Applications
Customer stories, use cases, and experiences of MongoDB
The Journey of MongoDB with COVESA in the Connected Vehicle Landscape
There’s a popular saying: “If you want to go fast, go alone; if you want to go far, go together.” I would argue The Connected Vehicle Systems Alliance (COVESA) in partnership with their extensive member network, turns this saying on its head. They have found a way to go fast, together and also go far, together. COVESA is an industry alliance focused on enabling the widespread adoption of connected vehicle systems. This group aims to accelerate the development of these technologies through collaboration and standardization. It's made up of various stakeholders in the automotive and technology sectors, including car manufacturers, suppliers, and tech companies. COVESA’s collaborative approach allows members to accelerate progress. Shared solutions eliminate the need for individual members to reinvent the wheel. This frees up their resources to tackle new challenges, as the community collectively builds, tests, and refines foundational components. As vehicles become more connected, the data they generate explodes in volume, variety, and velocity. Cars are no longer just a mode of transportation, but a platform for advanced technology and data-driven services. This is where MongoDB steps in. 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 MongoDB and COVESA As the database trusted for mission-critical systems by enterprises such as Cathay Pacific , Volvo Connect , or Cox Automotive ; MongoDB has gained expertise in automotive, along with many other industries, building cross-industry knowledge in handling large-scale, diverse data sets. This in turn enables us to contribute significantly to vehicle applications and provide a unique view, especially in the data architecture discussions within COVESA. MongoDB solutions support these kinds of innovations, enabling automotive companies to leverage data for advanced features. One of the main features we provide is Atlas Device SDKs : a low-footprint, embedded database directly living on ECUs. It can synchronize data automatically with the cloud using Atlas Device Sync , our data transfer protocol that compresses the data handles conflict resolution, and only syncs delta changes, making it extremely efficient in terms of operations and maintenance. VSS: The backbone of connected vehicle data An important area of COVESA’s work is the Vehicle Signal Specification (VSS). VSS is a standardized framework used to describe data of a vehicle, such as speed, location, and diagnostic information. This standardization is essential for interoperability between different systems and components within a vehicle, as well as for external communication with other vehicles and infrastructure. VSS has been gaining more and more adoption, and it’s backed by ongoing contributions from BMW, Volvo Cars, Jaguar LR, Robert Bosch and Geotab, among others. MongoDB’s BSON and our Object-oriented Device SDKs uniquely position us to contribute to VSS implementation. The VSS data structured maps 1 to 1 to documents in MongoDB and objects in Atlas Device SDKs , which simplifies development, and speeds up applications by completely skipping any Relational Mapper layer. For every read or write, there is no need to transform the data between relational and VSS. Our insights into data structuring, querying, and management can help optimize the way data is stored and accessed in connected vehicles, making it more efficient and robust. Where MongoDB contributes MongoDB, within COVESA, finds its most meaningful contributions in areas where data complexities and community collaboration intersect. First, we can share insights into managing vast and varied data emerging from connected vehicles generating data on everything from engine performance to driver behavior. Second, we have an important role in supporting the standardization efforts, crucial for ensuring different systems within vehicles can communicate seamlessly. Our inputs can help ensure these standards are robust and practical, considering the real-world scenarios of data usage in vehicles. Some of our contributions include an Over the Air update architectural review presented at Troy COVESA’s AMM in October 2023; sharing insights about the Data Middleware PoC with BMW; and weekly contributions at the Data Expert Group. You can find some of our contributions on COVESA’s Wiki page . In essence, MongoDB's role in COVESA is about providing a unique perspective from the database management point of view, offering our understanding from different industries and use cases to support the developments towards more connected and intelligent vehicles. MongoDB, COVESA, and AWS together at CES2024 MongoDB’s most recent collaboration with COVESA was at the Consumer Electronics Show CES 2024 during which MongoDB’s Connected Vehicle solution was showcased. This solution leverages Atlas Device SDKs, such as the SDK for C++ , which enables local data storage, in-vehicle data synchronization, and also uni and bi-directional data transfer with the cloud. Below is a schematic illustrating the integration of MongoDB within the software-defined vehicle: Schema 1: End to end integration for the connected vehicle At CES 2024, MongoDB also teamed up with AWS for a compelling presentation, " AI-powered Connected Vehicles with MongoDB and AWS " led by Dr. Humza Akhtar and Mohan Yellapantula, Head of Automotive Solutions & Go To Market at AWS. The session delved into the intricacies of building connected vehicle user experiences using MongoDB Atlas. It showcased the combined strengths of MongoDB's expertise and AWS's generative AI tools, emphasizing how Atlas Vector Search unlocks the full lifecycle value of connected vehicle data. During the event, MongoDB also engaged in a conversation with The Six Five, exploring various aspects of mobility, self-driving vehicles (SDVs), and the MongoDB and AWS partnership. This discussion extended to merging IT and OT, GenAI, Atlas Edger Server, and Atlas Device SDK. Going forward At the end of the road, it’s all about enhancing the end-user experience and providing unique value propositions. Defect diagnosis based on the acoustics of the engine, improved crash assistance with mobile and vehicle telemetry data, just-in-time food ordering while on the road, in-vehicle payments, and much, much more. What all these experiences have in common is the combination of interconnected data from different systems. At MongoDB, we are laser-focused on empowering OEMs to create, transform, and disrupt the automotive industry by unleashing the power of software and data. We enable this by: Partnering with alliances such as COVESA to build a strong ecosystem or collaboration. Having one single API for In-vehicle Data Storage, Edge to Cloud Synchronization, Time Series storage, and more, improves the developer experience. Focusing on having a robust, scalable, and secure suite of services trusted by tens of thousands of customers in more than 100 countries. Together with COVESA’s vision for connected vehicles, we’re driving a future where this industry is safer, more efficient, and seamlessly integrated into the digital world. The journey is just beginning. To learn more about MongoDB-connected mobility solutions, visit the MongoDB for Manufacturing & Motion webpage . Achieving fast, reliable and compressed data exchange is one of the pillars of Software Defined Vehicles, learn how MongoDB Atlas and Edge Server can help in this short demo .
Enabling Commerce Innovation with the Power of MongoDB and Google Cloud
Across all industries, business leaders are grappling with economic uncertainty, cost concerns, disruption to supply chains, and pressure to embrace new technologies like generative AI. In this dynamic landscape, having a performant and future-proofed technology foundation is critical to your business’s success. Kin + Carta, a Premier Google Cloud Partner and MongoDB Systems Integrator Partner, recently launched the Integrated Commerce Network . The Integrated Commerce Network is an Accelerator that enables clients to modernize to a composable commerce platform and create value with their commerce data on Google Cloud with a pre-integrated solution in as little as six weeks. This article explains the concept of composable commerce and explores how MongoDB and Google Cloud form a powerful combination that enables innovation in commerce. Finally, it explains how Kin + Carta can help you navigate the complexity facing businesses today with their approach to digital decoupling. 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 Unraveling the complexity: What is composable commerce? Why microservices and APIs? The evolution of commerce architecture Traditional monolithic architectures, once the cornerstone of commerce platforms, are facing challenges in meeting the demands of today's fast-paced digital environment. Microservices, a paradigm that breaks down applications into small, independent services, offer a solution to the limitations of monoliths. This architectural shift allows for improved agility, scalability, and maintainability. Defining composable commerce Composable commerce is a component-based, API-driven design approach that gives businesses the flexibility to build and run outstanding buying experiences free of constraints found in legacy platforms. To be truly composable, the platform must support key tenets: Support continuous delivery without downtime at the component level Have API as the contract of implementation between services, with open, industry-standard protocols providing the glue between components Be SaaS based, or portable to run on any modern public cloud environment Allow the open egress and ingress of data — no black-boxes of vendor data ownership Defining APIs and microservices APIs play a pivotal role in connecting microservices, enabling seamless communication and data exchange. This modular approach empowers businesses to adapt quickly to market changes, launch new features efficiently, and scale resources as needed. Enhanced scalability, resilience, and agility Taking a microservices approach provides businesses with options and now represents a mature and battle-tested approach with commoditized architectures, infrastructure-as-code, and open-source design patterns to enable robust, resilient, and scalable commerce workloads at lower cost and risk. Additionally, the decoupled nature of microservices facilitates faster development cycles. Development teams can work on isolated components, allowing for parallel development and quicker releases. This agility is a game-changer in the competitive e-commerce landscape, where rapid innovation is essential for staying ahead. Microservices and API-based commerce solutions (like commercetools, which is powered by MongoDB) have begun to dominate the market with their composable approach, and for good reason. These solutions remove the dead-end of legacy commerce suite software and enable a brand to pick and choose to enhance its environment on its own terms and schedule. MongoDB Atlas: The backbone of intelligent, generative AI-driven experiences As e-commerce has developed, customers are expecting more from their interactions — flat, unsophisticated experiences just don’t cut it anymore and brands need to deliver on the expectation of immediacy and contextual relevance. Taking a microservices approach enables richer and more granular data to be surfaced, analyzed, and fed back into the loop, perhaps leveraging generative AI to synthesize information that previously would have been difficult or impossible without huge computing capabilities. However, to do this well you need core data infrastructure that underpins the platform and provides the performance, resilience, and advanced features required. MongoDB Atlas on Google Cloud can play a pivotal role in this enablement. Flexible data models: Microservices often require diverse data models. MongoDB Atlas, a fully managed database service, accommodates these varying needs with its flexible schema design, which allows businesses to adapt their data structures without compromising performance. Horizontal scalability: Modern commerce moves a lot of data. MongoDB Atlas excels in distributing data across multiple nodes, ensuring that the database can handle increased loads effortlessly. Real-time data access: Delivering on expectations relies on real-time data access. MongoDB Atlas supports real-time, event-driven data updates, ensuring you are using the most up-to-date information about your customers. Serverless deployment: Rather than spend time and money managing complex database infrastructure, MongoDB Atlas can leverage serverless deployment, allowing developers to focus on building features that delight customers and impact the bottom line. Unleashing generative AI with MongoDB and Google Cloud Generative AI applications thrive on massive datasets and require robust data management. MongoDB effortlessly handles the complex and ever-evolving nature of gen AI data. This includes text, code, images, and more, allowing you to train your models on a richer data tapestry. MongoDB Atlas: Streamlined gen AI development on Google Cloud MongoDB Atlas, the cloud-based deployment option for MongoDB, integrates seamlessly with Google Cloud. Atlas offers scalability and manageability, letting you focus on building groundbreaking gen AI applications. Here's how this powerful duo functions together: Data ingestion and storage: Effortlessly ingest your training data, regardless of format, into MongoDB Atlas on Google Cloud. This data can include text for natural language processing, code for programming tasks, or images for creative generation. AI model training: Leverage Google Cloud's AI services like Vertex AI to train your gen AI models using the data stored in MongoDB Atlas. Vertex AI provides pre-built algorithms and tools to streamline model development. Operationalization and serving: Once trained, deploy your gen AI model seamlessly within your application. MongoDB Atlas ensures the smooth data flow to and from your model, enabling real-time generation. Vector search with MongoDB Atlas: MongoDB Atlas Vector Search allows for efficient retrieval of similar data points within your gen AI dataset. This is crucial for tasks like image generation or recommendation systems. Advantages of this open approach By leveraging a microservices architecture, APIs, and the scalability and flexibility of Atlas, businesses can build agile and adaptable composable platforms. Atlas seamlessly integrates with Google Cloud, providing a streamlined environment for developing and deploying generative AI models. This integrated approach offers several benefits: Simplified development: The combined power of MongoDB Atlas and Google Cloud streamlines the development process, allowing you to focus on core gen AI functionalities. Scalability and flexibility: Both MongoDB Atlas and Google Cloud offer on-demand scalability, ensuring your infrastructure adapts to your gen AI application's growing needs. Faster time to market: The ease of integration and development offered by this combination helps you get your gen AI applications to market quickly. Cost-effectiveness: Both MongoDB Atlas and Google Cloud offer flexible pricing models, allowing you to optimize costs based on your specific gen AI project requirements. Digital decoupling, a legacy modernization approach With so much digital disruption, technology leaders are constantly being challenged. Existing legacy architectures and infrastructure can be extremely rigid and hard to unravel. Over 94% of senior leaders reported experiencing tech anxiety . So how do you manage this noise, meet the needs of the business, stay relevant, and evolve your technology so that you can deliver the kinds of experiences audiences expect? Digital decoupling is a legacy modernization approach that enables large, often well-established organizations to present a unified online experience to their users, take full advantage of their data, innovate safely, and compete effectively with digital natives. Technology evolves rapidly, and an effective microservices solution should be designed with future scalability and adaptability in mind. Kin + Carta helps to ensure that your solution is not only robust for current requirements but also capable of evolving with emerging technologies and business needs. It all starts with a clear modernization strategy that allows you to iteratively untangle from legacy systems, while also meeting the needs of business stakeholders seeking innovation. Navigating commerce complexity with Kin + Carta on Google Cloud Commerce is undergoing a significant transformation, and businesses need a future-proof technology foundation to handle the demands of complex models and massive datasets. That’s why Kin + Carta launched their Integrated Commerce Network , the first commerce-related solution that’s part of Google’s Industry Value Network . With the right tools and partners, your business can be at the forefront of innovation with generative AI, through automating tasks in revolutionary new ways, creating entirely new content formats, and delivering more personalized customer experiences. The complexities of commerce transformation can be daunting. But you can master the art of digital decoupling and leverage the strengths of the Integrated Commerce Network to unlock limitless possibilities and gain an edge over your competition. Check out Kin + Carta’s guide: Flipping the script — A new vision of legacy modernization enabled by digital decoupling . Get started with MongoDB Atlas on Google Cloud today.
A Smarter Factory Floor with MongoDB Atlas and Google Cloud's Manufacturing Data Engine
The manufacturing industry is undergoing a transformative shift from traditional to digital, propelled by data-driven insights, intelligent automation, and artificial intelligence. Traditional methods of data collection and analysis are no longer sufficient to keep pace with the demands of today's competitive landscape. This is precisely where Google Cloud’s Manufacturing Data Engine (MDE) and MongoDB Atlas come into play, offering a powerful combination for optimizing your factory floor. Unlock the power of your factory data MDE is positioned to transform the factory floor with the power of cloud-driven insights. MDE simplifies communication with your factory floor, regardless of the diverse protocols your machines might use. It effortlessly connects legacy equipment with modern systems, ensuring a comprehensive data stream. MDE doesn't just collect data, it transforms it. By intelligently processing and contextualizing the information, you gain a clearer picture of your production processes in real-time with a historical pretext. It offers pre-built analytics and AI tools directly addressing common manufacturing pain points. This means you can start finding solutions faster, whether it's identifying bottlenecks, reducing downtime, or optimizing resource utilization. Conveniently, it also offers great support for integrations that can further enhance the potential of the data (e.g. additional data sinks). The MongoDB Atlas developer data platform enhances MDE by providing scalability and flexibility through automated scaling to adapt to evolving data requirements. This makes it particularly suitable for dynamic manufacturing environments. MongoDB’s document model can handle diverse data types and structures effortlessly while being incredibly flexible because of its native JSON format. This allows for enriching MDE data with other relevant data, such as supply chain logistics, for a deeper understanding of the factory business. You can gain immediate insights into your operations through real-time analytics, enabling informed decision-making based on up-to-date data. While MDE offers a robust solution for collecting, contextualizing, and managing industrial data, leveraging it alongside MongoDB Atlas offers tremendous advantages Inside the MDE integration Google Cloud’s Manufacturing Data Engine (MDE) acts as a central hub for your factory data. It not only processes and enriches the data with context, but also offers flexible storage options like BigQuery and Cloud Storage. Now, customers already using MongoDB Atlas can skip the hassle of application re-integration and make this data readily accessible for applications. Through this joint solution developed by Google Cloud and MongoDB, you can seamlessly move the processed streaming data from MDE to MongoDB Atlas using Dataflow jobs. MDE publishes the data via a Pub/Sub subscription, which is then picked up by a custom Dataflow job built by MongoDB. This job transforms the data into the desired format and writes it to your MongoDB Atlas cluster. Google Cloud’s MDE and MongoDB Atlas utilize compatible data structures, simplifying data integration through a shared semantic configuration. Once the data resides in MongoDB Atlas, your existing applications can access it seamlessly without any code modifications, saving you time and effort. The flexibility of MDE, combined with the scalability and ease of use of MongoDB Atlas, makes this a powerful and versatile solution for various data-driven use cases such as predictive maintenance and quality control, while still providing factory ownership of the data. Instructions on how to set up the dataflow job are available in the MongoDB github repository. Conclusion If you want to level up your manufacturing data analytics, pairing MDE with MongoDB Atlas provides a proven, easy-to-implement solution. It's easy to get started with MDE and MongoDB Atlas . 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
Unleashing Developer Potential–and Managing Costs–with MongoDB Atlas
In today's business landscape, where unpredictability has become the norm, engineering leaders have to balance the dual challenges of managing uncertainty while optimizing IT costs. Indeed, the 2024 MarginPLUS Deloitte survey—which draws on insights from over 300 business leaders—emphasizes a collective pivot towards growth initiatives and cost transformations amidst the fluctuating global economic climate. 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 MongoDB Atlas: A developer's ally for cost-effective productivity Executives across industries want to cut costs without impacting innovation; based on the Deloitte survey , 83% of companies are looking to change how they run their business margin improvement efforts. This is where MongoDB Atlas , the most advanced cloud database service on the market, comes in. An integrated suite of data services that simplify how developers build with data, MongoDB Atlas helps teams enhance their productivity without compromising on cost efficiency by offering visibility and control over spending—balancing developer freedom with data governance and cost management. This helps organizations escape the modernization hamster wheel—or the vicious cycle of continuously updating technology without making real progress, and draining resources while failing to deliver meaningful improvements. Put another way, MongoDB gives teams more time to innovate, instead of just maintaining the status quo. Outlined below are the built-in features of MongoDB Atlas, which enable customers to get the most out of their data while also focusing on budget optimization. Strategic features for cost optimization with MongoDB Atlas Right-sizing your cluster Use MongoDB Atlas’s Cluster Sizing Guide or auto-scalability to match your cluster with your workload, optimizing resource use with options for every requirement, including Low CPU options for lighter workloads. Pausing clusters and global distribution Save costs by pausing your cluster , and securely storing data for up to 30 days with auto-resume. Furthermore, Global Clusters improve performance across regions while maintaining cost efficiency and compliance. Index and storage management Enhance performance and reduce costs with MongoDB Atlas’s Performance Advisor , which provides tailored index and schema optimizations for better query execution and potential reductions in cluster size. Strategic data management Reduce storage expenses using Online Archive for infrequently accessed data and TTL indexes for efficient Time Series data management, ensuring only essential data is stored. Securely backup data before deletion with mongodump. Enhanced spend management Use spend analysis, billing alerts , and usage insights via the Billing Cost Explorer for detailed financial management and optimization. Resource tagging and customizable dashboards provide in-depth financial reporting and visual expense tracking, supporting effective budgeting and cost optimization. Additionally, Opt for serverless instances to adjust to your workload's scale, offering a pay-for-what-you-use model that eliminates overprovisioning concerns. Transforming uncertainty into advancement MongoDB Atlas equips IT decision-makers and developers with the features and tools to balance developer productivity with strategic cost management, transforming economic uncertainty into a platform for strategic advancement. MongoDB Atlas is more than a database management solution; it’s a strategic partner in optimizing your IT spending, ensuring that your organization remains agile, efficient, and cost-effective in the face of change. Need expert assistance in taking control of your MongoDB Atlas costs? MongoDB’s Professional Services team can provide a deep-dive assessment of your environment to build a tailored optimization plan—and to help you execute. Reach out to learn how we can support your cost optimization goals! If you haven't yet set up your free cluster on MongoDB Atlas , now is a great time to do so. You have all the instructions in this DevCenter article .
How MongoDB Enables Digital Twins in the Industrial Metaverse
The integration of MongoDB into the metaverse marks a pivotal moment for the manufacturing industry, unlocking innovative use cases across design and prototyping, training and simulation, and maintenance and repair. MongoDB's powerful capabilities — combined with Augmented Reality (AR) or Virtual Reality (VR) technologies — are reshaping how manufacturers approach these critical aspects of their operations, while also enabling the realization of innovative product features. But first: What is the metaverse, and why is it so important to manufacturers? We often use the term, "digital twin" to refer to a virtual replication of the physical world. It is commonly used for simulations and documentation. The metaverse goes one step further: Not only is it a virtual representation of a physical device or a complete factory, but the metaverse also reacts and changes in real time to reflect a physical object’s condition. The advent of the industrial metaverse over the past decade has given manufacturers an opportunity to embrace a new era of innovation, one that can enhance collaboration, visualization, and training. The industrial metaverse is also a virtual environment that allows geographically dispersed teams to work together in real time. Overall, the metaverse transforms the way individuals and organizations interact to produce, purchase, sell, consume, educate, and work together. This paradigm shift is expected to accelerate innovation and affect everything from design to production across the manufacturing industry. Here are some of the ways the metaverse — powered by MongoDB — is having an impact manufacturing. Design and prototyping Design and prototyping processes are at the core of manufacturing innovation. Within the metaverse, engineers and designers can collaborate seamlessly using VR, exploring virtual spaces to refine and iterate on product designs. MongoDB's flexible document-oriented structure ensures that complex design data, including 3D models and simulations, is efficiently stored and retrieved. This enables real-time collaboration, accelerating the design phase while maintaining the precision required for manufacturing excellence. Training and simulation Taking a digital twin and connecting it to physical assets enables training beyond traditional methods and provides immersive simulations in the metaverse that enhance skill development for manufacturing professionals. VR training, powered by MongoDB's capacity to manage diverse data types — such as time-series, key-values and events — enables realistic simulations of manufacturing environments. This approach allows workers to gain hands-on experience in a safe virtual space, preparing them for real-world challenges without affecting production cycles. Gamification is also one of the most effective ways to learn new things. MongoDB's scalability ensures that training data, including performance metrics and user feedback, is efficiently handled to continuously enlarge the training modules and the necessary resources for the ever-increasing amount of data. Maintenance and repair Maintenance and repair operations are streamlined through AR applications within the metaverse. The incorporation of AR and VR technologies into manufacturing processes amplifies the user experience, making interactions more intuitive and immersive. Technicians equipped with AR devices can access real-time information overlaid onto physical equipment, providing step-by-step guidance for maintenance and repairs. MongoDB's support for large volumes of diverse data types, including multimedia and spatial information, ensures a seamless integration of AR and VR content. This not only enhances the visual representation of data from the digital twin and the physical asset but also provides a comprehensive platform for managing the vast datasets generated during AR and VR interactions within the metaverse. Additionally, MongoDB's geospatial capabilities come into play, allowing manufacturers to manage and analyze location-based data for efficient maintenance scheduling and resource allocation. The result is reduced downtime through more efficient maintenance and improved overall operational efficiency. From the digital twin to metaverse with MongoDB The advantages of a metaverse for manufacturers are enormous, and according to Deloitte many executives are confident the industrial metaverse “ will transform research and development, design, and innovation, and enable new product strategies .” However, the realization is not easy for most companies. Challenges include managing system overload, handling vast amounts of data from physical assets, and creating accurate visualizations. The metaverse must also be easily adaptable to changes in the physical world, and new data from various sources must be continuously implemented seamlessly. Given these challenges, having a data platform that can contextualize all the data generated by various systems and then feed that to the metaverse is crucial. That is where MongoDB Atlas , the leading developer data platform, comes in, providing synchronization capabilities between physical and virtual worlds, enabling flexible data modeling, and providing access to the data via a unified query interface as seen in Figure 1. Figure 1: MongoDB connecting to a physical & virtual factory Generative AI with Atlas Vector Search With MongoDB Atlas, customers can combine three systems — database, search engine, and sync mechanisms — into one, delivering application search experiences for metaverse users 30% to 50% faster . Atlas powers use cases such as similarity search, recommendation engines, Q&A systems, dynamic personalization, and long-term memory for large language models (LLMs). Vector data is integrated with application data and seamlessly indexed for semantic queries, enabling customers to build easier and faster. MongoDB Atlas enables developers to store and access operational data and vector embeddings within a single unified platform. With Atlas Vector Search , users can generate information for maintenance, training, and all the other use cases from all possible information that is accessible. This information can come from text files such as Word, from PDFs, and even from pictures or sound streams from which an LLM then generates an accurate semantic answer. It’s no longer necessary to keep dozens of engineers busy, just creating useful manuals that are outdated at the moment a production line goes through first commissioning. Figure 2: Atlas Vector Search Transforming the manufacturing industry with MongoDB In the digital twin and metaverse-driven future of manufacturing, MongoDB emerges as a linchpin, enabling cost-effective virtual prototyping, enhancing simulation capabilities, and revolutionizing training processes. The marriage of MongoDB with AR and VR technologies creates a symbiotic relationship, fostering innovation and efficiency across design, training, and simulation. As the manufacturing industry continues its journey into the metaverse, the partnership between MongoDB and virtual technologies stands as a testament to the transformative power of digital integration in shaping the future of production. Learn more about how MongoDB is helping organizations innovate with the industrial metaverse by reading how we Build a Virtual Factory with MongoDB Atlas in 5 Simple Steps , how IIoT data can be integrated in 4 steps into MongoDB, or how MongoDB drives Innovations End-To-End in the whole Manufacturing Chain .
RegData & MongoDB: Streamline Data Control and Compliance
While navigating the requirements of keeping data secure in highly regulated markets, organizations can find themselves entangled in a web of costly and complex IT systems. Whether it's the GDPR safeguarding European personal data or the Monetary Authority of Singapore's guidelines on outsourcing and cloud computing , the greater the number of regulations organizations are subjected to, particularly across multiple geographical locations, the more intricate their IT infrastructure becomes, and organizations today face the challenge of adapting immediately or facing the consequences. In addition to regulations, customer expectations have become a major driver for innovation and modernization. In the financial sector, for example, customers demand a fast and convenient user experience with real-time access to transaction info, a fully digitized mobile-first experience with mobile banking, and personalization and accessibility for their specific needs. While these sorts of expectations have become the norm, they conflict with the complex infrastructures of modern financial institutions. Many financial institutions are saddled with legacy infrastructure that holds them back from adapting quickly to changing market conditions. Established financial institutions must find a way to modernize, or they risk losing market share to nimble challenger banks with cost-effective solutions. The banking market today is increasingly populated with nimble fintech companies powered by smaller and more straightforward IT systems, which makes it easier for them to pivot quickly. In contrast, established institutions often operate across borders, meaning they must adhere to a greater number of regulations. Modernizing these complex systems requires the simultaneous introduction of new, disruptive technology without violating any regulatory constraints, akin to driving a car while changing a tire. The primary focus for established banks is safeguarding existing systems to ensure compliance with regulatory constraints while prioritizing customer satisfaction and maintaining smooth operations as usual. RegData: Compliance without risk Multi-cloud application security platform, RegData embraces this challenge head-on. RegData has expertise across a number of highly regulated markets, from healthcare to public services, human resources, banking, and finance. The company’s mission is clear—delivering a robust, auditable, and confidential data protection platform within their comprehensive RegData Protection Suite (RPS), built on MongoDB. RegData provides its customers with more than 120 protection techniques , including 60 anonymization techniques, as well as custom techniques (protection of IBANs, SSNs, emails, etc), giving them total control over how sensitive data is managed within each organization. For example, by working with RegData, financial institutions can configure their infrastructure to specific regulations, by masking, encrypting, tokenizing, anonymizing, or pseudonymizing data into compliance. With RPS, company-wide reports can be automatically generated for the regulating authorities (i.e., ACPR, ECB, EU-GDPR, FINMA, etc.). To illustrate the impact of RPS, and to debunk some common misconceptions, let’s explore before and after scenarios. Figure 1 shows the decentralized management of access control. Some data sources employ features such as Field Level Encryption (FLE) to shield data, restricting access to individuals with the appropriate key. Additionally, certain applications implement Role-Based Access Control (RBAC) to regulate data access within the application. Some even come with an Active Directory (AD) interface to try and centralize the configuration. Figure 1: Simplified architecture with no centralized access control However, each of these only addresses parts of the challenge related to encrypting the actual data and managing single-system access. Neither FLE nor RBAC can protect data that isn’t on their data source or application. Even centralizing efforts like the AD interface exclude older legacy systems that might not have interfacing functionalities. The result in all of these cases is a mosaic of different configurations in which silos stay silos, and modernization is risky and slow because the data may or may not be protected. RegData, with its RPS solution, can integrate with a plethora of different data sources as well as provide control regardless of how data is accessed, be it via the web, APIs, files, emails, or others. This allows organizations to configure RPS at a company level. All applications including silos can and should interface with RPS to protect all of the data with a single global configuration. Another important aspect of RPS is its functions with tokenization, allowing organizations to decide which columns or fields from a given data source should be encrypted according to specific standards and govern the access to corresponding tokens. Thanks to tokenization, RPS can track who accesses what data and when they access it at a company level, regardless of the data source or the application. This is easy enough to articulate but quite difficult to execute at a data level. To efficiently manage diverse data sources, fine-grained authorization, and implement different protection techniques, RegData builds RPS on top of MongoDB's flexible and document-oriented database. The road to modernization As noted, to fully leverage RegData’s RPS, all data sources should go through the RPS. RPS works like a data filter, putting in all of the information and extracting protected data on the other side, to modernize and innovate. Just integrating RegData means being able to make previously siloed data available by masking, encrypting, or anonymizing it before sending it out to other applications and systems. Together, RegData and MongoDB form a robust and proven solution for protecting data and modernizing operations within highly regulated industries. The illustration below shows the architecture of a private bank utilizing RPS. Data can only be seen in plain text to database admins when the request comes from the company’s headquarters. This ensures compliance with regulations, while still being able to query and search for data outside the headquarters. This bank goes a step further by migrating their Customer Relationship Management (CRM), core banking, Portfolio Management System (PMS), customer reporting, advisory, tax reporting, and other digital apps into the public cloud. This is achieved while still being compliant and able to automatically generate submittable audit reports to regulating authorities. Figure 2: Private bank business care Another possible modernization scheme—given RegData’s functionalities—is a hybrid cloud Operational Data Layer (ODL), using MongoDB Atlas . This architectural pattern acts as a bridge between consuming applications and legacy solutions. It centrally integrates and organizes siloed enterprise data, rendering it easily available. Its purpose is to offload legacy systems by providing alternative access to information for consuming applications, thereby breaking down data silos, decreasing latency, allowing scalability, flexibility, and availability, and ultimately optimizing operational efficiency and facilitating modernization. RegData integrates, protects, and makes data available, while MongoDB Atlas provides its inherent scalability, flexibility, and availability to empower developers to offload legacy systems. Figure 3: Example of ODL with both RegData and MongoDB In conclusion, in a world where finding the right solutions can be difficult, RegData provides a strategic solution for financial institutions to securely modernize. By combining RegData's regulatory protection and modern cloud platforms such as MongoDB Atlas, the collaboration takes on the modernizing challenge of highly regulated sectors. Are you prepared to harness these capabilities for your projects? Do you have any questions about this? Then please reach out to us at industry.solutions@mongodb.com or info@regdata.ch You can also take a look at the following resources: Hybrid Cloud: Flexible Architecture for the Future of Financial Services Implementing an Operational Data Layer
Reducing Bias in Credit Scoring with Generative AI
This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Credit scoring plays a pivotal role in determining who gets access to credit and on what terms. Despite its importance, however, traditional credit scoring systems have long been plagued by a series of critical issues from biases and discrimination, to limited data consideration and scalability challenges. For example, a study of US loans showed that minority borrowers were charged higher interest rates (+8%) and rejected loans more often (+14%) than borrowers from more privileged groups. The rigid nature of credit systems means that they can be slow to adapt to changing economic landscapes and evolving consumer behaviors, leaving some individuals underserved and overlooked. To overcome this, banks and other lenders are looking to adopt artificial intelligence to develop increasingly sophisticated models for scoring credit risk. In this article, we'll explore the fundamentals of credit scoring, the challenges current systems present, and delve into how artificial intelligence (AI), in particular, generative AI (genAI) can be leveraged to mitigate bias and improve accuracy. From the incorporation of alternative data sources to the development of machine learning (ML) models, we'll uncover the transformative potential of AI in reshaping the future of credit scoring. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. What is credit scoring? Credit scoring is an integral aspect of the financial landscape, serving as a numerical gauge of an individual's creditworthiness. This vital metric is employed by lenders to evaluate the potential risk associated with extending credit or lending money to individuals or businesses. Traditionally, banks rely on predefined rules and statistical models often built using linear regression or logistic regression. The models are based on historical credit data, focusing on factors such as payment history, credit utilization, and length of credit history. However, assessing new credit applicants poses a challenge, leading to the need for more accurate profiling. To cater to the underserved or unserved segments traditionally discriminated against, fintechs and digital banks are increasingly incorporating information beyond traditional credit history with alternative data to create a more comprehensive view of an individual's financial behavior. Challenges with traditional credit scoring Credit scores are integral to modern life because they serve as a crucial determinant in various financial transactions, including securing loans, renting an apartment, obtaining insurance, and even sometimes in employment screenings. Because the pursuit of credit can be a labyrinthine journey, here are some of the challenges or limitations with traditional credit scoring models that often cloud the path to credit application approval. Limited credit history: Many individuals, especially those new to the credit game, encounter a significant hurdle – limited or non-existent credit history. Traditional credit scoring models heavily rely on past credit behavior, making it difficult for individuals without a robust credit history to prove their creditworthiness. Roughly 45 million Americans lack credit scores simply because those data points do not exist for them. Inconsistent income: Irregular income, typical in part-time work or freelancing, poses a challenge for traditional credit scoring models, potentially labeling individuals as higher risk and leading to application denials or restrictive credit limits. In 2023 in the United States , data sources differ on how many people are self-employed. One source shows more than 27 million Americans filed Schedule C tax documents, which cover net income or loss from a business – highlighting the need for different methods of credit scoring for those self-employed. High utilization of existing credit: Heavy reliance on existing credit is often perceived as a signal of potential financial strain, influencing credit decisions. Credit applications may face rejection or approval with less favorable terms, reflecting concerns about the applicant's ability to judiciously manage additional credit. Lack of clarity in rejection reasons: Understanding the reasons behind rejections hinders applicants from addressing the root causes – in the UK, a study between April 2022 and April 2023 showed the main reasons for rejection included “poor credit history” (38%), “couldn’t afford the repayments” (28%), “having too much other credit" (19%) and 10% said they weren’t told why. The reasons even when given are often too vague which leaves applicants in the dark, making it difficult for them to address the root cause and enhance their creditworthiness for future applications. The lack of transparency is not only a trouble for customers, it can also lead to a penalty for banks. For example, a Berlin bank was fined €300k in 2023 for lacking transparency in declining a credit card application. Lack of flexibility: Shifts in consumer behavior, especially among younger generations preferring digital transactions, challenge traditional models. Factors like the rise of the gig economy, non-traditional employment, student loan debt, and high living costs complicate assessing income stability and financial health. Traditional credit risk predictions are limited during unprecedented disruptions like COVID-19, not taking this into account in scoring models. Recognizing these challenges highlights the need for alternative credit scoring models that can adapt to evolving financial behaviors, handle non-traditional data sources, and provide a more inclusive and accurate assessment of creditworthiness in today's dynamic financial landscape. Credit scoring with alternative data Alternative credit scoring refers to the use of non-traditional data sources (aka. alternative data) and methods to assess an individual's creditworthiness. While traditional credit scoring relies heavily on credit history from major credit bureaus, alternative credit scoring incorporates a broader range of factors to create a more comprehensive picture of a person's financial behavior. Below are some of the popular alternative data sources: Utility payments: Beyond credit history, consistent payments for utilities like electricity and water offer a powerful indicator of financial responsibility and reveal a commitment to meeting financial obligations, providing crucial insights beyond traditional metrics. Rental history: For those without a mortgage, rental payment history emerges as a key alternative data source. Demonstrating consistent and timely rent payments paints a comprehensive picture of financial discipline and reliability. Mobile phone usage patterns: The ubiquity of mobile phones unlocks a wealth of alternative data. Analyzing call and text patterns provides insights into an individual's network, stability, and social connections, contributing valuable information for credit assessments. Online shopping behavior: Examining the frequency, type, and amount spent on online purchases offers valuable insights into spending behaviors, contributing to a more nuanced understanding of financial habits. Educational and employment background: Alternative credit scoring considers an individual's educational and employment history. Positive indicators, such as educational achievements and stable employment, play a crucial role in assessing financial stability. These alternative data sources represent a shift towards a more inclusive, nuanced, and holistic approach to credit assessments. As financial technology continues to advance, leveraging these alternative data sets ensures a more comprehensive evaluation of creditworthiness, marking a transformative step in the evolution of credit scoring models. Alternative credit scoring with artificial intelligence Besides the use of alternative data, the use of AI as an alternative method has emerged as a transformative force to address the challenges of traditional credit scoring, for a number of reasons: Ability to mitigate bias: Like traditional statistical models, AI models, including LLMs, trained on historical data that are biased will inherit biases present in that data, leading to discriminatory outcomes. LLMs might focus on certain features more than others or may lack the ability to understand the broader context of an individual's financial situation leading to biased decision-making. However, there are various techniques to mitigate the bias of AI models: Mitigation strategies: Initiatives begin with the use of diverse and representative training data to avoid reinforcing existing biases. Inadequate or ineffective mitigation strategies can result in biased outcomes persisting in AI credit scoring models. Careful attention to the data collected and model development is crucial in mitigating this bias. Incorporating alternative data for credit scoring plays a critical role in reducing biases. Rigorous bias detection tools, fairness constraints, and regularization techniques during training enhance model accountability: Balancing feature representation and employing post-processing techniques and specialized algorithms contribute to bias mitigation. Inclusive model evaluation, continuous monitoring, and iterative improvement, coupled with adherence to ethical guidelines and governance practices, complete a multifaceted approach to reducing bias in AI models. This is particularly significant in addressing concerns related to demographic or socioeconomic biases that may be present in historical credit data. Regular bias audits: Conduct regular audits to identify and mitigate biases in LLMs. This may involve analyzing model outputs for disparities across demographic groups and adjusting the algorithms accordingly. Transparency and explainability: Increase transparency and explainability in LLMs to understand how decisions are made. This can help identify and address biased decision-making processes. Trade Ledger , a lending software as a service (SaaS) tool, uses a data-driven approach to make informed decisions with greater transparency and traceability by bringing data from multiple sources with different schemas into a single data source. Ability to analyze vast and diverse datasets: Unlike traditional models that rely on predefined rules and historical credit data, AI models can process a myriad of information, including non-traditional data sources, to create a more comprehensive assessment of an individual's creditworthiness, ensuring that a broader range of financial behaviors is considered. AI brings unparalleled adaptability to the table: As economic conditions change and consumer behaviors evolve, AI-powered models can quickly adjust and learn from new data. The continuous learning aspect ensures that credit scoring remains relevant and effective in the face of ever-changing financial landscapes. The most common objections from banks to not using AI in credit scoring are transparency and explainability in credit decisions. The inherent complexity of some AI models, especially deep learning algorithms, may lead to challenges in providing clear explanations for credit decisions. Fortunately, the transparency and interpretability of AI models have seen significant advancements. Techniques like SHapley Additive exPlanations (SHAP) values and Local Interpretable Model-Agnostic Explanations (LIME) plots and several other advancements in the domain of Explainable AI (XAI) now allow us to understand how the model arrives at specific credit decisions. This not only enhances trust in the credit scoring process but also addresses the common critique that AI models are "black boxes." Understanding the criticality of leveraging alternative data that often comes in a semi or unstructured format, financial institutions work with MongoDB to enhance their credit application processes with a faster, simpler, and more flexible way to make payments and offer credit: Amar Bank, Indonesia's leading digital bank , is combatting bias by providing microloans to people who wouldn’t be able to get financial services from traditional banks (unbanked and underserved). Traditional underwriting processes were inadequate for customers lacking credit history or collateral so they have streamlined lending decisions by harnessing unstructured data. Leveraging MongoDB Atlas, they developed a predictive analytics model integrating structured and unstructured data to assess borrower creditworthiness. MongoDB's scalability and capability to manage diverse data types were instrumental in expanding and optimizing their lending operations. For the vast majority of Indians, getting credit is typically challenging due to stringent regulations and a lack of credit data. Through the use of modern underwriting systems, Slice, a leading innovator in India’s fintech ecosystem , is helping broaden the accessibility to credit in India by streamlining their KYC process for a smoother credit experience. By utilizing MongoDB Atlas across different use cases, including as a real-time ML feature store, slice transformed their onboarding process, slashing processing times to under a minute. slice uses the real-time feature store with MongoDB and ML models to compute over 100 variables instantly, enabling credit eligibility determination in less than 30 seconds. Transforming credit scoring with generative AI Besides the use of alternative data and AI in credit scoring, GenAI has the potential to revolutionize credit scoring and assessment with its ability to create synthetic data and understand intricate patterns, offering a more nuanced, adaptive, and predictive approach. GenAI’s capability to synthesize diverse data sets addresses one of the key limitations of traditional credit scoring – the reliance on historical credit data. By creating synthetic data that mirrors real-world financial behaviors, GenAI models enable a more inclusive assessment of creditworthiness. This transformative shift promotes financial inclusivity, opening doors for a broader demographic to access credit opportunities. Adaptability plays a crucial role in navigating the dynamic nature of economic conditions and changing consumer behaviors. Unlike traditional models which struggle to adjust to unforeseen disruptions, GenAI’s ability to continuously learn and adapt ensures that credit scoring remains effective in real-time, offering a more resilient and responsive tool for assessing credit risk. In addition to its predictive prowess, GenAI can contribute to transparency and interpretability in credit scoring. Models can generate explanations for their decisions, providing clearer insights into credit assessments, and enhancing trust among consumers, regulators, and financial institutions. One key concern however in making use of GenAI is the problem of hallucination, where the model may present information that is either nonsensical or outright false. There are several techniques to mitigate this risk and one approach is using the Retrieval Augment Generation (RAG) approach. RAG minimizes hallucinations by grounding the model’s responses in factual information from up-to-date sources, ensuring the model’s responses reflect the most current and accurate information available. Patronus AI for example leverages RAG with MongoDB Atlas to enable engineers to score and benchmark large language models (LLMs) performance on real-world scenarios, generate adversarial test cases at scale, and monitor hallucinations and other unexpected and unsafe behavior. This can help to detect LLM mistakes at scale and deploy AI products safely and confidently. Another technology partner of MongoDB is Robust Intelligence . The firm’s AI Firewall protects LLMs in production by validating inputs and outputs in real-time. It assesses and mitigates operational risks such as hallucinations, ethical risks including model bias and toxic outputs, and security risks such as prompt injections and personally identifiable information (PII) extractions. As generative AI continues to mature, its integration into credit scoring and the broader credit application systems promises not just a technological advancement, but a fundamental transformation in how we evaluate and extend credit. A pivotal moment in the history of credit The convergence of alternative data, artificial intelligence, and generative AI is reshaping the foundations of credit scoring, marking a pivotal moment in the financial industry. The challenges of traditional models are being overcome through the adoption of alternative credit scoring methods, offering a more inclusive and nuanced assessment. Generative AI, while introducing the potential challenge of hallucination, represents the forefront of innovation, not only revolutionizing technological capabilities but fundamentally redefining how credit is evaluated, fostering a new era of financial inclusivity, efficiency, and fairness. If you would like to discover more about building AI-enriched applications with MongoDB, take a look at the following resources: Digitizing the lending and leasing experience with MongoDB Deliver AI-enriched apps with the right security controls in place, and at the scale and performance users expect Discover how slice enables credit approval in less than a minute for millions
Safety Champion Builds the Future of Safety Management on MongoDB Atlas, with genAI in Sight
Safety Champion was born in Australia in 2015, out of an RMIT university project aiming to disrupt the safety management industry, still heavily reliant on paper-based processes and lagging in terms of digitisation, and bring it to the cloud. Most companies today need to comply with strict workplace safety policies. This is true for industries reliant on manual workers, such as manufacturing, transport and logistics, construction, and healthcare, but also for companies dealing with digital workers, both working in the office or remotely. To do this, organisations rely on safety management processes and systems that help them comply with government and industry-led regulations, as well as keep their employees safe. Whether it’s legal obligations about safety reporting, management of employees and contractors, or ways to implement company-wide safety programs, Safety Champion’s digital platform provides customers more visibility and tracking over safety programs, and a wealth of data to help make evidence-based safety decisions. "Data is core to our offering, as well as core to how next-generation safety programs are being designed and implemented. With paper-based processes, you simply can’t get access to rich data, connect data sets easily, or uncover organisation-wide insights and patterns that can help drive efficiencies and improve safety outcomes," explains Craig Salter, Founder of Safety Champion. MongoDB Atlas: Unlocking the power of data and analytics to improve safety outcomes for customers Safety Champion started using the self-managed version of MongoDB, and shortly after that in 2017 moved onto MongoDB Atlas which was more cost-effective, meant less overhead and not having to manage the day-to-day tasks required to keep a database up and running. The main challenge is that industry standards and policies around safety vary significantly for every company - the safety risks of an office-based business of digital workers are widely different from the risks workers on a manufacturing plant are exposed to, making data collection and itemisation for deeper insights very complex. MongoDB’s document model, its flexibility, and its ability to handle complex sets of data combined with Atlas’ ease of use for developers made it the perfect fit for Safety Champion. "It was very easy to get started on MongoDB, but also super easy and quick to get applications developed and brought to market," says Sid Jain, Solution Architect for Safety Champion. "The performance optimisation we saw using MongoDB Atlas was significant, and it freed up a lot of time from our developers so they could focus on what matters most to our business, instead of worrying about things like patching, setting up alerts, handling back-ups, and so on." The use of MongoDB Charts also gives Safety Champions’ customers access to important analytics that can be presented in visual forms, fitting very specific use cases and internal audiences. This helps organisations using Safety Champion improve decision-making by presenting very concrete data and graphs that can fuel evidence-based safety decisions. "MongoDB Atlas helps drive efficiencies for our clients, but it also helps safety and operations managers to have a voice in making important safety decisions because they are backed by strong data-led evidence. Connecting data sets means the ability to have a much deeper, richer view of what’s happening and what needs to be done," says Salter. Managing exponential growth: 2024, the year of scaling up, generative AI, Search, and much more Before 2020, Safety Champions was still a small start-up, with its platform managing about 5,000 documents a month - these include incident records, checklists, inspection reports, actionable tasks, task completion reports, and more. The COVID pandemic forced organisations to move their safety processes online and comply with a whole new set of safety measures and policies, which saw the company’s business explode: triple-digit annual growth between 2021 and 2023, a dev team that tripled in size, over 2,000 customers, and now up to 100,000 documents handled per month. "As our company kept growing, with some of our new customers handling tens of thousands of safety documents every month - we knew we needed to enable even more scale and future proof ourselves for the next years to come," explains Salter. "We also knew that if we wanted to take advantage of MongoDB’s capabilities in generative AI, search, multi-region, and more, which a lot of our customers were asking for, we needed to set some strong data foundations." Safety Champion is now in the process of upgrading to MongoDB 6.0, which will offer its clients more speed, especially when handling larger and more complex queries. MongoDB Search is now also available system-wide, allowing search queries to be performed across all the modules a client has added records for. "Since many modules allow linking records to each other, allowing a single search query to find and return records from multiple modules makes a world of difference. Developers no longer have to maintain other data systems and the extraction, transformation, and sync of data between MongoDB and search index happens seamlessly, greatly reducing the Ops burden on dev teams," explains Jain. The use of multi-region functionalities within MongoDB Atlas means customers, especially global ones operating in multiple geographic regions, will be able to segregate data and ensure they meet regulatory requirements around data hosting and security. Lastly, Safety Champion is exploring the potential of generative AI with plans to start using MongoDB Vector Search , later in 2024. Some of the use cases the company is already investigating include semantic insights, understanding textual data that employees enter in forms, applying LLMs to that data, and extracting helpful information from it. "Every client wants more analytics, more insights, and more high-level meaning out of data. It’s not just about making it easier to enter data and seeing safety incidents, it’s about what it means and decisions that can be made from a safety perspective," says Salter. "The new version of the Safety Champion platform powered by MongoDB Atlas means we are fully ready to dive into the next phase of our evolution as a business and offer features such as generative AI which will take both Safety Champions and our customers to the next era of safety management."
Spotlight on Two Aussie Start-Ups Building AI Services on MongoDB Atlas
Australian-based Eclipse AI and Pending AI are using the power of MongoDB Atlas to bring their AI ideas to life and blaze new trails in fields including pharmaceutical R&D and customer retention. With the recent advancements in the fields of AI and generative AI, innovation has been unleashed to new heights. Many organisations are taking advantage of technologies such as Natural Language Processing (NLP), Large Language Models (LLMs), and more to create AI-driven products, services, and apps. Amongst those that are blazing new trails in the AI space are two Australian start-ups: Pending AI , which is helping scientists and researchers in the pharmaceutical space improve early research & development stages, and Eclipse AI , a company that unifies and analyses omnichannel voice-of-customer data to give customers actionable intelligence to drive retention. What they have in common is their choice to use MongoDB Atlas . This multi-cloud, developer data platform unifies operational, analytical, and generative AI data services to streamline building AI-enriched applications. Here is how we are helping these two Australian start-ups create the next generation of AI products faster, with less complexity, and without breaking the bank. Pending AI improves pharmaceutical R&D by leveraging next-generation technologies Pending AI has developed a suite of artificial intelligence and quantum mechanics-based capabilities to solve critical problem statements within the earliest stages of pharmaceutical research and development. The Pending AI platform is capable of dramatically improving the efficiency and effectiveness of the compound discovery pipeline, meaning stakeholders can obtain better, commercially viable scaffolds for further clinical development in a fraction of the time and cost. Building its two artificial intelligence-based capabilities - Generative Molecule Designer and Retrosynthesis Engine - was a mammoth task. The known number of pharmacologically relevant molecules in chemical space is exceptionally large, and there are over 50 million known chemical reactions and billions of molecular building blocks - expert scientists have to undergo cost- and time-inefficient trial-and-error processes to design desired molecules and identify optimal synthesis routes to them. Pending AI needed a database that could handle a very large number of records, and be highly performant at that scale, as required by the vastness of chemical space. A few databases were considered by Pending AI, but MongoDB kept standing out as a battle-tested, reliable, and easy-to-implement solution, enabling Pending AI’s team to build a highly performant deployment on MongoDB Atlas. “As a startup, getting started with the community edition of MongoDB and being able to run a reliable cluster at scale was a huge benefit. Now that we’re starting to leverage the AWS infrastructure in our platform, MongoDB Atlas provides us with a fully managed solution at a low cost, and with a Private Endpoint between our AWS deployment and MongoDB cluster, we have kept latency to a minimum, and our data secure,” said Dr. David Almeida Cardoso , Vice President, Business Development at Pending AI. Output of Pending AI's Generative Molecule Designer Pending AI’s Generative Molecule Designer has been built as a machine learning model on MongoDB Atlas, trained to understand the language of pharmaceutical structures, which allows for automated production of novel compound scaffolds that can be focused and tailored to outputs of biological and/or structural studies. The Retrosynthesis Engine is also built using a set of machine learning models and MongoDB Atlas, trained to understand chemical reactions, which allows for the prediction of multiple, valid synthetic routes within a matter of minutes. “We’re also excited to explore the new Atlas Search index feature in MongoDB 7.0. We hope this will allow us to integrate some of the search functionality, which is currently complex to manage and maintain, directly into MongoDB, rather than relying on a separately maintained Elasticsearch cluster,” added Cardoso. Being part of the MongoDB AI Innovator program also allowed Pending AI to explore leveraging cloud infrastructure to scale its platform and test newer versions of MongoDB quickly and easily. Eclipse AI turns customer interaction insights into revenue Eclipse AI is a SaaS platform that turns siloed customer interactions from different sources - these can be customer calls, emails, surveys, reviews, support tickets, and more - into insights that drive retention and revenue. It was created to address the frustration of customer experience (CX) teams around the number of hours and man-weeks of effort needed to consolidate and analyse customer feedback data from different channels. Eclipse AI took on the challenge of solving this issue and worked hard to find a way to offer customers faster and more efficient ways to turn customer feedback into actionable insights. The first problem was consolidating the voice-of-customer data which was so fragmented; the second was analysing that data and turning it into specific improvement actions to improve the customer experience and prevent customer churn. Because MongoDB Atlas is a flexible document database that also can store and index vector embeddings for unstructured data, it was a perfect fit for Eclipse AI and enabled its small dev team to focus on building the product very efficiently and quickly, without being burdened with managing infrastructure. MongoDB Atlas also comes with key features such as MongoDB Atlas Device SDKs (formerly Realm) and MongoDB Atlas Search that were instrumental in bringing Eclipse AI’s platform to life. "For us, MongoDB is more than just a database, it is data-as-a-service. This is thanks to tools like Realm and Atlas Search that are seamlessly built into the platform. With minimum effort, we were able to add a relevance-based full-text search on top of our data. Without MongoDB Atlas we would not have been able to iterate quickly and ship new features fast,” commented Saad Irfani, co-founder of Eclipse AI. “Best of all, horizontal scaling is a breeze with single-click sharding that doesn't require setting up config servers or routers, reducing costs along the way. The unified monitoring and performance advisor recommendations are just the cherry on top.” Eclipse AI - MongoDB dashboard G2 rated Eclipse AI as the #1 proactive customer retention platform globally for SMEs, a recognition that wouldn’t have been possible without the use of MongoDB Atlas. Exploring your AI potential with MongoDB MongoDB Atlas is built for AI . Why? Because MongoDB specialises in helping companies and their developer teams manage richly structured data that doesn't neatly fit into the rigid rows and columns of traditional relational databases, and turn that into meaningful and actionable insights that help operationalise AI. More recently, we have added Vector Search - enabling developers to build intelligent applications powered by semantic search and generative AI over any type of data - and enhanced AWS CodeWhisperer coding assistant to our list of tools companies can use to further their AI exploration. These are just a handful of examples of what is possible in the realm of AI today. Many of our customers around the world, from start-ups to large enterprises like banks and telcos are investing in MongoDB Atlas and capabilities such as Atlas Search , Vector Search , and more to create what the future of AI and generative AI will look like in the next decade. If you want to learn more about how you can get started with your AI project, or take your AI capabilities to the next level, you can check out our MongoDB for Artificial Intelligence resources page for the latest best practices that get you started in turning your idea into an AI-driven reality.
Connected Vehicles: Accelerate Automotive Innovation With MongoDB Atlas and AWS
Capgemini's Trusted Vehicle Solution heralds a new era in driver and fleet management experiences. This innovative platform leverages car-to-cloud connectivity, unlocking a world of possibilities in fleet management, electric vehicle charging, predictive maintenance, payments, navigation, and consumer-facing mobile applications. Bridging the gap between disparate systems, Trusted Vehicle fast-tracks the development of software-defined vehicles and ushers in disruptive connectivity, autonomous driving, shared mobility, and electrification (CASE) technologies. In this post, we will explore how MongoDB Atlas and AWS work together to power Capgemini's Trusted Vehicle solution. What is Trusted Vehicle? Capgemini’s Trusted Vehicle solution accelerates time-to-market with a secure and scalable platform of next-generation driver and fleet-management experiences. Trusted Vehicle excels in fleet management, EV charging, navigation, and more while also accelerating software-defined vehicle development. By seamlessly connecting disparate systems, it paves the way for disruptive advancements in automotive technologies. AWS for Automotive empowers OEMs, mobility providers, parts suppliers, automotive software companies, and dealerships to effectively utilize AWS, providing them with tailored solutions and capabilities in many areas such as autonomous driving, connected mobility, digital customer engagement, software-defined vehicle, manufacturing, supply chain, product engineering, sustainability, and more. Based on its cloud mobility solution expertise and immense experience in successfully implementing Trusted Vehicle for its clients, Capgemini has developed repeatable and customizable modules for OEMs and mobility companies to accelerate their connected mobility journey. These quick-start modules can be swiftly customized for any organization by adding capabilities. Here are a few examples of the modules: Diagnostics trouble-code tracker for fleet maintenance that bolsters safety and efficiency Fleet management software with keyless vehicle remote control for convenience and security Predictive maintenance for connected vehicles to detect anomalies and ensure proactive interventions For automotive OEMs, innovation through digitization of their products and services is of paramount importance. The development of connected and smart vehicles requires cutting-edge technologies. Capgemini recognizes the significance of robust data platforms in shaping the future of connected vehicles. At the core of the Trusted Vehicle solution lies the MongoDB Atlas developer data platform. This strategic partnership and integration ensures that automotive OEMs can harness the power of a modern, scalable, and secure data platform, enabling efficiency, secure and robust connectivity, and seamless user experiences. Benefits of MongoDB Atlas for Capgemini Trusted Vehicle solution Faster time-to-market and developer velocity MongoDB Atlas’ core value proposition is to offer a unified data platform for developers to build applications. With MongoDB Atlas, Capgemini built the core data processing, from sensor data to valuable business insights, with one API. Limiting the number of infrastructure components helps developers spend less time writing orchestration code and the corresponding automated tests, setting up the infrastructure with all the disaster recovery requirements, and monitoring that stack. Absolving developers from those responsibilities allows them to deliver more features, bringing business value to the customers rather than spending precious time on technical plumbing. Cloud agnosticism and customized Trusted Vehicles for customers MongoDB Atlas is a fully managed database as a service that offers features like multi-cloud clusters, automated data tiering, continuous backups, and many more. With a multi-cloud cluster in MongoDB Atlas, customers can: use data from an application running in a single cloud and analyze that data on another cloud without manually managing data movement. use data stored in different clouds to power a single application. easily migrate an application from one cloud provider to another. Multi-cloud enables improved governance by accommodating customers who require data to be stored in a specific country for legal or regulatory reasons. It also allows for performance optimization by deploying resources in regions nearest to where users are located. Implementing Atlas for the Edge Atlas for the Edge provides a solution that streamlines the management of data generated across various sources at the edge, including connected cars and user applications. Two key components of this solution are Atlas Device Sync and SDKs . Together, they provide a fully managed backend that facilitates secure data synchronization between devices and the cloud. This also includes out-of-the-box network handling, conflict resolution, authentication, and permissions. To successfully implement MongoDB’s Atlas for the Edge solution, AWS Greengrass was used to facilitate over-the-air updates and manage the software deployment onto the vehicles, while Device Sync and SDKs handled the transmission of data from the car back to the cloud. Greengrass allows executing code through lambda functions, utilizing data received via MQTT or from the connected device. Device SDKs, however, overcome AWS Lambda's temporary file system storage limitation by offering a significantly enhanced data storage capacity. Greengrass can now locally store the telematics data in the database provided by the SDKs. Therefore, the data will be stored even if the device is offline. Following the restoration of network connectivity, the locally stored telematics data can be synchronized with the MongoDB Atlas cluster. The storage capabilities of the Device SDKs help ensure that processes run smoothly and continuously. Syncing telemetry data to Atlas Dynamic queries with flexible sync Device Sync lets developers control exactly what data moves between their client(s) and the cloud. This is made possible by flexible sync, a configuration that allows for the definition of a query in the client and synchronization of only the objects that match the query. These dynamic queries can be executed based on user inputs, eliminating developers' need to discern which query parameters to assign to an endpoint. Moreover, with Device SDKs, developers can integrate seamlessly with their chosen platform, directly interfacing with its native querying system. This synergy streamlines the development process for enhanced efficiency. Data ingest for IoT Data ingest , a sync configuration for applications with heavy client-side insert-only workloads facilitates seamless data streaming from the Trusted Vehicle software to a flexible sync-enabled app. This unidirectional data sync is useful in IoT applications, like when a weather sensor transmits data to the cloud. In the case of vehicles, information specific to each car — such as speed, tire pressure, and oil temperature — is transmitted to the cloud. Data ingest is also helpful in writing other types of immutable data where conflict resolution is unnecessary. This includes tasks like generating invoices through a retail application or logging events in an application. Data lifecycle management with Device Sync Atlas Device Sync completely manages the lifecycle of this data. Data ingest and flexible sync handles the writing and synchronization processes, including removing data that is no longer needed from devices. On-device storage, network handling, and conflict resolution ensure that clients retain data even when offline. Once reconnected to a network, data seamlessly and automatically synchronizes with MongoDB Atlas. Processing and accessing data with aggregation pipelines The raw data gathered from individual vehicles, like metrics such as speed, direction, and tire pressure, lacks meaningful interpretation on its own. MongoDB’s aggregation pipeline transforms these individual records into contextualized information like driver profiles, usage patterns, trip specifics, and more, yielding actionable insights. For optimal storage and performance efficiency, MongoDB automatically archives individual records after they are processed, ensuring they remain accessible for future retrieval. Overview of Atlas for the Edge - AWS architecture The implementation of Atlas for the Edge for Trusted Vehicle’s solution shifts the responsibility of collecting, syncing, and processing data from AWS components to Atlas Device Sync and SDKs. The Device SDK for Node.js is used in the lambda function, which runs as soon as the Greengrass core device boots up and stores the vehicle telematics data every two seconds in the Realm DB. Using flexible sync with data ingests, the vehicle will automatically sync the telemetry data from the device to the MongoDB Atlas cluster on AWS into a time series collection. An aggregated document representing the vehicle’s or drivers’ data can be computed with the aggregation pipeline and stored in a collection or as a materialized view and accessed via an API endpoint. Historical telemetric data that gets cold can be automatically archived into cold storage using Online Archive, native to the time series collection. This archived data is still accessible if needed on a specific API endpoint using the federated query feature of MongoDB Atlas. Trusted Vehicle with AWS and MongoDB Atlas MongoDB Atlas offers a trifecta of benefits when utilized within Capgemini's Trusted Vehicle solution. First, it accelerates time-to-market and enhances developer efficiency by streamlining and simplifying the technology stack. Second, MongoDB Atlas proves to be more cost-effective as the fleet of vehicles expands. The reduction in cost per vehicle, especially as scale reaches 1,000 and 10,000, results in a substantial decrease in the total cost of ownership. Keeping efficiencies of scale in mind, the OEMs running millions of cars on the road will certainly benefit from this solution. Third, MongoDB's cloud-agnostic components pave the way for a more flexible and adaptable implementation, breaking free from the constraints of specific cloud environments. Ultimately, MongoDB Atlas not only expedites development and reduces costs but also provides a more versatile solution catering to a wider range of clients. For more information on our partnership with Capgemini, please visit our partner webpage . Additionally, visit our MongoDB in Manufacturing and Automotive page to understand our value proposition for the automotive industry and take a look at our connected vehicle solution video .
Pledging Ourselves to the Future
As MongoDB’s sustainability manager, you could say I think about the climate a lot. After all, doing so is my job. But because it’s January and a time of reflection, I’ve been thinking about climate change more than usual — particularly about the progress we’ve made, but also the work that remains to be done. For example, in December the annual U.N. Climate Change Conference (COP 28) ended with a landmark agreement to transition away from fossil fuels, and the aim of reaching net zero carbon dioxide emissions by 2050. The COP 28 agreement also calls on countries to triple their renewable energy capacity and reduce other forms of emissions. The agreement was very welcome because before COP 28 began the U.N. released a stark report that showed national plans are, "insufficient to limit global temperature rise." As worried as I might be some days, I’m also buoyed by the climate action of the last few years. According to the U.S. Energy Information Administration, in 2022 more energy was generated by renewable sources than by coal for the first time. There have also been several regulations passed globally that make the measurement and disclosure of emissions mandatory, a key step in understanding — and reducing — emissions. MongoDB joins The Climate Pledge In the same spirit of optimism, I’m delighted to announce that MongoDB recently signed The Climate Pledge joining hundreds of leading organizations in publicly affirming our commitment to sustainability. The Climate Pledge’s hundreds of signatories commit to regularly report on their emissions and reach net-zero emissions by 2040 through decarbonization strategies and carbon offsets. “We’re thrilled to join the world’s leading companies — like MongoDB customers Verizon and Telefónica — in signing The Climate Pledge,” said MongoDB chief product officer, Sahir Azam. “MongoDB looks forward to working with the Climate Pledge team to ensure a more sustainable future for everyone.” Signing the The Climate Pledge is hardly the first step MongoDB has taken toward ensuring a more sustainable future. In 2023, MongoDB committed to being 100% powered by renewable energy by 2026, and achieving net-zero carbon emissions by 2030. To meet those targets, we’re working to reduce our carbon footprint through product innovation, by adding new sources of renewable energy, and by making MongoDB employees’ commutes more sustainable. Goodbye waste, hello (energy) savings In 2023, we also announced MongoDB’s new Sustainable Procurement Policy , which aims to ensure that sustainability is considered at all levels of our supply chain. The policy covers everything from the coffee we purchase (100% sustainably sourced) to the single-use items we use (restrictions leading to a 58% waste reduction in 2023). How MongoDB’s workloads are powered falls under our sustainable procurement efforts. Specifically, we’re currently working with our cloud partners — all of whom share MongoDB’s aim to be 100% powered by renewable energy by 2026 — to reduce our carbon footprint. "MongoDB takes its commitment to carbon reduction seriously, and we're fortunate to work with partners who share our enthusiasm for sustainability,” said MongoDB Lead Performance Engineer Ger Hartnett. “We look forward to continuing to collaborate with our partners on groundbreaking, energy-saving technology that makes real reductions in our carbon intensity." To meet our renewable energy target, we’ve focused our efforts on several areas, such as preferring buildings with renewable energy contracts or on-site solar when considering new office space. We’ve also entered into several virtual purchase power agreements (VPPAs). Virtual purchase power agreements are a great way for companies like MongoDB to invest in renewable energy without building anything on-site and are a proven method of adding renewable energy to the grid. Since 2022, MongoDB has worked with the enterprise sustainability platform Watershed to support renewable energy projects through VPPAs. Our first project helped build a solar plant in Texas that Watershed notes, “will avoid 13,000 tons of CO2, equivalent to taking nearly 3,000 gas-powered cars off the road each year.” And MongoDB recently signed a new VPPA that will support the development of solar panels for a factory in India. Solar energy is currently responsible for about 16% of global renewable energy, and only about 3.4% of overall energy in the U.S. Those numbers are sure to change, however. In the last fifteen years, global solar power generation has grown from 11.36 terawatt-hours to 1289.87 terawatt hours. What’s more, coal accounts for about 70% of India’s power — versus 20% in the United States — so projects like this will help reduce emissions across Asia. And because many MongoDB employees are directly impacted by air pollution in India , we see VPPAs as a way of benefitting the health and well-being of our employees, as well as the planet. MongoDB's stubborn optimism In the early months of the pandemic, Tom Rivett-Carnac, founding partner of Global Optimism — which launched The Climate Pledge with Amazon in 2019 — shared a video about shifting one’s mindset and changing the world . In the face of larger-than-life problems (like climate change), “stubborn optimism,” he said, “animates action, and infuses it with meaning.” “When the optimism leads to a determined action, then they can become self-sustaining … the two together can transform an entire issue and change the world,” he noted. “Stubborn optimism can fill our lives with meaning and purpose.” Composting is an example of a stubbornly optimistic action that’s both easy to adopt and one that (if enough of us do it) can change the world. Food waste accounts for 6% of global greenhouse emissions, and composting can help reduce those emissions. To put food waste emissions in perspective, 6% of global greenhouse emissions is roughly three times higher than annual global aviation emissions. In 2023, we also began tracking MongoDB’s waste and landfill diversion, and we’re working to improve how we dispose of waste by adding composting services to MongoDB’s hub offices. More than 80% of MongoDB’s offices already have composting services, and we aim to hit 100% in 2024. Not only have composting and single-use purchase reduction helped to decrease waste emissions, but both are highly visible to MongoDB employees. MongoDB employees are increasingly excited about sustainability, inspiring the creation of a mini-garden in our New York office, and the use of more sustainable commuting methods like biking. Though I tend to bike more for exercise than commuting these days (I’ve racked up more than 1,000 miles on my bike pass!), more and more MongoDB team members get to work in sustainable ways. For example, we’re rolling out electric vehicle commuting in India, an e-bike program was recently introduced in our Dublin office, and the bike locker in MongoDB’s New York HQ is generally packed. “I love biking to the office,” said Perry Taylor, a New York-based Information Technology Lead at MongoDB. “In addition to being a great way to stay fit, it’s awesome that how I commute helps the environment.” Looking back on 2023, I’m pleased with how much we accomplished toward MongoDB’s sustainability goals. At the same time, I recognize that more needs to be done. MongoDB enters 2024 with a renewed commitment to sustainability, and we look forward to furthering our progress. To learn more about MongoDB’s sustainability progress, please check out our Sustainability webpage and our latest Corporate Sustainability Report . For more information about fellow Climate Pledge signatories and an interactive timeline of progress made, visit The Climate Pledge .
Evolve Your Data Models as You Modernize with Hackolade and Relational Migrator
Application modernization has always been a constant. For many developers and database administrators, the realization that their legacy relational databases that have served their apps well to this point are no longer as easy and fast to work with has become glaringly apparent as they strive to incorporate emerging use cases like generative AI, search, and edge devices into their customer experience at an increasing rate. While many are turning to MongoDB Atlas for the flexible document model and wide range of integrated data services, migrations are often seen as daunting projects. MongoDB Relational Migrator has simplified several of the key tasks required to successfully migrate from today's popular relational databases to MongoDB. With Relational Migrator, teams can design their target MongoDB schema using their existing relational one as a blueprint, migrate their data to MongoDB while transforming it to their newly designed schema, and get a head start on app code modernization through code template generation and query conversion. But as organizations scale their MongoDB footprint through migrations and new app launches, a new challenge emerges: managing and evolving data models with more teams and stakeholders. Sooner or later, modernization becomes as much about change management as it does technology — keeping teams aligned is critical for keeping everyone moving forward. This is where Hackolade comes in. Hackolade Studio is a visual data modeling and schema design application that enables developers to design and document their MongoDB data models, and more importantly, use those entity-relationship diagrams (ERDs) to collaborate with their counterparts in other areas of the business, like database administration, architecture, and product management. MongoDB data model in Hackolade Studio No database is an island, and the teams working with MongoDB cannot afford to work in isolation. With Hackolade Studio, database teams can use these ERDs to translate their point-of-view to others, making hand-offs and handshakes with other teams like operations more seamless, driving developer productivity, and accelerating new feature builds. Jump from Relational Migrator to Hackoldate Studio with ease Hackolade Studio is now making it even easier to transition to their application after using MongoDB Relational Migrator to complete their migrations. Teams can now use Hackolade Studio’s reverse-engineering feature to import their Relational migrator project (.relmig) files, bringing their MongoDB schema directly over into Hackolade Studio. With this integration, teams can start with Relational Migrator to build their initial schema and execute their data migration, then transition to Hackolade Studio to document, manage, and evolve their schema going forward - giving them a greater degree of control, visibility, and collaboration needed to support modernization initiatives that include many migrations across several applications, teams, and legacy relational environments. MongoDB Relational Migrator, showing a relational schema on the left and its transformed MongoDB schema on the right Getting started is incredibly easy. First, you’ll need your Relational Migrator project file, which can be exported from Relational Migrator to your local device. Then in Hackolade Studio, use the reverse-engineering workflow to import your .relmig file into a new or existing data model. For a detailed walkthrough, dive into Hackolade’s documentation for this integration. Importing Relational Migrator files in Hackolade Studio As MongoDB adoption grows within your organization, more apps and more teams will need to interact with your MongoDB data models. With Relational Migrator and Hackolade together, you will have the tools at your disposal to not only kickstart migration projects but also manage MongoDB data models at scale, giving your teams the insights and visibility needed to drive performance and guide app modernization initiatives. Learn more about how Hackolade can maximize developer productivity and support your modernization to MongoDB initiatives. Download MongoDB Relational Migrator for free to get started with migrating your first databases.