Projects

Originally founded as an online bookstore, Amazon is now a leader in e-commerce and cloud computing, accounting for one in three online shopping transactions in North America.

When an Amazon customer needs help with a product, support staff must be ready immediately to ask him or her for more information in order to diagnose the problem, working hard to further avoid frustrating the customer.

Customer support organizations need better ways to understand the problems their customers are facing in order to help them more efficiently. Browser sharing, or co- browsing, provides support representatives with a visual way to guide customers to a quick and painless resolution.

With our Browser Sharing for Customer Support, the click of a button allows customers to share their browsers with Amazon representatives. The representative can offer quick and efficient assistance without having access to the customer’s screen or computer. In most cases the customer gets immediate assistance, without any time-consuming installation needed.

Because the Amazon representative cannot view any other content on the customer’s desktop, this is much safer than traditional remote control solutions.

Our browser sharing tool is written in JavaScript and hosted on Amazon Web Services (AWS) EC2 servers. It uses the W3C DOM API to capture the DOM events. The REST API is hosted on AWS and saves interactions in an AWS RDS Database for auditing purposes.

Headquartered in Dublin, Ireland, and with more than 147,000 employees in 45 countries, Aptiv is a global technology company focused on helping create the next generation of active safety, autonomous vehicles, and smart cities.

With the ultimate goal of improving the safety of autonomous vehicles, Aptiv has a video database that presents a wide range of driving scenarios that a vehicle may encounter, including traffic signs, traffic lights, other vehicles, and pedestrians, all of which help to ensure vehicular safety for their customers.

Heightened highway and road safety concerns have introduced the need to expand the range of scenarios identified in their driving data.

Our Analysis of Autonomous Vehicle Testing Video model identifies and places boxes around salient features including overpasses, bridges, tunnels and tollbooths, and stores the labeled images in a database. The web app is connected to the database and visualizes entries from it. In order to navigate the web application, Aptiv users must first create an account and log in.

Our Analysis of Autonomous Vehicle Testing Video web app allows vehicle testing engineers access to enhanced driving scenarios that increase the safety of autonomous vehicles being manufactured by Aptiv and minimize loss of life and damage to property caused during vehicular crashes.

Our model is written in Python. Our web application is written using HTML, CSS, PHP, JavaScript and Bootstrap. The backend database is implemented with MySQL.

Based in Michigan, DRIVEN-4 offers its customers a competitive edge by providing them with innovative strategies, insights and proven implementations of integrated process and technology.

Original equipment manufacturers, or OEMs, are companies whose goods are used as components in the products of another company. In order to be competitive, OEMs must have plans in place that guide them to the most profitable results. These plans include budget information and hiring needs, as well as the storage of information that may be useful for future projects.

Our Product Development Portfolio and Planning software gives project managers the ability to forecast and track execution of annual product development budgets.

In addition, it provides them with the ability to insert the data required to build multiple forecasts and then generate graphs with just a click of a button. This enables them to visualize plans and choose the best possible one to build the product. It also gives them the ability to track the execution of an ongoing development and compare it with the forecast to ensure timely delivery of the product.

Our mobile app allows project managers to view updates or changes made to the project by other managers. It also allows employees to log the hours they have spent working on different projects throughout the week.

Our web app frontend uses Angular and the backend uses PTC ThingWorx, a platform to create business logic. Our mobile app is written in Xamarin and is available on both Android and iOS devices.

Dashboards are one of the best ways AppDynamics makes the data visible and understandable to the user. For example, it can let the company know if the site is getting too much traffic and unless more servers are put into use the site could slow considerably or even crash.

A company’s applications are crucial to attracting new customers or retaining existing ones. AppDynamics reminds them how their complex apps directly affect their businesses.

Our AppDynamics Platform Configuration Tool is utilized by Evolutio to deploy efficiently dynamic template dashboards that allow for quick and easy exportation across applications. It significantly reduces the amount of work required to create custom dashboards by using specially created templates and a simple-to-use interface assisting in dashboard deployment.

There also is an Amazon Alexa feature that interfaces with AppDynamics. Customers simply use their favorite Alexa device to ask questions about the performance of and health information for a given application that AppDynamics is monitoring and receive easy-to-understand results.

Our AppDynamics Platform Configuration Tool is written in JavaScript and communicates via a Java RESTful API. The Alexa skill is run via an AWS Lambda function.

Ford Motor Company is a multinational automotive manufacturer based in Dearborn, Michigan, employing 202,000 employees and producing a total of 6.6 million vehicles in 2017.

Ford’s Greenfield Labs in Palo Alto, California is made up of a cross-functional team of researchers who often acquire cutting edge hardware. Upon completion of each research project, these devices often find their way to the back of a drawer or a storage rack within the lab.

Our “SHARED” system, or Shared High-value Asset Reallocation Enablement Device Locker System, showcases these devices in such a way to encourage more efficient use. Unlike most lockers, which hide objects stored within, our system enables the process of walk- up, checkout, and basic tracking of the assets inside.

When an employee checks out or in an item, it is done using our cross-platform app that requires authentication and unlocks the locker. Locker reservations save the requested item at a specific date and time that is set by the user. Administrators keep track of devices, as well as monitor what devices are used.

There are three colors of LED lights that indicate the current status of a locker. Green is for available, yellow is for reserved, and red is for unavailable.

Our system utilizes a Raspberry Pi device to control a cluster of lockers. The corresponding software is written with React and React Native to support web, Android, and iOS platforms. The status of all lockers is displayed within the applications as well as within the physical locker itself.

Headquartered in Zeeland, Michigan, Herman Miller is one of the world’s largest producers of high-end office furnishings. Its products are used in modern workspaces around the globe.

When moving to a new location, workers can easily get lost trying to find their office, the breakroom or even the bathroom. Finding their way can also be challenging for visitors, including potential customers.

Our Office Navigation Using Augmented Reality app is an indoor AR navigation tool for clients and employees of Herman Miller.

Our app provides turn-by-turn instructions on how to maneuver through an unfamiliar building. It allows a building administrator to set up the environment by locating multiple destinations, as well as identifying the routes throughout the building. After selecting a destination, directional spheres, as shown on the right, will be displayed on the screen. Users then follow the spheres to lead them to their destination.

Our app uses computer vision via Apple’s ARKit and machine learning to detect features and landmarks within an enclosed space. It uses AR to display turn-by- turn instructions. It also has the potential to be useful in a variety of other settings, including hospitals, shopping malls and parking structures.

Our Office Navigation Using Augmented Reality app is built using ARKit, Placenote, AWS Cognito, AWS SageMaker and Pods. They work together to create a coherent user experience.

Meijer is a supercenter chain with roots firmly planted in Michigan. With 242 stores in six states, Grand Rapids-based Meijer is one of the biggest retailers in the nation.

Meijer is at the forefront of innovation with the early adoption of products such as shopping carts, automated checkout conveyor belts, and the mPerks digital-coupon program. mPerks enhances shoppers’ experiences by helping them earn discounts without the hassle of paper coupons.

Our Location-Based Personalized Shopping system, or aislePerks, improves the mPerks experience by notifying customers of deals and specials that may interest them while they are shopping. Customers who have the mPerks app on their mobile device have the option to download aislePerks.

When customers enter the store, they can open the app to see any relevant deals or specials. As they walk through the store, deals are recommended based on their previous purchases and where they spend the most time in the store. Deals are selected by customers and saved to their “Saved Deals” page where they can view their deal history.

Our system includes a companion dashboard website that Meijer employees utilize to view aislePerks usage data. This data includes statistics about top-selling products, the number of customers using it, and its effectiveness.

aislePerks utilizes APIs of Mist wireless networks to determine a customer’s exact location within a store. aislePerks is written in Java for Google Android devices and Swift for Apple iOS devices. Our backend system is hosted on Microsoft Azure. Our companion administrative dashboard is written in AngularJS.

Michigan State University is a public research institution founded in 1855. The goal of its Information Technology Services unit is to deliver and maintain effective technology resources for students, faculty and staff.

All students frequently take part in group projects. This is frustrating because finding the best way to communicate, setting up meeting times, and managing resources used for the project is very difficult.

Our Group Project Organization and Scheduling app reduces the stress of these tasks in a quick and simple way by allowing a user to create a group, easily add members, and connect with any services they find useful for their project.

The user simply clicks to create the group, invitations are sent out, and the tools selected are automatically configured. A team portal is revealed, displaying scheduled meetings, linked tools, and suggested tools the group may find useful.

The main feature of our app is the ability to schedule meetings based on a user’s linked calendar, whether it is Google Calendar or Outlook Calendar.

The scheduling algorithm finds available times from the individual calendars and proposes times which work for all or most members. After approval by a group member, the new meeting time is added to a list of scheduled meetings.

Our web app is built with the Serverless Framework and calls to Amazon API Gateway endpoints to invoke AWS Lambda functions written in Python. Our user interface is constructed with Vue.js and Bootstrap, served from a public Amazon S3 Bucket.

Since 1937, Michigan State University Federal Credit Union (MSUFCU) has offered financial services to members of the MSU and Oakland University communities. MSUFCU is the largest university-based credit union in the world, with nearly 900 employees and more than 265,000 members.

With the evolution of financial tools comes a need for a tailored, more personalized user experience. MSUFCU currently offers robust home banking and mobile apps for members to connect with their accounts, check their balances, transfer funds, move money, and more.

However, these apps have not provided a personalized budgeting solution to assist members with their financial planning needs. MSUFCU has enhanced its services with our AutoBudget Chatbot, a voice-enabled tool that helps members get their financial worlds in order.

Our AutoBudget Chatbot provides members with a suggested budget based on their income and spending patterns. The chatbot also allows members to categorize their transactions and provides a graphical overview of the member’s transactional trends. Members can set spending goals for any number of categories while the app helps them stay on track.

Our AutoBudget Chatbot is available on Alexa and Google Home devices, all web browsers, and mobile devices running iOS or Android.

The iOS app is built using Swift, the Android app is built with Java, and the website is primarily HTML, CSS and PHP. All applications call an API running Node.js.

Headquartered in Detroit, Urban Science is internationally renowned for providing data-driven, science-based solutions to problems in the automotive, health and retail industries.

The history of a vehicle is important to owners, sellers and buyers alike. Keeping an accurate and accessible record of a vehicle is a challenge, which often results in a disorganized group of documents stored in a vehicle’s built-in filing cabinet, its glove box.

Based on a vehicle’s unique Vehicle Identification Number (VIN), our VIN-Verse system leverages Urban Science’s existing data to provide a comprehensive history of a vehicle, including maintenance, both past and upcoming, repairs, recalls, and accidents.

VIN-Verse enables a vehicle’s owner to manage who can view their vehicle’s history. Repair facilities give better service when they are able to review a vehicle’s complete history. Potential buyers are more likely to purchase if they know the complete facts about a vehicle.

To augment Urban Science’s existing vehicle data, VIN-Verse includes a verified self-reporting system with which vehicle owners can enter their own repairs, thereby making the history complete.

For vehicle manufacturers, VIN-Verse provides a dashboard that visualizes trends in the service history of the vehicles that they make and sell.

As a responsive web app, VIN-Verse is accessible using any web browser on desktops or mobile devices.

The frontend of our VIN-Verse system is built with ASP.NET/C# and Angular 6. The backend is implemented using Microsoft SQL Server 2016.

Spectrum Health is a not-for-profit health care provider based in Grand Rapids, Michigan, consisting of 12 hospitals and over 140 service sites throughout the state.

Among its offerings are urgent care and ER services, MedNow, for virtual doctor appointments, and eVisit, an online questionnaire in which a health care provider provides a diagnosis and treatment plan.

Many patients are not aware of these services and will instead head directly to the emergency room. These visits are usually unnecessary, can be overly expensive for both patient and provider, and could lead to longer wait times for all.

To combat this, our Patient Training Tool is a chatbot app that recommends the appropriate Spectrum service based on the symptoms the patient is experiencing.

After a patient speaks to the Google Home device, describing their symptoms, our Patient Training Tool searches through a database and identifies the condition that best matches those symptoms. It relays the matching condition to the patient in easy- to-understand terms and recommends the appropriate service.

The recommended service for a condition may change based on patient choices. Once recommended a service, the patient accepts or rejects the recommendation, informing the Google Home device of the service that will be utilized. If an alternative service is repeatedly chosen for a condition, the Patient Training Tool changes its recommendation to match for all future users.

Our Patient Training Tool, compatible with both Google Home and Amazon Alexa, is written in JavaScript using the Node.js framework to query an Azure SQL database. Dialogflow is utilized for natural language processing.

To help its customers communicate more effectively, TechSmith assists in the creation of images and videos. Its flagship products, Snagit and Camtasia, are used by more than 30 million customers.

While TechSmith prides itself on providing a superior product to its customers, like all companies, problems sometimes arise.

For example, when an app crashes, customers can submit a report so that TechSmith can investigate. Often these reports don’t provide the best or enough information.

Our Internal Telemetry for TechSmith Products collects crash reports from multiple sources, gathering as much relevant information as possible. It then sends the combined crash report to a database, where an automatic notification is sent to the assigned TechSmith development team.

The tech team can then use an internal web portal to access the detailed crash report and associated information from each active product, together with access to a direct download of the report file. This, in turn, makes the diagnostic and repair process more streamlined, more efficient, and more to the customer’s satisfaction, as well as resulting in long-range improvements to a product.

Our Internal Telemetry for TechSmith Products is written in C#/C++ and incorporated into Snagit and Camtasia as a dll file. The web portal uses ASP.NET Core and is hosted on Microsoft Azure. The crash report data is stored in an SQL database in Azure. Two prototype applications, modeled after Snagit and Camtasia, are used to demonstrate the working Internal Telemetry.

United Airlines is a major United States airline, operating 4,600 flights a day out of more than 350 airports. Having properly trained personnel to maintain its fleet of aircraft is vital to the successful operation of each flight.

In order to maintain a staff of trained personnel, United’s Technical Operations division has 60 instructors that deliver some 700 courses to more than 7,000 employees a year.

Our Training Scheduling and Optimization System provides a web app and a mobile app that enable United’s schedulers to schedule, instructors to teach, and students to take courses around the country.

When the scheduler wants to schedule a course, the system automatically displays the available locations and instructors that can be assigned to that course. It allows instructors to track attendance of their classes and generate a completion roster at the conclusion of the course.

Our system includes a schedule optimization system. Using a given set of classes and a timeframe, the optimizer recommends an optimal schedule. This reduces the amount of time it takes for the scheduler to plan courses.

Instructors also can request time off through the system and supervisors have the ability to approve or deny these requests, as well as track their instructors’ time.

All of the functionality of our system is available using either our web app or our Apple iOS app with the exception of scheduling, which is exclusive to the web app.

Our Training Scheduling and Optimization System web app is built with ASP.NET Core, Angular 7 and a Microsoft SQL Server database. The Apple iOS app is written in Swift.

Volkswagen Group of America is the North American operation headquarters and subsidiary of the Volkswagen Group, which is comprised of 16 brands producing a variety of cars, motorcycles and commercial vehicles.

Volkswagen uses sophisticated robotic automation in direct manufacturing processes. By contrast, the indirect and administrative processes are yet to be automated on a large scale. With a workforce of over 600,000 employees, roughly 400,000 workers spend much of their day executing repetitive tasks.

Our Cognitive Enterprise Software Robots, referred to as softbots, take over manual work a human is performing on a computer and act as an intelligent co-worker.

For example, a Volkswagen Logistics Specialist spends several hours a day contacting suppliers to ensure all deliveries arrive on time. An employee must continuously look up shipments in a spreadsheet and compare the expected delivery date with the current date.

If the expected delivery date is five days or less away, the employee must send a reminder email to the supplier that the shipment has not arrived yet.

Using a technique called deep learning, our softbots learn a business process and execute it on the human’s behalf. The employee is now able to focus on more complex tasks.

Our softbots use a recurrent neural network to predict future clicks in a clickstream. Utilizing natural language processing, the softbots read emails and then perform the appropriate actions.