities, Major Research University (MRU) is a public institution with more than 43,000 students and 37,000 employees offering degrees in the arts and sciences, engineering, business, medicine and more. The school is a hub for technological innovation home to numerous startups, a data center institute and a world-class supercomputing center.
THEIR GOAL
Improve information access and streamline tasks for staff and faculty
MRU had a vast knowledge base of institutional information — including policies, procedures, contracts and technical resources — across dozens of departments, institutes and schools. MRU’s IT team wanted to build a GPT solution that could make that information far more accessible and usable via intuitive chat agents in a secure environment. Ultimately, they envisioned helping staff, faculty and student employees save time and work smarter.
THEIR SOLUTION
Llama powers a suite of university AI assistants and coaches
In a 12-month sprint, MRU’s IT team used Llama to build a multi-agent fleet of AI assistants backed by university knowledge bases. The agents help with everything from internet searches and job descriptions to fund management and preventing email phishing.MRU chose Llama as the solution’s primary large language model (LLM) for its performance, cost savings, transparency and safety features. Llama performed better than competing open and closed models for accuracy, F1 scores, precision and recall. As an open-source solution, Llama allowed MRU to bypass the licensing fees and per-token charges that come with commercial models. Llama also gave developers more fine-tuning control and the ability to innovate quickly, upgrading from Llama 2 to Llama 3 as new models were released.
THEIR APPROACH
Multiple security layers ensure MRU stays in control of its data
To protect privacy, sensitive university data must never leave the school’s domain or be touched by a third-party service. With Llama, the GPT solution can be hosted entirely on premises at the school’s supercomputing center to ensure control.MRU’s IT team took data security a step further with the help of Protopia and their Stained Glass Transform (SGT) technology. SGT uses AI to transform data into randomized re-representations that an LLM can consume without exposing the source. The combination of on-premises infrastructure and SGT gives the solution the security required to index sensitive data and generate answers with minimal risk of data leaks or breaches.
THEIR SUCCESS
Significant productivity gains across the university
With Llama-backed AI agents, MRU has eased information gathering and automated routine and complex tasks. The solution does more than save time for staff and faculty, it lessens friction throughout the university.
Projected results:
62% reduction in time spent writing job descriptions
87% reduction in time spent searching HR, finance and facilities policies and documents
“The university is now perceived as a national and global leader in the application of AI due to the time-to-market advantage we had with the Llama models. We are inundated with inquiries from universities wanting to understand ‘How did we do this so quickly?’”
Chief Information Officer — Major Research University
“Universities have thousands of knowledge workers with a wide variety of processes and tasks that can be automated or augmented with AI. As we apply the Llama-powered GPT solution to the ‘long tail’ of knowledge tasks, we are seeing productivity gains at the university in many information and knowledge tasks.”
Chief Information Officer — Major Research University
“The GPT solution exemplifies a cost-efficient, multi-tenant, secure generative AI infrastructure. By harnessing the performance of Llama and the safety of Stained Glass Transform, it drives significant efficiencies both within and beyond the organization. We are excited to bring the GPT solution to other organizations with similar requirements across other regulated verticals and enterprises with sensitive data.”
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The Llama-powered MRU GPT solution uses an agentic approach that prioritizes open-source software whenever possible.
80% reduction in time spent analyzing and redlining contracts
*All results are self-reported and not identifiably repeatable. Generally expected individual results will differ.
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