Empowering Store Associates with an AI-Enabled Customer Experience

A fortune 500 retailer offers a breadth of products to customers. From hardware to livestock supplies and beyond, at any point, employees must be able to guide customers to the right products for their needs. However, rarely can an employee know all options available to the customer for every product category. As a result, the sales process can become a challenging customer service experience. Needing to streamline how team members help customers and increase sales, an industry-leading company turned to UDig to help develop an AI enabled customer experience. They wanted to build a UX prototype to visualize their future-state vision and then bring that solution to life. Ready for the challenge, our team moved quickly to help them create their custom AI software.



Our client’s vast product inventory meant that employees sometimes lacked the expertise required to recommend relevant products to customers, limiting the company’s sales potential.


A two-step process that focused on first designing the prototype of an AI-powered app to realize our client’s vision and support funding goals, and then building the application and AI platform.


A cutting-edge app that uses AI to empower employees to become product experts and deliver a great customer experience that results in improved sales and business growth.

Challenge: Expansive Inventory Affects Sales Process and Customer Experience

Our client’s product inventory is vast and covers a breadth of home improvement, farming, gardening, and pet needs. Often, customers would talk to employees to get suggestions of the right product to buy. Sometimes, employees would easily know the answer. However, with such a large inventory, it was nearly impossible for every employee to be a product expert with everything they offer. Current practices meant that staff had to find an expert in the store when they didn’t know the answer to a question. This step took time, and as a result, the customers may have left the store without having all the answers or products they needed. 

As an industry leader, our client knew they had to improve their sales process and deepen employees’ understanding of their retail offerings. To do so, they wanted to innovate their tech strategy with an AI-powered app that could serve as a product recommendation tool for team members. To meet this goal, they needed help bringing their long-term vision to life and telling the story visually so stakeholders could understand the improvement potential.

Strategy: Build an App for Their Unique Customer Experience Goals

To support their goals and an expedited timeline, we needed to work in a two-step process:  

1. Build a long-term clickable prototype.

This step was critical for showing executive leadership how an AI-powered app could drive an industry-leading customer service experience. With a two-week timeframe for leadership to decide whether to build the app, we quickly produced a clickable prototype to help our client meet their deadline.

Once we completed the prototype, leadership had a tangible example of how an AI-powered app could drive an industry-leading customer service experience and they approved the prototype for immediate build. 

2. Develop the AI-powered app.

From there, our development team hit the ground running to build the app within a four-week turnaround. Since we had built the prototype first, we were able to greatly accelerate our development timeline. We had already flushed out any kinks or requirements in the prototype rather than during the app-design process.  

To further meet their expedited deadline and goals, we:   

  • Aligned priorities between our team and our client: Our client had different teams on the project, and we needed to ensure we all united around project priorities, goals, and requirements. 
  • Developed workflows: We spent the first week of the project hashing out API details and creating workflow diagrams to support an efficient build process between all teams.  
  • Built consensus with stakeholders: We had to identify how the app could seamlessly integrate into their current processes and fill gaps in the sales process. From there, we needed all stakeholders to agree that the proposed solution was the design they were seeking.   
  • Identified our client’s knowledge base and product inventory: Using AI to power the app meant that we had to gather all of TSC’s available data sources to feed the model in order to successfully help a customer.

From there, we applied our experience in full-stack development to build the app’s frontend and the backend orchestration. By leveraging AI, all curated information pulled details from our client’s content. This requirement ensures that the app delivers the right information to the staff and the best answer to the customer at any point in the sales process. When an employee doesn’t know an answer, they can speak into an earpiece and ask questions. From there, the chat will share which products exist in the store to fulfill the customer’s needs.

Outcome: An Industry-Leading, Product Recommendation Tool

Today, our client gained a first-mover advantage with AI tools within their industry by developing this app. This cutting-edge technology empowers employees to become product experts and delivers a great AI enabled customer experience that results in improved sales and business growth. Within a few months of deploying the app, our client had over 2,000 employees engaged and asking the app over 11,000 questions and receiving over 3,000 product recommendations. As a result, this technology has helped transform the team member experience while instilling customer trust during their shopping experience. Across stores, sales conversions have greatly increased. By prioritizing AI, they’ve gained a powerful solution that further solidifies their marketplace competitiveness and future growth.

How We Did It

Generative Artificial Intelligence
Mobile App Development
Product Ideation & Prototyping
Solution Architecture
UX/UI Design

Tech Stack

  • Azure Cache for Redis
  • Azure Machine Learning
  • Java, Spring Boot
  • Typescript, React Native