2020 MongoDB Innovation Awards

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June 10, 2020 | Updated: December 20, 2021
#Innovation Awards

The MongoDB Innovation Awards honor projects and people who dream big. They are presented annually to celebrate the groundbreaking use of data to build compelling applications, and the creativity of professionals expanding the limits of technology with MongoDB. This year the company received entries across dozens of industries, from cloud-native start-ups to Fortune Global 500 companies.

We are thrilled to announce the nine winners who are being honored during MongoDB.live June 9th and June 10th.

Partner of the Year

Google Cloud views the database as an essential building block of cloud infrastructure. The partnership between Google Cloud and MongoDB gives customers all the benefits of a modern database service in a cloud-native way. More and more of Google Cloud’s customers are choosing to run MongoDB Atlas for a variety of needs, such as managing large-scale product catalogs of popular e-commerce websites, building great customer experiences by unifying disparate pieces of data, or building modern global web and mobile applications. Learn more about the partnership here.

Data For Good & Innovator of the Year

dacadoo offers Life & Health insurance platform technology solutions for digital health engagement, health risk quantification (B2B) and management of non-communicable diseases (NCDs), which include heart disease, stroke, cancer, diabetes and chronic lung disease. Collectively, these diseases are responsible for almost 70% of all deaths. Dacadoo developed its Health Score Engagement Platform to help customers gain better insight into their overall health and make informed decisions about modifiable risk factors, such as exercise, nutrition, smoking, drinking, and stress - all of which contribute to chronic diseases. 80-85% of all people who passed away globally because of COVID-19 - had one or more chronic diseases.

Industry Transformation

Spathe Systems provides innovative solutions for Cyber Security, Cloud Enterprise, DevSecOps, and IT network infrastructure. Its Global Logistics Infrastructure has turned the Department of Defense logistics planning on its face with an application that revolutionizes the way estimates are calculated. The tool has already reduced the time it takes to calculate basic food and water (Class I: Subsistence) requirements by over 1000% and enabled a global view of all requirements - the first of its kind. It can also be used to plan Humanitarian Assistance / Disaster Relief operations.

From Batch to Real-Time

Nationwide Building Society is the world's largest building society as well as one of the largest savings providers and a top-three provider of mortgages in the UK. It is also a major provider of current accounts, credit cards, ISAs and personal loans. In 2018, Nationwide committed to a £1.4B investment in technology to focus on building a better customer experience. A major part of this initiative was to speed up access to transaction data, increase resiliency and deliver near-real-time data from backend systems to channel applications.

Unbound

7-Eleven is the premier name and largest chain in the convenience retailing industry. Based in Irving, Texas, 7-Eleven operates, franchises and/or licenses more than 70,000 stores in 17 countries, including 11,800 in North America. As part of 7-Eleven’s store technology modernization initiative, they deployed a custom mobile device across 10,000 stores in the U.S. and Canada that scans and manages inventory. The device will soon enable store employees to ring up customers directly from mobile devices.

Jackpot

Toyota Connected North America (TCNA) was established in 2016 to drive Toyota’s global efforts for an intelligent mobile society. With big data collected from vehicles and analyzed on a cloud platform, TCNA humanizes the driving experience by freeing customers from the tyranny of technology via secure, seamless and contextual services, elevating the customer experience while benefiting dealers, distributors, and partners. In August 2019, it launched its homegrown Telematics Services Platform (TSP) which leverages a special piece of hardware available in select new Toyota and Lexus vehicles to enable safety and convenience services such as collision detection, roadside assistance, destination assist and stolen vehicle support. MongoDB has reduced TCNA’s annual costs by 20% and given its developers time back so they keep up with evolving consumer expectations and continue to innovate.

Cloud-First

Pizza Hut is America’s original pizza company, founded in 1958 in Wichita, Kansas by Dan and Frank Carney. The brand is known for iconic menu items like the Original Pan and Original Stuffed Crust pizzas—and prides itself on being the pizza for Pizza Lovers. Pizza Hut launched its Customer 360 program to create a central platform for collecting data from multiple sources including mobile apps, online orders, phone orders, surveys, loyalty program info, etc. In addition, their data platform has become an instrumental part of their digital transformation imperative acting as the "backbone" of everything they do - including tracking new projects and use cases like driver tracking, CRM, eCommerce optimization, mobile app enhancement, and more.

Savvy Start-UP

Zinc is an eCommerce startup and full-service consultancy that builds SaaS products and tools for listing creation, inventory management, catalog building, repricing, brand pricing enforcement, and order fulfillment. They have developed a suite of software used to power the eCommerce platforms of small businesses, public companies and resellers,and are processing $250 million in sales per year. Zinc selected MongoDB as the data component for its core API which provides automation for all stages of the digital purchasing process on a variety of large online retail sites.

Certified Professional of the Year

Each year we recognize one of our Certified Professionals whose MongoDB expertise has helped them advance their career and give back to the community. The 2020 MongoDB Certified Professional of the Year is Michael Schreier, Software Engineer, Charles Schwab.

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Retrieval Augmented Generation for Claim Processing: Combining MongoDB Atlas Vector Search and Large Language Models

Following up on our previous blog, AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases , we’ll pick up the conversation right where we left it. We discussed extensively how Atlas Vector Search can benefit the claim process in insurance and briefly covered Retrieval Augmented Generation (RAG) and Large Language Models (LLMs). MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More One of the biggest challenges for claim adjusters is pulling and aggregating information from disparate systems and diverse data formats. PDFs of policy guidelines might be stored in a content-sharing platform, customer information locked in a legacy CRM, and claim-related pictures and voice reports in yet another tool. All of this data is not just fragmented across siloed sources and hard to find but also in formats that have been historically nearly impossible to index with traditional methods. Over the years, insurance companies have accumulated terabytes of unstructured data in their data stores but have failed to capitalize on the possibility of accessing and leveraging it to uncover business insights, deliver better customer experiences, and streamline operations. Some of our customers even admit they’re not fully aware of all the data in their archives. There’s a tremendous opportunity to leverage this unstructured data to benefit the insurer and its customers. Our image search post covered part of the solution to these challenges, opening the door to working more easily with unstructured data. RAG takes it a step further, integrating Atlas Vector Search and LLMs, thus allowing insurers to go beyond the limitations of baseline foundational models, making them context-aware by feeding them proprietary data. Figure 1 shows how the interaction works in practice: through a chat prompt, we can ask questions to the system, and the LLM returns answers to the user and shows what references it used to retrieve the information contained in the response. Great! We’ve got a nice UI, but how can we build an RAG application? Let’s open the hood and see what’s in it! Figure 1: UI of the claim adjuster RAG-powered chatbot Architecture and flow Before we start building our application, we need to ensure that our data is easily accessible and in one secure place. Operational Data Layers (ODLs) are the recommended pattern for wrangling data to create single views. This post walks the reader through the process of modernizing insurance data models with Relational Migrator, helping insurers migrate off legacy systems to create ODLs. Once the data is organized in our MongoDB collections and ready to be consumed, we can start architecting our solution. Building upon the schema developed in the image search post , we augment our documents by adding a few fields that will allow adjusters to ask more complex questions about the data and solve harder business challenges, such as resolving a claim in a fraction of the time with increased accuracy. Figure 2 shows the resulting document with two highlighted fields, “claimDescription” and its vector representation, “claimDescriptionEmbedding” . We can now create a Vector Search index on this array, a key step to facilitate retrieving the information fed to the LLM. Figure 2: document schema of the claim collection, the highlighted fields are used to retrieve the data that will be passed as context to the LLM Having prepared our data, building the RAG interaction is straightforward; refer to this GitHub repository for the implementation details. Here, we’ll just discuss the high-level architecture and the data flow, as shown in Figure 3 below: The user enters the prompt, a question in natural language. The prompt is vectorized and sent to Atlas Vector Search; similar documents are retrieved. The prompt and the retrieved documents are passed to the LLM as context. The LLM produces an answer to the user (in natural language), considering the context and the prompt. Figure 3: RAG architecture and interaction flow It is important to note how the semantics of the question are preserved throughout the different steps. The reference to “adverse weather” related accidents in the prompt is captured and passed to Atlas Vector Search, which surfaces claim documents whose claim description relates to similar concepts (e.g., rain) without needing to mention them explicitly. Finally, the LLM consumes the relevant documents to produce a context-aware question referencing rain, hail, and fire, as we’d expect based on the user's initial question. So what? To sum it all up, what’s the benefit of combining Atlas Vector Search and LLMs in a Claim Processing RAG application? Speed and accuracy: Having the data centrally organized and ready to be consumed by LLMs, adjusters can find all the necessary information in a fraction of the time. Flexibility: LLMs can answer a wide spectrum of questions, meaning applications require less upfront system design. There is no need to build custom APIs for each piece of information you’re trying to retrieve; just ask the LLM to do it for you. Natural interaction: Applications can be interrogated in plain English without programming skills or system training. Data accessibility: Insurers can finally leverage and explore unstructured data that was previously hard to access. Not just claim processing The same data model and architecture can serve additional personas and use cases within the organization: Customer Service: Operators can quickly pull customer data and answer complex questions without navigating different systems. For example, “Summarize this customer's past interactions,” “What coverages does this customer have?” or “What coverages can I recommend to this customer?” Customer self-service: Simplify your members’ experience by enabling them to ask questions themselves. For example, “My apartment is flooded. Am I covered?” or “How long do windshield repairs take on average?” Underwriting: Underwriters can quickly aggregate and summarize information, providing quotes in a fraction of the time. For example, “Summarize this customer claim history.” “I Am renewing a customer policy. What are the customer's current coverages? Pull everything related to the policy entity/customer. I need to get baseline info. Find relevant underwriting guidelines.” If you would like to discover more about Converged AI and Application Data Stores with MongoDB, take a look at the following resources: RAG for claim processing GitHub repository From Relational Databases to AI: An Insurance Data Modernization Journey Modernize your insurance data models with MongoDB and Relational Migrator

April 18, 2024