Accenture is a professional services company that solves their clients’ problems by providing services in strategy, consulting, digital, technology and operations. Accenture’s iDefense provides timely, relevant and actionable cyber threat intelligence to the largest organizations in the world.

Malware is software that is intended to damage or disable computers. Accenture iDefense maintains a large database with more than 260 million samples of malware. Before being added to this database, each new malware sample must be analyzed to determine what it does and how it works.

iDefense uses various software modules to analyze malware. However, the number of malware samples is growing so fast that it cannot be processed manually.

Our Automated Malware Analysis Platform (AMAP) is a web app that combines iDefense’s various malware analysis modules to process malware automatically at the speed and scale required.

For each batch of malware, users choose which iDefense malware analysis modules to apply. Our dashboard displays the status of malware currently being processed and the status of the overall system. Malware is also processed automatically by our system when users are not actively using it.

Our Automated Malware Analysis Platform runs as a web-based application with a backend written in Python. The underlying database holding malware samples and analysis output is mongoDB.

Amazon is an e-commerce and cloud computing company based in Seattle. Accounting for 1 in 3 online shopping transactions in North America, Amazon is the world’s largest online retailer.

In the third quarter of 2017, Nielsen reported that 40% of Americans listened to a podcast. Despite having a large audience, podcast producers have few opportunities to profit from their work.

Our Amazon Marketplace Podcast Earnings Detection system (AMPED) provides podcast producers an additional source of revenue by finding products related to their content. The results are displayed on a dashboard for producers with an audio player integrated with the items.

For example, if a podcast producer is doing a tech review on the latest MacBook Pro, AMPED searches the Amazon marketplace for the latest MacBook Pro, and provides an image and link for the listener to buy it.

AMPED finds relevant products by first transcribing the podcast’s audio content to text. Next AMPED comprehends the meaning of the text. Finally, AMPED searches the Amazon marketplace for the most relevant products.

Products appear in the bottom of the app as the podcaster mentions them. Listeners can choose to purchase the products in Amazon’s app or website by clicking on the picture of the product.

AMPED is built with Python and is hosted on AWS Lambda serverless architecture. AMPED uses Amazon Transcribe for speech to text, Amazon Comprehend for natural language processing, and Product Advertising API to find products.

DRIVEN-4 is focused on driving competitive advantage for their customers by providing strategies, insights and proven implementations of integrated process and technology to deliver products and services to market.

Our 2020 Business in a Box system shows how a connected product platform can bridge the gap between product design and consumer use.

Our system comprises both manufacturer and consumer platforms to provide the insight needed to improve product design and consumer relationships while at the same time providing consumers with an enhanced experience for improving their daily lives.

Our web-based manufacturer portal provides for domain-specific configuration to create a dashboard for users in various job roles to visualize data and perform actions specific to their fulfillment of business and consumer needs. The built-in device simulator allows manufacturers to digitally simulate connected products before they are produced.

Our showcase of the platform demonstrates its use through the eyes of a mattress manufacturer and its consumers. When placed in the mattress, our connected device uses accelerometer and temperature sensors to transmit sleep cycle information to the platform.

The end-consumer experiences the platform through a mobile app that provides sleep analysis, suggestions to improve sleep quality and augmented reality visualizations for servicing their device.

Our web portal is written in CSS, HTML and ReactJS. Our device simulator is written in Java. Our mobile app is built in React Native for Apple iOS devices.

Founded in 1908, General Motors designs and manufactures a wide variety of vehicles that meet the needs and expectations of drivers around the globe.

GM’s continued success requires its software developers to integrate an expanding number of software applications into their workflow. To do so, they use several unique applications to design, develop, test, and maintain software. Having to frequently maneuver between these applications hinders the development process.

To enable collaboration and teamwork, GM developers use Microsoft Teams, a chat-based workspace. Developers are members of a channel, which is specific to their team where they can chat, manage files, and access external applications.

Our DevBot for Microsoft Teams system (Plato) extends the functionality of Microsoft Teams through the creation of three separate but cohesive applications.

Our first Plato app is an artificially intelligent chatbot that enables team members to create and manage virtual machines and test cases. A graphical web dashboard embedded into Microsoft Teams in the form of a tab is our second extension. Our third app is a Microsoft Teams connector that enables the team to configure regular notifications about the system’s activity.

GM developers using our apps have full control over their resources and can monitor resource allocation, run tests and update the status of virtual machines all within a familiar Microsoft Teams environment.

Our Plato system is designed with the Microsoft Bot Framework, Microsoft LUIS, C# with .NET, AngularJS, HTML and CSS, and is deployed into Microsoft Teams.

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

Every Herman Miller chair is highly configurable to match the unique needs of each user, ensuring proper ergonomic support in any office setting.

Adjust is our augmented reality (AR) chair adjustment app for owners of Herman Miller chairs.

Adjust automatically identifies the model of a chair using an iPhone’s camera along with computer vision and machine learning.

Once the chair model is known, Adjust displays an interactive 3D model and guides the user through an ergonomic adjustment session by highlighting the relevant chair parts.

Tapping on any highlighted part presents an instructional video and additional adjustment tips. For returning users, the application menu features a gallery option, which enables users to interact with a variety of Herman Miller chairs.

Our Adjust app reduces the need for in-person training sessions, allows for day-two training and eliminates the need for paper reference texts. Chair owners can locate any adjustment instantly using our built-in search functionality.

Adjust is a native iOS app written in Swift. Object recognition is performed using Microsoft Custom Vision and CoreML. Interactive 3D chairs are rendered using Apple’s SceneKit.

Meijer is a large supercenter chain located in the Midwest. With 237 supercenters in six states, Meijer is one of the largest retailers and private companies in the nation.

From shopping carts to self-checkouts, Meijer always leads on the frontline of innovation to ensure Meijer guest satisfaction.

Our Personal Shopping Assistant app (Thrifty) continues that tradition by enabling a guest to ask Thrifty anything they would ask an in-store Meijer team member. In addition, a guest can navigate through coupons and check competitor pricing.

Meijer guests interact with Thrifty using either text or voice. If Thrifty isn’t sure what they mean, it responds with a list of possibilities from which a guest can select.

After an item is selected, Thrifty tells a guest where it’s located in the store, how much it costs, and if there are any coupons available from Meijer’s rewards program, mPerks. Guests can clip all coupons to their mPerks account and display a single barcode at checkout to apply all of their savings.

Thrifty also informs guests of item prices at competitors like Target or Walmart.

If an item is missing from the shelf, Thrifty notifies a Meijer team member to check for more.

Thrifty is built using the Microsoft Bot Framework and is hosted on Microsoft Azure cloud services. It uses Microsoft LUIS for natural language processing and supports both Apple iOS and Google Android devices.

Michigan State University (MSU) is the nation’s pioneer land-grant university and is a world- renowned research university, with top-tier academic and service-learning programs.

Our Classroom Engagement System (Pulse) provides a simple and convenient interface that enables extended engagement between course instructors and students. Pulse is designed around attendance, communication and analytics.

Attendance is made easy. Students simply respond to a notification that is sent to their mobile device as they enter a classroom. The student is then transported into the app to access the class material.

Pulse allows students and instructors to access multiple classroom resources on a compact and intuitive platform. Pulse offers a class overview page with course information, as well as a class forum where students and instructors can hold discussions.

Pulse’s quizzes replace paper quizzes, thereby saving paper and time. The quizzes can be taken during class or at any time. Progress bars and timers are available to enhance the user experience.

Analytics are collected and displayed by our instructor- side web app for evaluation in real-time. Based on student responses to questions, instructors can vary the content in their lectures.

Our Pulse mobile apps are native apps for Apple iOS and Google Android developed with Swift and Kotlin, respectively. Our web apps are created with Vue.js. The backend interface consists of several platforms from Amazon Web Services.

Founded in 1937, Michigan State University Federal Credit Union (MSUFCU) is the largest university- based financial credit union in the world. With over 246,000 members around the globe, customer service is a high priority for MSUFCU.

Our Digital Assistant and Personal Financial Coach expands the capabilities of MSUFCU’s existing apps for Apple and Android devices. In addition, it offers services through a web app, an Amazon Alexa app, Facebook Messenger and iMessage.

Using a chatbot interface, MSUFCU members can get answers to questions to everything from “What’s my account balance?” to “Can I afford to buy a laptop?” Our bot also offers financial planning services. If a member cannot afford a laptop, for example, our Digital Assistant and Personal Financial Coach will suggest a plan for a member on how to save for one.

For MSUFCU members who prefer a hands-free approach to banking, our Alexa skill can respond to the same voice commands as the chatbot interface. Our Apple and Android apps, as well as Facebook Messenger and iMessage, all accept the same voice commands.

Our Digital Assistant and Personal Financial Coach Apple iOS app is written in Swift, and the Google Android app in Java. Our Alexa skill is written in JavaScript. The natural language processing is handled using DialogFlow. All six of the available apps contact a SQLite database hosted on a server.

The Phoenix Group (TPG), an Ingram Micro company, is one of the largest distributors of credit card terminals. They offer customers a vast selection of devices from all major manufacturers and take pride in having outstanding customer service.

Customer service associates at TPG frequently reference paper device manuals to provide customers with helpful information. With hundreds of manuals to reference, TPG associates need a better way to manage and access them.

Our Customer Service System enables TPG to convert paper manuals to a digital format using optical character recognition (OCR) technology. An associate easily adds new manuals into the system by uploading images of its pages. Once added to our system, a specific manual can be found quickly by searching for it and viewed conveniently as an eBook.

Our Customer Service System enables TPG’s customer service associates to maintain their outstanding responsiveness despite a growing lineup of carried devices.

Additionally, our Customer Service Chatbot provides an informative resource for customers visiting TPG’s website. Customers can converse with the chatbot to ask about TPG and the devices it offers.

Our Customer Service System is a Windows WPF application written in C# that communicates with our custom remote server where the digital manuals are stored. Our Customer Service Chatbot is implemented using Google Dialogflow and is written in AngularJS.

Proofpoint is a leading next generation cybersecurity firm that provides comprehensive cloud-based security solutions to protect organizations from advanced threats and attacks that target email, mobile apps and social media.

Every day Proofpoint blocks threats in more than 600 million emails, 7 million mobile apps and hundreds of thousands of social media accounts. This volume of threats makes it increasingly more difficult to rely solely on human analysis.

Our Next Generation Malware Analysis Platform provides a comprehensive tool for analyzing malware samples automatically and quickly.

Our system first analyzes malware using a basic set of tools. Depending on the results of this initial analysis, our system determines whether or not more in-depth analysis should be done.

Malware is classified and identified using something called a signature. Our system clusters malware based on similarity, thereby enabling Proofpoint analysts to generate signatures more efficiently.

Analysts use our dashboard to visualize the results of malware analysis. Additionally, they can view malware of interest and apply filters.

Our Next Generation Malware Analysis Platform uses Bootstrap. A RESTful API based on NodeJS communicates with the MongoDB Database. Cuckoo, YARA, ClamAV and Suricata are utilized for the malware analysis tools. All of our tools run on virtual machines handled by an ESXi Hypervisor.

Rook Security, based out of Indianapolis, Indiana, is a leader in the managed detection and response service, providing IT security solutions to clients around the world.

Rook’s Force Platform protects their clients from malicious attacks by analyzing log data about client devices and appliances such as critical application servers. This log data is collected by Rook’s software agents running remotely on client host computers.

Rook’s diverse client base is growing quickly. In order to keep up with this growth, Rook’s log data agents must support various operating systems, and be easy to install and configure on client computers.

Our Endpoint Data Monitoring and Analysis Agent sends the digital defenders at Rook their clients’ system and application logs from client machines running Microsoft Windows, Apple MacOS or Linux operating systems. This enables client computers to continue working seamlessly and securely with our agent running in the background.

In addition to the agent, an extension of the Force Platform enables Rook analysts to deploy and configure agents as well as view agent health metrics. The ability of our agent to be managed remotely by Force Platform administrators ensures that any infringing anomalies are acted upon quickly.

Our Endpoint Data Monitoring and Analysis Agent system is written in Go for Windows, Mac, and Linux. The logs are stored using Amazon Simple Storage Service (S3). Our extension of the Force Platform is written in Python using the Django web framework. The front end utilizes ReactJS and Redux.

Symantec is a global leader in providing security and information management solutions. Their customers range from consumers and small businesses to large global organizations.

Symantec’s flagship authentication solutions include Symantec VIP, a multifactor authentication solution that enables businesses to secure access to networks and applications without affecting productivity.

Our system detects security threats by analyzing user authentication patterns and visualizing the results using Splunk and Elasticsearch, Logstash and Kibana (ELK).

Our custom Splunk and ELK apps enable VIP customers to view and analyze various operational and security trends in near real time. Both apps ingest and visualize VIP authentication log data pulled from Symantec servers using their VIP Reference Client.

A VIP customer initially configures their Splunk or ELK apps with their VIP account certificate. The apps open directly to pre-built dashboards, which show operational data and security trends. Users can add custom charts and panels to their dashboard.

While our dashboards do help to visualize data trends and behavior, security analysts may not always be viewing the dashboards. To prevent an analyst from missing an important security event, both the Splunk and ELK apps alert analysts via alternate methods when threats are detected.

Our Splunk app is written using the Splunk Enterprise software and dashboards are created with Splunk Processing language. Our ELK app runs on an Amazon Machine Image hosted on Amazon Web Services.