MongoDB Donates Laptops to Metropolitan School - Frankfurt

Cara Heimbaugh
December 8, 2022

MongoDB has teamed up with one of its customers, Cyntegrity, to provide 20 MacBook Air laptops to the Metropolitan School - Frankfurt (MSF). This step is part of an initiative to support the school’s students, including Ukrainian refugees who are studying there after leaving their home country due to instability created by war.

Cyntegrity is a German-based, risk-based quality management software company as well as a customer of MongoDB. Cyntegrity has a strong focus on education, and Artem Andrianov, the company’s CEO, has been a vehement supporter of those affected by the Russian invasion of Ukraine since the conflicts began in February 2022.

Support for Ukraine

Andrianov has positioned Cyntegrity to support displaced Ukrainians as well as those who actively work to aid in conflict zones. At the start of the conflict, Cyntegrity outlined their initial plan of support, including:

  • Hiring Ukrainian software developers, IT specialists, and software testers who have arrived in the European Union.

  • Making regular donations to Doctors Without Borders, which regularly treats victims in conflict zones.

  • Offering relocation packages to all employees affected by the conflict.

  • Developing The Butterfly Challenge, a program aimed at collaborating to make a difference and add value to the world around us all.

Knowing that Andrianov and Cyntegrity are always looking for ways to help displaced Ukrainian people, MongoDB employees Kevin Litwiak, Ladi Olusola, and Dayana Nikolova worked to procure the laptops and get them into the hands of people who could use them as learning tools.

Laptops for learning

Through collaboration with leadership at the MongoDB New York City headquarters, 20 laptops were collected and then distributed to MSF with the help of Andrianov, who worked behind the scenes to find an institution that would be able to use the laptops to support refugee aid efforts.

The laptops, pre-loaded with MongoDB, arrived at MSF with an aim to provide all students, including refugee-status students, with the technology they need to learn and to stay connected with family and friends as they enter a new life in Europe.

“This donation has been fabulous in improving our secondary school IT equipment,” said Tatyana Bogushevitch, head of marketing at Metropolitan School Frankfurt. “The laptops support the learning of all Secondary School students, including all our Ukrainian students. This donation is positively impacting the education of 350 students.”

Other ways to help Ukrainian refugees

Even as headlines focus on other topics, Ukrainian people are still being displaced and attempting to flee conflict for a stable life in other countries. The United Nations has reported that there are currently 5.2 million Ukrainian refugees who have fled their country and still need help.

Here are ways to support Ukrainian refugees across the globe:

  • Consider housing a refugee. Currently, most Ukrainian refugees have relocated largely to Poland, Germany, Romania, Moldova, Hungary, and Slovakia. You can register your interest in housing refugees by visiting Homes for Ukraine.

  • You can find opportunities to volunteer in local Ukrainian efforts by visiting Refugee Council USA.

  • You can donate to a variety of nonprofits working to support refugees as they migrate and after they settle. Visit the Save Ukraine website to learn more.

About Metropolitan School - Frankfurt

Metropolitan School - Frankfurt is an IB World School with a clear international profile offering an outstanding, inspirational education. Students are inspired to become well-rounded, independent thinkers equipped with the skills, knowledge, and understanding to master the challenges of the future.

Metropolitan School - Frankfurt has developed multiple initiatives to support Ukrainians as they are welcomed in Germany. Through its school kitchen — in cooperation with Werksküchen Catering and the volunteer time of teachers — it provides daily food supplies for up to 200 refugees per day, including snacks, fruit, and a hot meal for the arrivals after their long journey. The school also provides some daily necessities for refugees.

If you would like to assist in these efforts by the school, you can:

About Cyntegrity

Cyntegrity has created a fully automated, centralized risk management cloud platform for improving patient safety, data integrity, and clinical trial transparency. Its vision is based on three main pillars:

  • Control of risks, performance, and data quality

  • Application of the latest data mining technologies to gain new knowledge about the data

  • Follow up on the identified risks until resolution and development of a risk-based strategy for a focused use of resources are available for a trial

Cyntegrity’s intelligent RBQM solution has evolved in progressive ways over time and opened the opportunity for BioPharma to optimize the clinical trial operations with the GCP-driven workflow, predictive analytics, and intelligence of the specialized algorithms.

About MongoDB

MongoDB helps companies get their ideas to market faster with a developer data platform built on the leading modern database. Support transactional, search, analytics, and mobile use cases while using a common query interface and the data model developers love.

Headquartered in New York, with offices across North America, Europe, and Asia-Pacific, and a workforce that is now mostly remote, we are close to where you do business. MongoDB has more than 37,000 customers in more than 100 countries. The MongoDB database platform has been downloaded over 300 million times and there have been more than 1.5 million MongoDB University registrations.

You can try MongoDB Atlas for free by registering today.

MongoDB is also working to help support the people of Ukraine during this time. For those in Ukraine, MongoDB is offering free Atlas credits to a project called Unterkunft Ukraine, which helps refugees fleeing Ukraine find temporary housing. The company is also making MongoDB Atlas credits available for organizations that are developing software projects to help alleviate the current humanitarian crisis in Ukraine.

If you are working on a project that provides assistance in the region, fill out the application to apply for free MongoDB Atlas credits.

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Telco Scaling Strategies: Modernizing Business Support Systems for Flexible Revenue Growth

Consumers and businesses alike are driving huge demand for innovative telecommunications technology that tests the limits of monolithic, traditional business support system (BSS) architecture. The competition is fierce within the industry, tipping telcos to differentiate their businesses with fresh digital services like low latency mobile apps, ultra-fast streaming services, virtual reality, and IoT solutions. The worldwide adoption of 5G is driving the change, bringing with it the need to simplify architectures to accommodate the complexity of modern 5G networks, which require multiple assurances, orchestration, provisioning, and charging functions to aggregate data effectively and manage services. Alongside the development of headline-grabbing technologies, telecommunications enterprises are also busy building increasingly customer-centric experiences. 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The global telecommunications billing and revenue management market size reached $13 billion in 2019, and is forecast to expand at a compound annual growth rate of 11.6% to more than $31 billion between 2020 and 2027, according to research prepared by Forrester Research, Inc. Core, consolidated customer data act as an enabler for many other related solutions that share the need for a solid, reliable record of customer core data. The common factor for payment processing, customer loyalty programs, service provisioning, service usage, and finally billing generation, is customer core data. Figure 1:   Customer centricity in billing Like many large corporations, CSPs are often made up of siloed application stacks broken out by product area, such as VoIP, mobile, cable, and so on. Since customers use products and services that exist within multiple silos, changes to customer data need to be propagated to multiple systems. The lack of a single, consolidated view is often a result of historical mergers and acquisitions. Without the ability to analyze data within a single view, opportunities to capitalize on data analytics to uncover cross-selling and up-selling opportunities are lost. Furthermore, enterprises managing multiple parallel billing implementations and the systems’ associated data synchronization infrastructure can incur hefty costs and architectural complexity. The best way to address these challenges is to modernize core customer data systems to have a single view of customers and their billing-related data. At the heart of these modernization projects is the move to a new platform. Product catalog simplification and the hybrid cloud approach Akin to the global-scale retailers with some of the most complex product offerings around, telcos have a complicated array of product offerings that require CPQ processes to be able to combine offerings. Whether a telco is combining handsets, tariffs, warranties, add-ons or promotions, managing data in a streamlined and scalable infrastructure is a crucial modernization strategy. What’s more, telcos must deliver personalized and real-time shopping experiences to customers across web, mobile, phone, and in-store platforms to stay competitive. The modernization and simplification of a telco’s product catalog architecture can quickly turn into a complicated mess when certain existing legacy systems must stay in place. Making one swift rip-and-replace move can be a big risk with costly implications for long-term transformation projects. Today, more and more successful modernization strategies are achieving omnichannel implementation and multi-play services goals by migrating incrementally. This enables telcos to set the stage for a successful migration, at the right pace for the company. A key part of a modernization strategy is often a move from a self-managed, on-premise architecture to a cloud-based one. Initially, a hybrid cloud strategy is often more cost-efficient, and acts as an important stepping stone in any enterprise’s digital transformation journey. The limits of existing legacy systems inhibit telcos’ ability to scale and grow. As the telecommunications industry reimagines how to apply new technology in the digital-first world, heavier reliance on the public cloud is delivering operational and competitive advantages. But for an industry with complex legacy infrastructure and loads of personal data, moving every workload to the cloud isn’t feasible yet. Through implementation projects with telcos around the world, Tech Mahindra has proven that a central commercial catalog, integrated with the right legacy technical catalogs and BSS stacks, improves time-to-market for launching multiple bundles and offers that are still processed and billed to a single customer. The benefits of this hybrid approach are immediately apparent: Faster time-to-market Increase in sales Improved customer satisfaction Reduced handling time Less fallouts, errors, and training time Through Tech Mahindra’s BlueMarble Commerce solution, underpinned by MongoDB, telecommunications enterprises are quickly overhauling their omnichannel strategy without overhauling their business or existing systems. A true end-to-end omnichannel multi-play solution for telcos, BlueMarble automates channel sales, order management, CPQ (product configuration, pricing, and quoting) and fulfillment, including reverse logistics. Combatting the complexity of BSS modernization By putting the needs of their customers first, telco, cable, and media service providers are building a bridge toward seamless and consistent experiences for both digital and physical user experiences. BlueMarble Commerce was built with this principle in mind. 

With its ability to connect with legacy systems using custom-built adaptors and APIs, BlueMarble combines multiple channels in a uniform, seamless manner, helping to simplify telco modernization. MongoDB delivers a multi-cloud database service built for resilience, scale, and the highest levels of data privacy and security. This is critical when building a platform that enables a cohesive, integrated suite of offerings capable of managing modern data requirements for building applications in a microservices framework without sacrificing speed, security, developer experience, and the ability to scale. These were the primary reasons to choose MongoDB for the BlueMarble platform. With these features of MongoDB Atlas, BlueMarble acts as a federated, overlay solution that masks the complexity of legacy, and enables the creation of new digital functionalities by integrating new digital architectures with existing legacy, eliminating the need to build new bespoke applications. In conclusion, as telcos compete to provide the exciting new technologies driving change in the industry, they must not lose sight of the customer, or providing customer value. Tech partners like MongoDB and Tech Mahindra are leading the charge in supplying cloud-native, microservices-based architectures in business support systems. This essay appears in the new TM Forum report: Evolving BSS for future services. Access the full report here .

December 8, 2022
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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