GLP embraces AI-driven data analytics on Microsoft Azure

This leading logistics real estate provider and business builder transformed its data lake with an intuitive interface that lets users ask open-ended questions and receive instant answers.

GLP Logo Industry Automotive, Transportation, Logistics Challenge

GLP operates an enterprise data lake (EDL) with downstream reporting tools and needed to enhance workforce productivity and minimize the data management team's workload when creating new reports and conducting analyses.

Solutions AI, Foundry for AI by Rackspace (FAIR™) Platforms Microsoft Azure
“Rackspace [has the] data experts who can really help and work with us directly on a day-to-day basis.”
Miao Song, Chief Information Officer, GLP

GLP image with FAIR trapezoids

Our customer

GLP is a leading logistics real estate provider and business builder specializing in logistics, real estate, data centers, infrastructure, renewable energy, finance and related technologies. With an extensive network of smart warehouses, GLP serves a myriad of logistics service providers, manufacturers, retailers and e-commerce giants.

Through careful incubation of new business ventures and efficient management of its assets, GLP maintains an inventive path toward growth and value creation. The firm is always on the lookout for novel, tech-driven strategies to enhance its operations and provide superior service to its partners and clients.

“We put digital technology, data and innovation at the center of how we run our business,” explains Miao Song, Chief Information Officer of GLP. “We're trying to help the business move faster, but also use what we have more efficiently.”

The obstacles they faced

GLP operated a robust Enterprise Data Lake (EDL), but the output data wasn't particularly user-friendly. This posed a notable challenge for the data management team, who frequently found themselves assisting other users in extracting crucial information regarding the company's managed assets and its operational regions.

GLP needed a swift, effective solution to alleviate the growing strain on resources and ensure timely, accurate data retrieval for all users. At the recommendation of members of its Microsoft® Azure® partner team, GLP reached out to the generative AI experts at Rackspace Technology®.

“We chose Rackspace because they are very practical,” explains Song. “They also have data experts who can really help and work with us directly on a day-to-day basis. We think that's most effective way of building use cases.”

worker in a warehouse

“Rackspace has a different approach compared to traditional consulting firms. They are much simpler, faster and practical.”

Miao Song, Chief Information Officer, GLP
worker in a warehouse

How we helped

Presented with 20+ potential use cases by GLP for its envisioned AI-driven data analytics solution, the Foundry for AI by Rackspace (FAIR™) team embarked on a series of discovery sessions to meticulously analyze GLP’s needs and identify the most pressing use case. Amid a fast-approaching visit by a global team to Singapore, a demo was created to showcase the potential of addressing GLP's core issue: the transformation of its daily reports through AI-enhanced data-querying capabilities.

“We did a few rounds of our sprints. And from sprint to sprint,  it was getting better and better. The use case become much more usable and real for our organization”, explains Song. “That was a key differentiator for Rackspace. I think the company has a different approach compared to traditional consulting firms. They are much simpler, faster and practical.”

Nathan Cheng, Data Analytics and Machine Learning Systems Engineer at Rackspace Technology, explained the wish list's two main themes: a user-friendly product interface and an intelligent system capable of deep-diving into data dimensions to answer complex business inquiries. This wish list evolved into concrete test cases, marking the roadmap for the project's technical progression.

Building a centralized hub for open-ended data insights was an ambitious endeavor that began with a focus on fund management and could extended to ESG metrics and beyond. The goal was to create a dynamic interface where users could easily access their conversation histories, particularly when reviewing the fund management KPIs via the app.

The interface design focused on a cohesive user experience and easy conversation-based functionality. Interactions needed to be logged, as documentation was crucial for identifying and analyzing any existing logic gaps, which would, in turn, inform iterative improvements to the system. Then conversations turned toward complex queries, like comparing the occupancy rate metrics of properties across different quarters. Such requests required the system to sift through multiple data dimensions, pinpoint the relevant data, and identify the factors that drove changes in the metrics.

Transitioning to the technical execution, Srinivas Chakravarthy, a Senior Director of Business Development at Rackspace, highlighted the innovative approach of crafting on-the-fly SQL queries based on users' inquiries. Initially working with CSP files, the goal was to transition into a production-ready phase where data lakes could be utilized for real-time data access and analysis. The process entailed running an SQL query, interpreting the data and furnishing the user with insightful responses. Achieving this required a precise SQL phrasing, aligning the technology to comprehend and respond to the contextual nuances of each inquiry.

What we achieved together

FAIR engaged with various GLP teams to create a deep understanding of the types of inquiries employees made. This led to quick and effective calibration of the AI and allowed GLP to deploy a generative AI chatbot atop its EDL that could generate answers to business-related questions. The chatbot, with its intuitive user interface, showcases common questions and guides users on how to formulate and pose accurate prompts. This feature also empowers users to review conversation histories and revisit previous inquiries.

This initiative was not just a technical challenge, but a journey toward creating a solution that bridged the technical-functional divide, making data-driven insights accessible to users in an intuitive and interactive manner. The synergies between a user-centric interface and a robust back-end data analysis mechanism showcase the immense potential of what this project could achieve in future phases.

About Rackspace Technology

Rackspace Technology is a hybrid, multicloud solutions expert. We combine our expertise with the world’s leading technologies — across AI, applications, data and security — to deliver end-to-end solutions. We have a proven record of advising customers based on their business challenges, designing solutions that scale, building and managing those solutions, and optimizing returns into the future. 

As a global hybrid, multicloud technology services pioneer, we deliver innovative capabilities to help customers build new revenue streams, increase efficiency and create incredible experiences. Named a best place to work, year after year according to Fortune, Forbes and Glassdoor, we attract and develop world-class talent to deliver the best expertise to our customers. Everything we do is wrapped in Fanatical Experience® — our obsession with customer success that drives us to help each customer work faster, smarter and stay ahead of what’s next. 


About Foundry for AI by Rackspace (FAIR)

FAIR™ is at the forefront of global AI innovation, paving the way for businesses to accelerate the responsible adoption of AI solutions. FAIR aligns with hundreds of AI use cases across a wide range of industries while allowing for customization through the creation of a tailor-made AI strategy that’s applicable to your specific business needs. Capable of deployment on any private, hybrid or hyperscale public cloud platform, FAIR solutions empower businesses worldwide by going beyond digital transformation to unlock creativity, unleash productivity and open the door to new areas of growth for our customers.


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