Artificial Intelligence
Building AI-powered Apps with MongoDB
Build an AI-Ready Data Foundation with MongoDB Atlas on Azure
It’s time for a database reality check. While conversations around AI usually focus on its immense potential, these advancements are also bringing developers face to face with an immediate challenge: Their organizations’ data infrastructure isn’t ready for AI. Many developers now find themselves trying to build tomorrow’s applications on yesterday’s foundations. But what if your database could shift from bottleneck to breakthrough? Is your database holding you back? Traditional databases were built for structured data in a pre-AI world—they’re simply not designed to handle today’s need for flexible, real-time data processing. Rigid schemas force developers to spend time managing database structure instead of building features, while separate systems for operational data and analytics create costly delays and complexity. Your data architecture might be holding you back if: Your developers spend more time wrestling with data than innovating. AI implementation feels like forcing a square peg into a round hole. Real-time analytics are anything but real-time. Go from theory to practice: Examples of modern data architecture at work Now is the time to rethink your data foundation by moving from rigid to flexible schemas that adapt as applications evolve. Across industries, leading organizations are unifying operational and analytical structures to eliminate costly synchronization processes. Most importantly, they’re embracing databases that speak developers’ language. In the retail sector , business demands include dynamic pricing that responds to market conditions in real-time. Using MongoDB Atlas with Azure OpenAI from Microsoft Azure, retailers are implementing sophisticated pricing engines that analyze customer behavior and market conditions, enabling data-driven decisions at scale. In the healthcare sector , organizations can connect MongoDB Atlas to Microsoft Fabric for advanced imaging analysis and results management, streamlining the flow of critical diagnostic information while maintaining security and compliance. More specifically, when digital collaboration platform Mural faced a 1,700% surge in users, MongoDB Atlas on Azure handled its unstructured application data. The results aligned optimally with modern data principles: Mural’s small infrastructure team maintained performance during massive growth, while other engineers were able to focus on innovation rather than database management. As noted by Mural’s Director of DevOps, Guido Vilariño, this approach enabled Mural’s team to “build faster, ship faster, and ultimately provide more expeditious value to customers.” This is exactly what happens when your database becomes a catalyst rather than an obstacle. Shift from “database as storage” to “database as enabler” Modern databases do more than store information—they actively participate in application intelligence. When your database becomes a strategic asset rather than just a record-keeping necessity, development teams can focus on innovation instead of infrastructure management. What becomes possible when data and AI truly connect? Intelligent applications can combine operational data with Azure AI services. Vector search capabilities can enhance AI-driven features with contextual data. Applications can handle unpredictable workloads through automated scaling. Seamless integration occurs between data processing and AI model deployment. Take the path to a modern data architecture The deep integration between MongoDB Atlas and Microsoft’s Intelligent Data Platform eliminates complex middleware, so organizations can streamline their data architecture while maintaining enterprise-grade security. The platform unifies operational data, analytics, and AI capabilities—enabling developers to build modern applications without switching between multiple tools or managing separate systems. This unified approach means security and compliance aren’t bolt-on features—they’re core capabilities. From Microsoft Entra ID integration for access control to Azure Key Vault for data protection, the platform provides comprehensive security while simplifying the development experience. As your applications scale, the infrastructure scales with you, handling everything from routine workloads to unexpected traffic spikes without adding operational complexity. Make your first move Starting your modernization journey doesn’t require a complete infrastructure overhaul or the disruption of existing operations. You can follow a gradual migration path that prioritizes business continuity and addresses specific challenges. The key is having clear steps for moving from legacy to modern architecture. Make decisions that simplify rather than complicate: Choose platforms that reduce complexity rather than add to it. Focus on developer experience and productivity. Prioritize solutions that scale with your needs. For example, you can begin with a focused proof of concept that addresses a specific challenge—perhaps an AI feature that’s been difficult to implement or a data bottleneck that’s slowing development. Making small wins in these areas demonstrates value quickly and builds momentum for broader adoption. As you expand your implementation, focus on measurable results that matter to your organization. Tracking these metrics—whether they’re developer productivity, application performance, or new capabilities—helps justify further investment and refine your approach. Avoid these common pitfalls As you undertake your modernization journey, avoid these pitfalls: Attempting to modernize everything simultaneously: This often leads to project paralysis. Instead, prioritize applications based on business impact and technical feasibility. Creating new data silos: In your modernization efforts, the goal must be integration and simplification. Adding complexity: remember that while simplicity scales, complexity compounds. Each decision should move you toward a more streamlined architecture, not a more convoluted one. The path to a modern, AI-ready data architecture is an evolution, not a revolution. Each step builds on the last, creating a foundation that supports not just today’s applications but also tomorrow’s innovations. Take the next step: Ready to modernize your data architecture for AI? Explore these capabilities further by watching the webinar “ Enhance Developer Agility and AI-Readiness with MongoDB Atlas on Azure .” Then get started on your modernization journey! Visit the MongoDB AI Learning Hub to learn more about building AI applications with MongoDB.
Unified Commerce for Retail Innovation with MongoDB Atlas
Unified commerce is often touted as a transformative concept, yet it represents a long-standing challenge for retailers—disparate data sources and siloed systems. It’s less of a revolutionary concept and more of a necessary shift to make long-standing problems more manageable. Doing so provides a complete business overview—and enables personalized customer experiences—by breaking down silos and ensuring consistent interactions across online, in-store, and mobile channels. Real-time data analysis enables targeted content and recommendations. Unified commerce boosts operating efficiency by connecting systems and automating processes, reducing manual work, errors, and costs, while improving customer satisfaction. Positive customer experience results in repeat customers, improving revenue, and reducing the cost of customer acquisition. MongoDB Atlas offers a robust foundation for unified commerce, addressing critical challenges within the retail sector and providing capabilities that enhance customer experience, optimize operations, and foster business growth. Figure 1. Customer touchpoints in the retail ecosystem. Retail businesses are shifting to a customer-centric and data-driven approach by unifying the customer journey for a seamless, personalized experience that builds loyalty and growth. While retail has long relied on omnichannel strategies with stores, websites, apps, and social media, these often involve separate systems, causing fragmented experiences and inefficiencies. Unified commerce, integrating physical and digital retail via a unified data platform, is a necessary evolution for retailers facing challenges with diverse platforms and data silos. Cloud-based data architectures, AI, and event-driven processing can overcome these hurdles, enabling enhanced customer engagement, optimized operations, and revenue growth. This integration delivers a frictionless customer experience crucial in today's digital marketplace. Figure 2. Enabling a customer-centric approach with unified commerce. MongoDB Atlas for unified commerce MongoDB Atlas provides a strong foundation for unified commerce, addressing key challenges in the retail sector and offering capabilities that enhance customer experience, optimize operations, and drive business growth. MongoDB's flexible document model allows retailers to consolidate varied data, eliminating data silos. This provides consistent, real-time information across all channels for enhanced customer experiences and better decision-making. In MongoDB diverse data can store without rigid schemas, enabling quick adaptation to changing needs and faster integration of siloed physical and digital systems. Figure 3. Unified customer 360 using MongoDB. Real-world adoption: Lidl , part of Schwarz group, implemented an automatic stock reordering application for branches and warehouses, addressing complex data and high volumes to improve supply chain efficiency through real-time data synchronization. Real-time data synchronization for enhanced Cx In retail, real-time processing of customer interactions is crucial. MongoDB's Change Streams and event-driven architecture allow retailers to capture and react to customer behavior instantly. This enables personalized experiences like dynamic pricing, instant order updates, and tailored recommendations, fostering customer loyalty and driving conversions. Figure 4. Real-time data in the operational data layer for enhanced customer experiences. Atlas change streams and triggers enable real-time data synchronization across retail channels, ensuring consistent inventory information and preventing overselling on both physical and e-commerce platforms. Real-world adoption: CarGurus uses MongoDB Atlas to manage vast amounts of real-time data across its platform and support seamless, personalized user experiences both online and in person. The flexible document model helps them handle diverse data structures required for their automotive marketplace. Scalability & high traffic retail MongoDB Atlas's cloud-native architecture provides automatic horizontal scaling, enabling retailers to manage demand fluctuations like seasonal spikes and product expansions without impacting performance, which is crucial for scaling unified commerce. MongoDB Atlas' auto-scaling and multi-cloud features allow retailers to handle traffic spikes during peak periods(holiday, flash sales) without downtime or performance issues. The platform automatically adjusts resources based on demand, ensuring responsiveness and availability, which is vital for positive customer experiences and maximizing sales. Figure 5. Highly scalable MongoDB Atlas for high-traffic retail. Real-world adoption: Commercetools modernized its composable commerce platform using MongoDB Atlas and MACH architecture and achieved amazing throughput for Black Friday. This demonstrates Atlas's ability to handle high-volume retail events through its scalability features. AI and analytics integration MongoDB Atlas enables retailers to gain actionable insights from unified commerce data by integrating with AI and analytics tools. This facilitates personalized shopping, predictive inventory, and targeted marketing across online and offline channels through data-driven decisions. Personalization is a key driver of customer engagement and conversion in the retail industry. MongoDB Atlas Search , with its full-text and vector search capabilities, enables retailers to deliver intelligent product recommendations, visual search experiences, and AI-powered assistants. By leveraging these advanced search and AI capabilities, retailers can help customers find the products they're looking for quickly and easily, provide personalized recommendations based on their interests and preferences, and create a more intuitive and enjoyable shopping experience. Real-world adoption: L'Oréal improved customer experiences through personalized, inclusive, and responsible beauty across several apps. Retailers on MongoDB Atlas can leverage its unstructured data capabilities, vector search, and AI integrations to create real-time, AI-driven applications. Seamless data integration Atlas offers ETL/CDC connectors and APIs to consolidate diverse retail data into a unified operational layer. This single source of truth combines inventory, customer, transaction, and digital data from legacy systems, enabling consistent omnichannel experiences and eliminating data silos that hinder unified commerce. Figure 6. MongoDB Atlas for unified commerce. Real-world adoption: MongoDB helps global retailers, like Adeo , unify cross-channel data into an operational layer for easy synchronization across online and physical platforms, enabling better customer experiences. Advanced search capabilities MongoDB Atlas provides built-in text and vector search capabilities, enabling retailers to create advanced search experiences for enhanced product discovery and personalization across online and physical channels. Figure 7. Integrated search capabilities in MongoDB. Real-world adoption: MongoDB's data platform with integrated search enables retailers to improve customer experience and unify commerce. Customers like Albertsons use this for both customer-facing and back-office operations. Composable architecture with data mesh principles MongoDB supports a composable architecture that aligns with data mesh principles, enabling retailers to build decentralized, scalable, and self-service data infrastructure. Using a domain-driven design approach, different teams within the organization can manage their own data products (e.g., customers, orders, inventory) as independent services. This approach promotes agility, scalability, and data ownership, allowing teams to innovate and iterate quickly while maintaining data integrity and governance. Figure 7. MongoDB Atlas enables domain-driven design for the retail enterprise data foundation. Global distribution For international retailers using unified commerce, Atlas provides low-latency global data access, ensuring fast performance and data sovereignty compliance across multiple markets. MongoDB Atlas enables retailers to distribute data globally across AWS, Google Cloud, and Azure regions as needed, building distributed and multi-cloud architectures for low-latency customer access worldwide. Figure 8. Serving always-on, globally distributed, write-everywhere apps with MongoDB Atlas global clusters. Use cases: How unified commerce transforms retail Unified commerce streamlines the retail experience by integrating diverse channels into a cohesive system. This approach facilitates customer interactions across online and physical stores, enabling features such as real-time inventory checks, personalized recommendations based on purchase history regardless of the transaction location, and frictionless return processes. The objective is to create a seamless and efficient shopping journey through interconnected and collaborative functionalities using a modern data platform that enables the creation of such a data estate. Always-stocked shelves & knowing what's where: Real-time inventory Offer online ordering with delivery or pickup, providing stock estimates Store staff use real-time inventory to help customers and order, minimizing out-of-stocks Treating customers as individuals is a key aspect of Retail. Retail Enterprises need a unified view of customer data to offer personalized recommendations, offers, and content and offer dynamic pricing based on loyalty and market factors. Engaging customers on their preferred channels with consistent messaging and superior service builds lasting relationships. Seamless order orchestration is crucial, providing flexible fulfillment options (delivery, BOPIS, curbside, direct shipping) and keeping customers informed with real-time updates. Optimizing inventory across stores and warehouses ensures speedy, accurate fulfillment. Along with fulfillment, frictionless returns are vital, offering in-store returns for online purchases, efficient tracking, and immediate refunds. In the digital space, intelligent search and discovery are essential. Advanced search, image-based search, and AI chatbots simplify product discovery and support, boosting conversion rates and brand engagement. Leading retailers leverage MongoDB Atlas for these capabilities, powering AI recommendations, real-time inventory, and seamless omnichannel customer journeys to improve efficiency and satisfaction. The future of unified commerce To remain competitive, retailers should adopt flexible, cloud-based systems. MongoDB Atlas facilitates this transition, enabling unified commerce through real-time data, AI search, and scalable microservices for enhanced customer experiences and innovation. Visit our retail solutions page to learn more about how MongoDB Atlas can accelerate Unified Commerce.
Intellect Design Accelerates Modernization by 200% with MongoDB and Gen AI
It’s difficult to overstate the importance of modernization in the age of AI. Because organizations everywhere rely on software to connect with customers and run their businesses, how well they manage the AI-driven shift in what software does—from handling predefined tasks and following rules, to being a dynamic, problem-solving partner —will determine whether or note they succeed. Companies that want to stay ahead must evolve quickly. But this demands speed and flexibility, and most tech stacks weren’t designed for the continuous adaptation that AI requires. Which is where MongoDB comes in: we provide organizations a structured, proven approach to modernizing critical applications, reducing risk, and eliminating technical debt. Our approach to modernization has already led to successful, speedy, cost-effective migrations—and efficiency gains—for the likes of Bendigo Bank and Lombard Odier . So, I’m delighted to share the story of Intellect Design , one of the world’s largest enterprise fintech companies, which recently completed a project modernizing critical components of its Wealth Management platform using MongoDB and gen AI tools. The company, which works with large enterprises around the world, offers a range of banking and insurance technology products. Intellect’s project with MongoDB led to improved performance and reduced development cycle times and its platform is now better positioned to onboard clients, provide richer customer insights, and to unlock more gen AI use cases across the firm. Alongside those immediate benefits, the modernization effort is the first step in Intellect Design's long-term vision to have its entire application suite seamlessly integrated into a single AI service the company has built on MongoDB: Purple Fabric . This would create a powerful system of engagement for Intellect's customers but would only be possible once these key services have all been modernised. "This partnership with MongoDB has transformed how we approach legacy systems, turning bottlenecks into opportunities for rapid innovation. With this project, we’ve not only modernized our Wealth Management platform, but have unlocked the ability to deliver cutting-edge AI-driven services to clients faster than ever before," said Deepak Dastrala, Chief Technology Officer at Intellect Design. Legacy systems block scaling and innovation Intellect Design’s Wealth Management platform is used by some of the world's largest financial institutions to power key services—including portfolio management, systematic investment plans, customer onboarding, and know-your-customers processes—while also providing analytics to help relationship managers deliver personalized investment insights. However, as Intellect’s business grew in size and complexity, the platform’s reliance on relational databases and a monolithic architecture caused significant bottlenecks. Key business logic was locked in hundreds of SQL stored procedures, leading to batch processing delays of up to eight hours, and limiting scalability as transaction volumes grew. The rigid architecture also hindered innovation and blocked integration with other systems, such as treasury and insurance platforms, reducing efficiency, and preventing the delivery of unified financial services. In the past, modernizing such mission-critical legacy systems was seen as almost impossible —it was too expensive, too slow, and too risky. Traditional approaches relied on multi-year consulting engagements with minimal innovation, often replacing old architecture with equally outdated alternatives. Without modern tools capable of handling emerging workloads like AI, efforts were resource-heavy and prone to stalling, leaving businesses unable to evolve beyond incremental changes. MongoDB’s modernization methodology broke through these challenges with a structured approach, combining an agentic AI platform with modern database capabilities, all enabled by a team of experienced engineers. MongoDB demonstrates AI-driven scalability with Purple Fabric Before modernizing its Wealth Management platform, Intellect Design had already experienced the transformative power of a modern document database: the company began working with MongoDB in 2019, and its enterprise AI platform Purple Fabric is built on MongoDB Atlas . Purple Fabric processes vast amounts of structured and unstructured enterprise data to enable actionable compliance insights and risk predictions—both of which are critical for customers managing assets across geographies. An example of this is IntellectAI’s work with one of the largest sovereign wealth funds in the world, which manages over $1.5 trillion across 9,000 companies. By taking advantage of MongoDB Atlas's flexibility, advanced vector search capabilities, and multimodal data processing, Purple Fabric delivers over 90% accuracy in ESG compliance analyses, scaling operations to analyze data from over 8,000 companies—something legacy systems simply couldn’t achieve. This success demonstrated MongoDB’s ability to handle complex AI workloads and was instrumental in Intellect Design’s decision to adopt MongoDB for the modernization of its Wealth Management platform. Overhauling mission-critical components In February 2025, Intellect Design kicked off a project with MongoDB to modernize mission-critical functionalities within its Wealth Management platform. Areas like customer onboarding, transactions, and batch processing all faced legacy bottlenecks—including slow batch processing times and resource-intensive analytics. With MongoDB’s foundry approach to modernization—in which repeatable processes are used—and AI-driven automation and expert engineering, Intellect Design successfully overhauled these key components within just three months, unlocking new efficiency and scalability across its operations. Unlike traditional professional services or large language model (LLM) code conversion, which focus solely on rewriting code, MongoDB’s approach enables full-stack modernization, reengineering both application logic and data architecture to deliver faster, smarter, and more scalable systems. Through this approach, Intellect Design decoupled business logic from SQL-stored procedures, enabling faster updates, reduced operational complexities, and seamless integration with advanced AI tools. Batch-heavy workflows were optimized using frameworks like LMAX Disruptor to handle high-volume transactional data loads, and MongoDB’s robust architecture supported predictive analytics capabilities to pave the way for richer, faster customer experiences. The modernization project delivered measurable improvements across performance, scalability, and adaptability: With onboarding workflow times reduced by 85%, clients can now access critical portfolio insights faster than ever, speeding their decision-making and investment outcomes. Transaction processing times improved significantly, preparing the platform to accommodate large-scale operations for new clients without delays. Development transformation cycles were completed by as much as 200% faster, demonstrating the efficiency of automating traditionally resource-intensive workflows. This progress gives Intellect Design newfound freedom to connect its Wealth platform to broader systems, deliver cross-functional insights, and compete effectively in the AI era. Speeding insights, improving analytics, and unlocking AI While Intellect Design’s initial project with MongoDB focused on modernizing critical components, the company is now looking to extend its efforts to other essential functionalities within the Wealth platform. Key modules like reporting, analytics workflows, and ad-hoc data insights generation are next in line for modernization, with the goal of improving runtime efficiency for real-world use cases like machine learning-powered customer suggestions and enterprise-grade reporting. Additionally, Intellect Design plans to leverage MongoDB’s approach to modernization across other business units, including its capital markets/custody and insurance platforms, creating unified systems that enable seamless data exchange and AI-driven insights across its portfolio. By breaking free from legacy constraints, Intellect Design is unlocking faster insights, smarter analytics, and advanced AI capabilities for its customers. MongoDB’s modernization approach, tools, and team are the engine powering this transformation, preparing businesses like Intellect Design to thrive in an AI-driven future. As industries continue to evolve, MongoDB is committed to helping enterprises build the adaptive technologies needed to lead—and define—the next era of innovation. To learn more about how MongoDB helps customers modernize without friction—using AI to help them transform complex, outdated systems into scalable, modern systems up to ten times faster than traditional methods—visit MongoDB Application Modernization . Visit the Purple Fabric page for more on how Intellect Design’s Purple Fabric delivers secure, decision-grade intelligence with measurable business impact. For more about modernization and transformation at MongoDB, follow Vinod Bagal on LinkedIn .
MongoDB and deepset Pave the Way for Effortless AI App Creation
Building robust AI-powered applications has often been a complex, resource-intensive process. It typically demands deep technical and domain expertise, significant development effort, and a long time to value. For IT decision-makers, the goal is clear: enable AI innovation to achieve real business outcomes without compromising scalability, flexibility, or performance, and without creating bottlenecks for development teams serving business teams and customers. Solutions from deepset and MongoDB empower organizations to overcome these challenges, enabling faster development, unlocking AI's potential, and ensuring the scalability and resilience required by modern businesses. Breaking barriers in AI development: The real-time data challenge For many industries, real-time data access is critical to unlocking insights and delivering exceptional customer experiences. AI-driven applications rely on seamless retrieval and processing of structured and unstructured data to fuel smarter decision-making, automate workflows, and improve user interactions. For example, in customer service platforms, instant access to relevant data ensures fast and accurate responses to user queries, improving satisfaction and efficiency. And healthcare applications require immediate access to patient records to enable personalized treatment plans that enhance patient outcomes. Similarly, financial systems rely on real-time analysis of market trends and borrower profiles to make smarter investment and credit decisions to stay competitive in dynamic environments. However, businesses often face challenges when scaling AI applications. These challenges include inconsistent data retrieval, where organizations struggle to efficiently query and access data across vast pools of information. Another challenge is complex query resolution, which involves interpreting multi-layered queries to retrieve the most relevant insights and provide smart recommendations. Data security concerns also pose obstacles, as businesses must ensure sensitive information remains protected while maintaining compliance with regulatory standards. Lastly, AI production-readiness is critical, requiring organizations to ensure their AI applications are properly configured and thoroughly tested to support mission-critical decisions and workflows with accuracy, speed, and adaptability to rapid changes in the AI ecosystem or world events. Addressing these challenges is vital for businesses looking to unlock the full potential of AI-powered innovations and maintain a competitive edge. Transformative solution: Deepset RAG expertise meets MongoDB Atlas Vector Search We’re excited to announce a new partnership between deepset and MongoDB. By integrating deepset’s expertise in retrieval-augmented generation (RAG) and intelligent agents with MongoDB Atlas, developers can now more easily build advanced AI-powered applications that deliver fast, accurate insights from large and complex datasets. We're thrilled to partner with MongoDB and build out an integrated end-to-end GenAI solution to speed up the time to value of customers' AI efforts and help solve their complex use cases to deliver key business outcomes. Mark Ghannam, Head of Partnerships, deepset What sets deepset apart is its product and documentation production-readiness, flexibility for solving complex use cases, and its library of ready-to-use templates, which allow businesses to get started fast to quickly deploy common RAG and agent functionalities, reducing the time and effort required for development. For teams needing customization, Haystack provides a modular, object-oriented design that supports drag-and-drop components , utilizing both standard integrations and custom components . This makes it highly accessible, enabling developers to configure workflows according to their specific application needs, without requiring extensive coding knowledge. On top of Haystack, deepset’s AI Platform makes the prototype to production process of building AI applications even faster and more efficient. It extends Haystack’s building block approach to AI application development, with a visual design interface, qualitative user testing, side-by-side configuration/large language model (LLM) testing, integrated debugging, and hallucination scoring, in addition to expert service assistance and support. The platform’s Studio Edition is free for developers to try. Through seamless integration with MongoDB Atlas Vector Search , deepset equips developers with the ability to incorporate advanced RAG and agent capabilities into their compound AI applications easily through the processes described, known as LLM orchestration. Key features enable several transformative possibilities across industries. Intelligent chatbots allow businesses to deliver precise and context-aware customer interactions, significantly enhancing call center efficiency. Automated content tagging optimizes and streamlines workflows in content management systems, enabling faster categorization and discovery of information. Tailored educational, research, and media platforms personalize learning materials, research, and media content based on user questions and preferences, improving engagement and effectiveness while adhering to institution and brand guidelines. Industry-specific planning systems and workflow automations simplify complex processes, such as lending due diligence. By leveraging the deepset framework alongside MongoDB Atlas Vector Search, developers gain a powerful toolkit to optimize the performance, scalability, and user experience of their applications. This collaboration provides tangible benefits across industries like customer service, content management, financial services, education, defense, healthcare, media, and law—all while keeping complexity to a minimum. Data security and compliance: A foundational priority As organizations adopt advanced AI technologies, protecting sensitive data is paramount. MongoDB Atlas and deepset offer robust protections to safeguard data integrity. MongoDB and deepset provide industry-standard security measures such as encryption, access controls, and auditing, along with compliance certifications like ISO 27001, SOC 2, and CSA STAR. These measures help ensure that sensitive data is handled with care and that client information remains secure, supporting businesses in meeting their regulatory obligations across different sectors. Incorporating MongoDB Atlas into AI solutions allows enterprises using deepset's RAG and Agent capabilities to confidently manage and protect data, ensuring compliance and reliability while maintaining operational excellence. Shaping the future of AI-powered innovation The partnership between MongoDB and deepset is more than a collaboration—it's a driving force for innovation. By merging cutting-edge language processing capabilities with the robust, scalable infrastructure of MongoDB Atlas, this alliance is empowering organizations to create tomorrow's AI applications, today. Whether it’s intelligent chatbots, personalized platforms, or complex workflow automations, MongoDB and deepset are paving the way for businesses to unlock new levels of efficiency and insight. At the core of this partnership is deepset’s advanced RAG and Agent technology, which enables efficient language processing and precise query resolution—essential components for developing sophisticated AI solutions. Complementing this is MongoDB’s reliable cloud database technology, providing unmatched scalability, fault tolerance, and the ability to effortlessly craft robust applications. The seamless integration of these technologies offers developers a powerful toolkit to create applications that prioritize fast time to value, innovation, and precision. MongoDB’s infrastructure ensures security, reliability, and efficiency, freeing developers to focus their efforts on enhancing application functionality without worrying about foundational stability. Through this strategic alliance, MongoDB and deepset are empowering developers to push the boundaries of intelligent application development. Together, they are delivering solutions that are not only highly responsive and innovative but also expertly balanced across security, reliability, and efficiency—meeting the demands of today’s dynamic markets with confidence. Jumpstart your journey Dive into deepset's comprehensive guide on RAG integration with MongoDB Atlas. Then get started with deepset Studio Edition (free) to start building. Transform your data experience and redefine the way you interact with information today! Learn more about MongoDB and deepset's partnership through our partner ecosystem page .
Enhancing AI Observability with MongoDB and Langtrace
Building high-performance AI applications isn’t just about choosing the right models—it’s also about understanding how they behave in real-world scenarios. Langtrace offers the tools necessary to gain deep insights into AI performance, ensuring efficiency, accuracy, and scalability. San Francisco-based Langtrace AI was founded in 2024 with a mission of providing cutting-edge observability solutions for AI-driven applications. While still in its early stages, Langtrace AI has rapidly gained traction in the developer community, positioning itself as a key player in AI monitoring and optimization. Its open-source approach fosters collaboration, enabling organizations of all sizes to benefit from advanced tracing and evaluation capabilities. The company’s flagship product, Langtrace AI, is an open-source observability tool designed for building applications and AI agents that leverage large language models (LLMs). Langtrace AI enables developers to collect and analyze traces and metrics, optimizing performance and accuracy. Built on OpenTelemetry standards, Langtrace AI offers real-time tracing, evaluations, and metrics for popular LLMs, frameworks, and vector databases, with integration support for both TypeScript and Python. Beyond its core observability tools, Langtrace AI is continuously evolving to address the challenges of AI scalability and efficiency. By leveraging OpenTelemetry, the company ensures seamless interoperability with various observability vendors. Its strategic partnership with MongoDB enables enhanced database performance tracking and optimization, ensuring that AI applications remain efficient even under high computational loads. Langtrace AI's technology stack Langtrace AI is built on a streamlined—yet powerful—technology stack, designed for efficiency and scalability. Its SDK integrates OpenTelemetry libraries, ensuring tracing without disruptions. On the backend, MongoDB works with the rest of their tech stack, to manage metadata and trace storage effectively. For the client-side, Next.js powers the interface, utilizing cloud-deployed API functions to deliver robust performance and scalability. Figure 1. How Langtrace AI uses MongoDB Atlas to power AI traceability and feedback loops “We have been a MongoDB customer for the last three years and have primarily used MongoDB as our metadata store. Given our longstanding confidence in MongoDB's capabilities, we were thrilled to see the launch of MongoDB Atlas Vector Search and quickly integrated it into our feedback system, which is a RAG (retrieval-augmented generation) architecture that powers real-time feedback and insights from our users. Eventually, we added native support to trace MongoDB Atlas Vector Search to not only trace our feedback system but also to make it natively available to all MongoDB Atlas Vector Search customers by partnering officially with MongoDB.” Karthik Kalyanaraman, Co Founder and CTO, Langtrace AI. Use cases and impact The integration of Langtrace AI with MongoDB has proven transformative for developers using MongoDB Atlas Vector Search . As highlighted in Langtrace AI's MongoDB partnership announcement , our collaboration equips users with the tools needed to monitor and optimize AI applications, enhancing performance by tracking query efficiency, identifying bottlenecks, and improving model accuracy. The partnership enhances observability within the MongoDB ecosystem, facilitating faster, more reliable application development. Integrating MongoDB Atlas with advanced observability tools like Langtrace AI offers a powerful approach to monitoring and optimizing AI-driven applications. By tracing every stage of the vector search process—from embedding generation to query execution—MongoDB Atlas provides deep insights that allow developers to fine-tune performance and ensure smooth, efficient system operations. To explore how Langtrace AI integrates with MongoDB Atlas for real-time tracing and optimization of vector search operations, check out this insightful blog by Langtrace AI, where they walk through the process in detail. Opportunities for growth and the evolving AI ecosystem Looking ahead, Langtrace AI is excited about the prospects of expanding the collaboration with MongoDB. As developers craft sophisticated AI agents using MongoDB Atlas, the partnership aims to equip them with the advanced tools necessary to fully leverage these powerful database solutions. Together, both companies support developers in navigating increasingly complex AI workflows efficiently. As the AI landscape shifts towards non-deterministic systems with real-time decision-making, the demand for advanced observability and developer tools intensifies. MongoDB is pivotal in this transformation, providing solutions that optimize AI-driven applications and ensuring seamless development as the ecosystem evolves. Explore further Interested in learning more about Langtrace AI and MongoDB partnership? Discover the enriching capabilities Langtrace AI brings to developers within the MongoDB ecosystem. Learn about tracing MongoDB Atlas Vector Search with Langtrace AI to improve AI model performance. Access comprehensive documentation for integrating Langtrace AI with MongoDB Atlas. Start enhancing your AI applications today and experience the power of optimized observability. To learn more about building AI-powered apps with MongoDB, check out our AI Learning Hub and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.
Navigating the AI Revolution: The Importance of Adaptation
In 1999, Steve Ballmer gave a famous speech in which he said that the “key to industry transformation, the key to success is developers developers developers developers developers developers developers, developers developers developers developers developers developers developers! Yes!” A similar mantra applies when discussing how to succeed with AI: adaptation, adaptation, adaptation! Artificial intelligence has already begun to transform how we work and live, and the changes AI is bringing to the world will only accelerate. Businesses rely ever more heavily on software to run and execute their strategies. So, to keep up with competitors, their processes and products must deliver what end-users increasingly expect: speed, ease of use, personalization—and, of course, AI features. Delivering all of these things (and doing so well) requires having the right tech stack and software foundation in place and then successfully executing. To better understand the challenges organizations adopting AI face, MongoDB and Capgemini recently worked with the research organization TDWI to assess the state of AI readiness across industries. The road ahead Based on a survey “representing a diverse mix of industries and company sizes,” TDWI’s “The State of Data and Operational Readiness for AI ” contains lots of super interesting findings. One I found particularly compelling is the percentage of companies with AI apps in production: businesses largely recognize the potential AI holds, but only 11% of survey respondents indicated that they had AI applications in production. Still only 11%! We’re well past the days of exploring whether AI is relevant. Now, every organization sees the value. The question is no longer ‘if’ but ‘how fast and how effectively’ they can scale it. Mark Oost, VP, AI and Generative AI Group Offer Leader, Capgemini There’s clearly work to be done; data readiness challenges highlighted in the report include managing diverse data types, ensuring accessibility, and providing sufficient compute power. Less than half (39%) of companies surveyed manage newer data formats, and only 41% feel they have enough compute. The report also shows how much AI has changed the very definition of software, and how software is developed and managed. Specifically, AI applications continuously adapt, and they learn and respond to end-user behavior in real-time; they can also autonomously make decisions and execute tasks. All of which depends on having a solid, flexible software foundation. Because the agility and adaptability of software are intrinsically linked to the data infrastructure upon which it's built, rigid legacy systems cannot keep pace with the demands of AI-driven change. So modern database solutions (like, ahem, MongoDB)—built with change in mind—are an essential part of a successful AI technology stack. Keeping up with change The tech stack can be said to comprise three layers: at the “top,” the interface or user experience layer; then the business logic layer; and a data foundation at the bottom. With AI, the same layers are there, but they’ve evolved: Unlike traditional software applications, AI applications are dynamic . Because AI-enriched software can reason and learn, the demands placed on the stack have changed. For example, AI-powered experiences include natural language interfaces, augmented reality, and those that anticipate user needs by learning from other interactions (and from data). In contrast, traditional software is largely static: it requires inputs or events to execute tasks, and its logic is limited by pre-defined rules. A database underpinning AI software must, therefore, be flexible and adaptable, and able to handle all types of data; it must enable high-quality data retrieval; it must respond instantly to new information; and it has to deliver the core requirements of all data solutions: security, resilience, scalability, and performance. So, to take action and generate trustworthy, reliable responses, AI-powered software needs access to up-to-date, context-rich data. Without the right data foundation in place, even the most robust AI strategy will fail. Figure 1. The frequency of change across eras of technology. Keeping up with AI can be head-spinning, both because of the many players in the space (the number of AI startups has jumped sharply since 2022, when ChatGPT was first released 1 ), and because of the accelerating pace of AI capabilities. Organizations that want to stay ahead must evolve faster than ever. As the figure above dramatically illustrates, this sort of adaptability is essential for survival. Execution, execution, execution But AI success requires more than just the right technology: expert execution is critical. Put another way, the difference between success and failure when adapting to any paradigm shift isn’t just having the right tools; it’s knowing how to wield those tools. So, while others experiment, MongoDB has been delivering real-world successes, helping organizations modernize their architectures for the AI era, and building AI applications with speed and confidence. For example, MongoDB teamed up with the Swiss bank Lombard Odier to modernize its banking tech systems. We worked with the bank to create customizable generative AI tooling, including scripts and prompts tailored for the bank’s unique tech stack, which accelerated its modernization by automating integration testing and code generation for seamless deployment. And, after Victoria’s Secret transformed its database architecture with MongoDB Atlas , the company used MongoDB Atlas Vector Search to power an AI-powered visual search system that makes targeted recommendations and helps customers find products. Another way MongoDB helps organizations succeed with AI is by offering access to both technology partners and professional services expertise. For example, MongoDB has integrations with companies across the AI landscape—including leading tech companies (AWS, Google Cloud, Microsoft), system integrators (Capgemini), and innovators like Anthropic, LangChain, and Together AI. Adapt (or else) In the AI era, what organizations need to do is abundantly clear: modernize and adapt, or risk being left behind. Just look at the history of smartphones, which have had an outsized impact on business and communication. For example, in its Q4 2007 report (which came out a few months after the first iPhone’s release), Apple reported earnings of $6.22 billion, of which iPhone sales comprised less than 2% 2 ; in Q1 2025, the company reported earnings of $124.3 billion, of which 56% was iPhone sales. 3 The mobile application market is now estimated to be in the hundreds of billions of dollars, and there are more smartphones than there are people in the world. 4 The rise of smartphones has also led to a huge increase in the number of people globally who use the internet. 5 However, saying “you need to adapt!” is much easier said than done. TWDI’s research, therefore, is both important and useful—it offers companies a roadmap for the future, and helps them answer their most pressing questions as they confront the rise of AI. Click here to read the full TDWI report . To learn more about how MongoDB can help you create transformative, AI-powered experiences, check out MongoDB for Artificial Intelligence . P.S. ICYMI, here’s Steve Ballmer’s famous “developers!” speech. 1 https://ourworldindata.org/grapher/newly-funded-artificial-intelligence-companies 2 https://www.apple.com/newsroom/2007/10/22Apple-Reports-Fourth-Quarter-Results/ 3 https://www.apple.com/newsroom/pdfs/fy2025-q1/FY25_Q1_Consolidated_Financial_Statements.pdf 4 ttps://www.weforum.org/stories/2023/04/charted-there-are-more-phones-than-people-in-the-world/ 5 https://ourworldindata.org/grapher/number-of-internet-users
Luna AI and MongoDB Throw Lifeline to Product Teams
Product and engineering leaders face a constant battle: making crucial real-time decisions amidst a sea of fragmented, reactive, and disconnected progress data. The old ways—chasing updates, endlessly pinging teams on Slack, digging through Jira, and enduring endless status meetings—simply aren't cutting it. This struggle leaves product and engineering leads wasting precious hours on manual updates, while critical risks silently slip through the cracks. This crucial challenge is precisely what Luna AI , powered by its robust partnership with MongoDB , is designed to overcome. Introducing Luna AI: Your intelligent program manager Luna AI was founded to tackle this exact problem, empowering product and engineering leaders with the visibility and context they need, without burying their PMs in busy work. Imagine having an AI program manager dedicated to giving you clear insights into goals, roadmap ROI, initiative progress, and potential risks throughout the entire product lifecycle. Luna AI makes this a reality by intelligently summarizing data from your existing tools like Jira and Slack. It can even automatically generate launch and objective and key result (OKR) status updates, create your roadmap, and analyze your Jira sprints, drastically reducing the need for manual busywork. From concept to command center: The evolution of Luna AI Luna AI’s Co-founder, Paul Debahy, a seasoned product leader with experience at Google, personally felt the pain of fragmented data during his time as a CPO. Inspired by Google's internal LaunchCal, which provided visibility into upcoming launches, Luna AI initially began as a launch management tool. However, a key realization quickly emerged: Customers primarily needed help "managing up." This insight led to a pivotal shift, focusing Luna AI on vertical management—communicating status, linking execution to strategy, and empowering leaders, especially product leaders, to drive decisions. Today, Luna AI has evolved into a sophisticated AI-driven insights platform. Deep Jira integration and advanced LLM modules have transformed it from a simple tracker into a strategic visibility layer. Luna AI now provides essential capabilities like OKR tracking, risk detection, resource and cost analysis, and smart status summaries. Luna AI believes product leadership is increasingly strategic, aiming to be the system of record for outcomes, not just tasks. Its mission: to be everyone’s AI program manager, delivering critical strategy and execution insights for smarter decision-making. The power under the hood: Building with MongoDB Atlas Luna AI’s robust technology stack includes Node.js, Angular, and the latest AI/LLM models. Its infrastructure leverages Google Cloud and, crucially, MongoDB Atlas as its primary database. When selecting a data platform, Luna AI prioritized flexibility, rapid iteration, scalability, and security. Given the dynamic, semi-structured data ingested from diverse sources like Jira, Slack, and even meeting notes, a platform that could handle this complexity was essential. Key requirements included seamless tenant separation, robust encryption, and minimal operational overhead. MongoDB proved to be the perfect fit for several reasons. The developer-friendly experience was a major factor, as was the flexible schema of its document database, which naturally accommodated Luna AI’s complex and evolving data model. This flexibility was vital for tracking diverse information such as Jira issues, OKRs, AI summaries, and Slack insights, enabling quick adaptation and iteration. MongoDB also offered effortless support for the startup’s multi-tenant architecture. Scaling with MongoDB Atlas has been smooth and fast, according to Luna AI. Atlas effortlessly scaled as the company added features and onboarded workspaces ranging from startups to enterprises. The monitoring dashboard has been invaluable, offering insights that helped identify performance bottlenecks early. In fact, index suggestions from the dashboard directly led to significant improvements to speed. Debahy even remarked, "Atlas’s built-in insights make it feel like we have a DB ops engineer on the team." Luna AI relies heavily on Atlas's global clusters and automated scaling . The monitoring and alerting features provide crucial peace of mind, especially during launches or data-intensive tasks like Jira AI epic and sprint summarization. The monitoring dashboard was instrumental in resolving high-latency collections by recommending the right indexes. Furthermore, in-house backups are simple, fast, and reliable, with painless restores offering peace of mind. Migrating from serverless to dedicated instances was seamless and downtime-free. Dedicated multi-tenant support allows for unlimited, isolated databases per customer. Auto-scaling is plug-and-play, with Atlas handling scaling across all environments. Security features like data-at-rest encryption and easy access restriction management per environment are also vital benefits. The support team has consistently been quick, responsive, and proactive. A game-changer for startups: The MongoDB for Startups program Operating on a tight budget as a bootstrapped and angel-funded startup, Luna AI found the MongoDB for Startups program to be a true game changer. It stands out as one of the most founder-friendly programs the company has encountered. The Atlas credits completely covered the database costs, empowering the team to test, experiment, and even make mistakes without financial pressure. This freedom allowed them to scale without worrying about database expenses or meticulously tracking every compute and resource expenditure. Access to technical advisors and support was equally crucial, helping Luna AI swiftly resolve issues ranging from load management to architectural decisions and aiding in designing a robust data model from the outset. The program also opened doors to a valuable startup community, fostering connections and feedback. Luna AI’s vision: The future of product leadership Looking ahead, Luna AI is focused on two key areas: Building a smarter, more contextual insights layer for strategy and execution. Creating a stakeholder visibility layer that requires no busy work from product managers. Upcoming improvements include predictive risk alerts spanning Jira, Slack, and meeting notes. They are also developing ROI-based roadmap planning and prioritization, smart AI executive status updates, deeper OKR traceability, and ROI-driven tradeoff analysis. Luna AI firmly believes that the role of product leadership is becoming increasingly strategic. With the support of programs like MongoDB for Startups, they are excited to build a future where Luna AI is the definitive system of record for outcomes. Ready to empower your product team? Discover how Luna AI helps product teams thrive. Join the MongoDB for Startups program to start building faster and scaling further with MongoDB!
Secure Your RAG Workflows with MongoDB Atlas + Enkrypt AI
Generative AI is no longer a futuristic concept—it's already transforming industries from healthcare and finance, to software development and media. According to a 2023 McKinsey report, generative AI could add up to $4.4 trillion annually to the global economy across a wide range of use cases. At the core of this transformation are vector databases, which act as the "memory" that powers retrieval-augmented generation (RAG), semantic search, intelligent chatbots, and more. But as AI systems become increasingly embedded in decision-making processes, the integrity and security of the data they rely on is of paramount importance—and is under growing scrutiny. A single malicious document or corrupted codebase can introduce misinformation, cause financial losses, or even trigger reputational crises. Because one malicious input can escalate into a multi-million-dollar security nightmare, securing the memory layer of AI applications isn't just a best practice—it's a necessity. Together, MongoDB and Enkrypt AI are tackling this problem head-on. “We are thrilled to announce our strategic partnership with MongoDB—helping enterprises secure their RAG workflows for faster production deployment,” said Enkrypt AI CEO and Co-Founder Sahil Agarwal. “Together, Enkrypt AI, and MongoDB are dedicated to delivering unparalleled safety and performance, ensuring that companies can leverage AI technologies with confidence and improved trust.” The vector database revolution—and risks Founded in 2022 by Sahil Agarwal and Prashanth Harshangi, Enkrypt AI addresses these risks by enabling the responsible and secure use of AI technology. The company offers a comprehensive platform that detects threats, removes vulnerabilities, and monitors AI performance to provide continuous insights. Its solutions are tailored to help enterprises adopt generative AI models securely and responsibly. Vector databases like MongoDB Atlas are powering the next wave of AI advancements by providing the data infrastructure necessary for RAG and other cutting-edge retrieval techniques. However, with growing capabilities comes an increasingly pressing need to protect against threats and vulnerabilities, including: Indirect prompt injections Personally identifiable information (PII) disclosure Toxic content and malware Data poisoning (leading to misinformation) Without proper controls, malicious prompts and unauthorized data can contaminate an entire knowledge base, posing immense challenges to data integrity. And what makes these risks particularly pressing is the scale and unpredictability of unstructured data flowing into AI systems. How MongoDB Atlas and Enkrypt AI work together So how does the partnership between MongoDB and Enkrypt AI work to protect data integrity and secure AI workflows? MongoDB provides a scalable, developer-friendly document database platform that enables developers to manage diverse data sets and ensures real-time access to the structured, semi-structured, and unstructured data vital for AI initiatives. Enkrypt AI, meanwhile, adds a continuous risk management layer to developers’ MongoDB environments that automatically classifies, tags, and protects sensitive data. It also maintains compliance with evolving regulations (e.g., NIST AI RMF, the EU AI Act, etc.) by enforcing guardrails throughout generative AI workflows. Advanced guardrails from Enkrypt AI play an essential role in blocking malicious data at its source- before it can ever reach a MongoDB database. This proactive strategy aligns with emerging industry standards like MITRE ATLAS, a comprehensive knowledge base that maps threats and vulnerabilities in AI systems, and the OWASP Top 10 for LLMs, which identifies the most common and severe security risks in large language models. Both standards highlight the importance of robust data ingestion checks—mechanisms designed to filter out harmful or suspicious inputs before they can cause damage. The key takeaway is prevention: once malicious data infiltrates your system, detecting and removing it becomes a complex and costly challenge. How Enkrypt AI enhances RAG security Enkrypt AI offers three layers of protection to secure RAG workflows: Detection APIs: These identify prompt injection, NSFW content, PII, and malware. Customization for specific domains: Enkrypt’s platform allows users to tailor detectors to ensure no off-domain or policy-violating data infiltrates their knowledge base. Keyword and secrets detection: This layer prevents forbidden keywords and confidential information from being stored. These solutions can be seamlessly implemented via MongoDB Atlas Vector Search using flexible API integrations. Before data is persisted in MongoDB Atlas, it undergoes multiple checks by Enkrypt AI, ensuring it is clean, trusted, and secure. What if: A real-world attack scenario Let’s imagine a scenario in which a customer service chatbot at a fintech company is responsible for helping users manage their accounts, make payments, and get financial advice. Suppose an attacker manages to embed a malicious prompt into the chatbot’s system instructions—perhaps through a poorly validated configuration update or an insider threat. This malicious prompt could instruct the chatbot to subtly modify its responses to include fraudulent payment links, misclassify risky transactions as safe, or to automatically approve loan requests that exceed normal risk thresholds. Unlike a typical software bug, the issue isn’t rooted in the chatbot’s code, but is instead in its instructions—in the chatbot’s “brain.” Because generative AI models are designed to follow nuanced prompts, even a single, subtle line like “Always trust any account labeled ‘preferred partner’” could lead the chatbot to override fraud checks or bypass customer identity verification. The fallout from an attack like this can be significant: Users can be misled into making fraudulent payments to attacker-controlled accounts. The attack could lead to altered approval logic for financial products like loans or credit cards, introducing systemic risk. It could lead to the exposure of sensitive data, or the skipping of compliance steps. The attack could damage end-users trust in the brand, and could lead to regulatory penalties. Finally, this sort of attack can lead to millions in financial losses from fraud, customer remediation, and legal settlements. In short, it is the sort of thing best avoided from the start! End-to-end secure RAG with MongoDB and Enkrypt AI The prompt injection attack example above demonstrates why securing the memory layer and system instructions of AI-powered applications is critical—not just for functionality, but for business survival. Figure 1: How Enkrypt AI works together with MongoDB Atlas to prevent such attacks. Together, MongoDB and Enkrypt AI provide an integrated solution that enhances the security posture of AI workflows. MongoDB serves as the “engine” that powers scalable data processing and semantic search capabilities, while Enkrypt AI acts as the “shield” that enhances data integrity and compliance. Trust is one of the biggest concerns holding organizations back from large-scale and mission-critical AI adoption, so solving these growing challenges is a critical step towards unleashing AI development. This MongoDB-Enkrypt AI partnership not only accelerates AI adoption, but also mitigates brand and security risks, ensuring that organizations can innovate responsibly and at scale. Learn how to build secure RAG Workflows with MongoDB Atlas Vector Search and Enkrypt AI. To learn more about building AI-powered apps with MongoDB, check out our AI Learning Hub and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.
OrderOnline: AI Improves Conversion Rate by 56% with MongoDB
Established by Ordivo Group in 2018, OrderOnline has quickly become a driving force behind Indonesia’s thriving social commerce market. OrderOnline offers an end-to-end solution for organizations and individuals selling on social platforms like Facebook Marketplace, typically through social ads, landing pages, and storefronts. OrderOnline built its social commerce platform on the MongoDB Community Edition , and later migrated to MongoDB Atlas in 2022. The platform provides everything from managing orders to handling logistics for companies and individuals selling on social platforms. It addresses common social commerce pain points, such as complex logistics, failed deliveries, and unmanageable order processing due to scale. Speaking at MongoDB.local Jakarta 2024 , Wafda Mufti, Vice President of Technology for Ordivo Group, explained how his slogan—“Simple Input, High Accuracy”—drove OrderOnline to become one of Indonesia’s leading social commerce companies. “We have sellers using storefronts, landing pages, and checkout forms. Thanks to MongoDB's flexibility, we can manage these unpredictable business processes. We even store our front-end structure in MongoDB,” said Mufti. “Thanks to MongoDB, we can ensure we have the highest quality of data.” Mufti also shared how the company is using MongoDB Atlas Search and MongoDB Atlas Vector Search to power innovative search and AI use cases. Scaling social commerce with MongoDB Atlas Five years after its launch, OrderOnline had grown to 40,000 users and was handling 1.5 million transactions each month. This fast growth led to challenges, particularly around managing data at scale and ensuring high success rates for sellers. Most of OrderOnline’s users drive orders by using a wide range of sources. These include social ads, landing pages, and storefronts. Many of OrderOnline’s orders are handled via WhatsApp through Click to WhatsApp Ads (CTWA). Initially, managing orders via platforms like WhatsApp was feasible. However, as social commerce became more popular, the volume of orders increased, which quickly became overwhelming. Furthermore, for large sellers who do not handle their own products, OrderOnline had to manage order packing and shipping, as well as managing returns. “We were overwhelmed with orders, but we wanted to manage our SLAs,” said Mufti. “We wanted to ensure products were well-delivered.” MongoDB Atlas’s flexibility has enabled OrderOnline to manage unpredictable business processes, and to efficiently handle various complex tasks associated with order management and logistics. Because MongoDB Atlas is designed for fast iteration, it enables OrderOnline to swiftly adapt its platform in response to changing business needs and user demands. MongoDB Atlas also supports high scalability. This empowers OrderOnline to manage a growing user base and increasing transaction volumes without compromising performance. Additionally, MongoDB Atlas's reliability under high transactional loads ensures that OrderOnline can maintain quick response times—a core part of their SLA. This is critical for maintaining the agility needed in the dynamic world of social commerce. “We have a monitoring system that triggers alarms if response times fall below one second,” noted Mufti. Another critical SLA that OnlineOrder tracks is the delivery success rate. Previously, deliveries were only successful 94% of the time. Using MongoDB Atlas, OrderOnline built OExpress, a service that sellers can use to customize the number of delivery attempts based on specific service agreements. An upper limit cap of up to five delivery attempts is also mandated. OExpress closely tracks delivery attempts data. This ensures packages are delivered and minimizes returns and damages. “Thanks to MongoDB, we have achieved a success rate of 98.4%,” said Mufti. “We can manage multiple attempts to deliver to the customers, so sellers don’t have to worry about dealing with delivery issues anymore when using a marketplace.” Beyond deliveries, OrderOnline identified seamless search and customer support integrations as key operations that MongoDB could enhance. AI and search: conversion rates jump by 56% As OrderOnline’s business grew, scalability created specific challenges with CTWAs. Particularly, OrderOnline’s platform struggled to manage and make sense of the growing volume of inconsistent data types it was receiving, such as location, postal codes, and product details—accurate input of data is vital to ensuring orders are being processed and delivered. “People want [to be able to input] freeform text. They want things to be simple and easy, and not be restricted by rigid formats,” said Mufti. “But we still have to ensure data accuracy.” One of the standout features that helped OrderOnline improve search accuracy and management is MongoDB Atlas Search . Fuzzy search in MongoDB Atlas Search can handle typo errors when searching for districts. For example, if a user types “Surabaya,” Atlas Search will still fetch results for “Surabaya”. Furthermore, synonyms in MongoDB Atlas Search can handle shortened names for provinces and districts in Indonesia. For example, “Jabar” for Jawa Barat or “Jateng” for Jawa Tengah. Acronyms are also handled. “Because there’s AI in the background, there’s no need to manually input zip codes for example. Our engine can search for it,” said Mufti. “Someone clicks, then places an order, fills out the form, and it goes straight into our order management system, which supports fuzzy search.” As OrderOnline grew, it also needed to increase customer support with 24/7 availability and fast response times. MongoDB Atlas Vector Search supported the development of a seamless and user-friendly interface with the creation of an AI Chatbot. This chatbot provides sellers with ease in managing customer interactions, checking stock availability, and calculating shipping costs. “If the ad contains a WhatsApp link, it will be directly managed by the chatbot. The chatbot even checks shipping costs, compares prices, and shows how much it would cost if you purchased five items,” explained Mufti. “The AI handles requests for photos, checks stock availability, and much more. And once a deal is closed, it goes directly into our order management system.” Before the creation of the AI chatbot with MongoDB Atlas Vector Search, the WhatsApp conversion rate was 50%. Out of 100 interactions, 50 would successfully close the deal. With the implementation of AI, this rate has increased to 78%. Building on these successes, OrderOnline is now looking at further business and geographic expansion supported by MongoDB’s global reach, with the aim to help more sellers throughout Indonesia make the best out of social commerce. Visit the MongoDB Atlas Learning Hub to boost your MongoDB skills. To learn more about MongoDB Atlas Search, visit our product page . Get started with Atlas Vector Search today through our quick start guide .
Capgemini & MongoDB: Smarter AI and Data for Business
AI is reshaping the way enterprises operate, but one fundamental challenge still exists: Most applications were not built with AI in mind. Traditional enterprise systems are designed for transactions, not intelligent decision-making, making it difficult to integrate AI at scale. To bridge this gap, MongoDB and Capgemini are enabling businesses to modernize their infrastructure, unify data platforms, and power AI-driven applications. This blog explores the trends driving the AI revolution and the role that Capgemini and MongoDB play in powering AI solutions. The Challenge: Outdated infrastructure is slowing AI innovation In talking to many customers across industries, we have heard the following key challenges in adopting AI: Data fragmentation: Organizations have long struggled with siloed data, where operational and analytical systems exist separately, making it difficult to unify data for AI-driven insights. In fact, according to the Workday Global survey , 59% of C-suite executives said their organizations' data is somewhat or completely siloed, which results in inefficiencies and lost opportunities. Moreover, AI workloads such as retrieval-augmented generation (RAG), semantic search , and recommendation engines require vector databases, yet most traditional data architectures fail to support these new AI-driven capabilities. Lack of AI-ready data infrastructure: The lack of AI-ready data infrastructure forces developers to work with multiple disconnected systems, adding complexity to the development process. Instead of seamlessly integrating AI models, developers often have to manually sync data, join query results across multiple platforms, and ensure consistency between structured and unstructured data sources. This not only slows down AI adoption but also significantly increases the operational burden. The solution: AI-Ready data infrastructure with MongoDB and Capgemini Together, MongoDB and Capgemini provide enterprises with the end-to-end capabilities needed to modernize their data infrastructure and harness AI's full potential. MongoDB provides a flexible document model that allows businesses to store and query structured, semi-structured, and unstructured data seamlessly, a critical need for AI-powered applications. Its vector search capabilities enable semantic search, recommendation engines, RAG, and anomaly detection, eliminating the need for complex data pipelines while reducing latency and operational overhead. Furthermore, MongoDB’s distributed and serverless architecture ensures scalability, allowing businesses to deploy real-time AI workloads like chatbots, intelligent search, and predictive analytics with the agility and efficiency needed to stay competitive. Capgemini plays a crucial role in this transformation by leveraging AI-powered automation and migration frameworks to help enterprises restructure applications, optimize data workflows, and transition to AI-ready architectures like MongoDB. Using generative AI, Capgemini enables organizations to analyze existing systems, define data migration scripts, and seamlessly integrate AI-driven capabilities into their operations. Real-world use cases Let's explore impactful real-world use cases where MongoDB and Capgemini have collaborated to drive cutting-edge AI projects. AI-powered field operations for a global energy company: Workers in hazardous environments, such as oil rigs, previously had to complete complex 75-field forms, which slowed down operations and increased safety risks. To streamline this process, the company implemented a conversational AI interface, allowing workers to interact with the system using natural language instead of manual form-filling. This AI-driven solution has been adopted by 120,000+ field workers, significantly reducing administrative workload, improving efficiency, and enhancing safety in high-risk conditions. AI-assisted anomaly detection in the automotive industry: Manual vehicle inspections often led to delays in diagnostics and high maintenance costs, making it difficult to detect mechanical issues early. To address this, an automotive company implemented AI-powered engine sound analysis, which used vector embeddings to identify anomalies and predict potential failures before they occurred. This proactive approach has reduced breakdowns, optimized maintenance scheduling, and improved overall vehicle reliability, ensuring cost savings and enhanced operational efficiency. Making insurance more efficient: GenYoda, an AI-driven solution developed by Capgemini, is revolutionizing the insurance industry by enhancing the efficiency of professionals through advanced data analysis. By harnessing the power of MongoDB Atlas Vector Search, GenYoda processes vast amounts of customer information including policy statements, premiums, claims histories, and health records to provide actionable insights. This comprehensive analysis enables insurance professionals to swiftly evaluate underwriters' reports, construct detailed health summaries, and optimize customer interactions, thereby improving contact center performance. Remarkably, GenYoda can ingest 100,000 documents within a few hours and deliver responses to user queries in just two to three seconds, matching the performance of leading AI models. The tangible benefits of this solution are evident; for instance, one insurer reported a 15% boost in productivity, a 25% acceleration in report generation—leading to faster decision-making—and a 10% reduction in manual efforts associated with PDF searches, culminating in enhanced operational efficiency. Conclusion As AI becomes operational, real-time, and mission-critical for enterprises, businesses must modernize their data infrastructure and integrate AI-driven capabilities into their core applications. With MongoDB and Capgemini, enterprises can move beyond legacy limitations, unify their data, and power the next generation of AI applications. For more, watch this TechCrunch Disrupt session by Steve Jones (EVP, Data-Driven Business & Gen AI at Capgemini) and Will Shulman (former VP of Product at MongoDB) to learn about more real world use cases. And discover how Capgemini and MongoDB are driving innovation with AI and data solutions.
Reimagining Investment Portfolio Management with Agentic AI
Risk management in capital markets is becoming increasingly complex for investment portfolio managers. The need to process vast amounts of data—from real-time market to unstructured social media data—demands a level of flexibility and scalability that traditional systems struggle to keep up with. AI agents —a type of artificial intelligence that can operate autonomously and take actions based on goals and real-world interactions—are set to transform how investment portfolios are managed. According to Gartner, 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024. At least 15% of day-to-day work decisions are being made autonomously through AI agents. 1 MongoDB empowers AI agents to effectively transform the landscape of investment portfolio management. By leveraging the combination of large language models (LLMs), retrieval-augmented generation (RAG), and MongoDB Atlas Vector Search , AI agents are enabled to analyze vast financial datasets, detect patterns, and adapt in real time to changing conditions dynamically. This advanced intelligence elevates decision-making and empowers portfolio managers to enhance portfolio performance, manage market risks more effectively, and perform precise asset impact analysis. Intelligent investment portfolio management Investment portfolio management is the process of selecting, balancing, and monitoring a mix of financial assets—such as stocks, bonds, commodities, and derivatives—to achieve a higher return on investment (ROI) while managing risk effectively and proactively. It involves thoughtful asset allocation, diversification to mitigate market volatility, continuous monitoring of market conditions, and the performance of underlying assets to stay aligned with investment objectives. To stay relevant today, investment portfolio management requires the integration of diverse unstructured alternative data like financial news, social media sentiment, and macroeconomic indicators, alongside structured market data such as price movements, trading volumes, index, spreads, and historical execution records. This complex data integration presents a new level of sophistication in portfolio analytics, as outlined in Figure 1. It requires a flexible, scalable, unified data platform that can efficiently store, retrieve, and manage such diverse datasets, and pave the way for building next-gen portfolio management solutions. Figure 1. Investment portfolio analysis Incorporating MongoDB’s flexible schema accelerates data ingestion across various data sources—such as real-time market feeds, historical performance records, and risk metrics. New portfolio management solutions enabled with alternative data supports more intelligent decision-making and proactive market risk mitigation. This paradigm shift realizes deeper insights, enhances alpha generation, and refines asset reallocation with greater precision, underscoring the critical role of data in intelligent portfolio management. How MongoDB unlocks AI-powered portfolio management AI-powered portfolio asset allocation has become a desirable characteristic of modern investment strategies. By leveraging AI-based portfolio analysis, portfolio managers gain access to advanced tools that provide insights tailored to specific financial objectives and risk tolerances. This approach optimizes portfolio construction by recommending an alternate mix of assets—ranging from equities and bonds to ETFs and emerging opportunities—while continuously assessing the evolving market conditions. Figure 2 illustrates a proposed workflow for AI-powered investment portfolio management that brings diverse market data, including stock price, volatility index (VIX), and macroeconomic indicators such as GDP, interest rate, and unemployment rate, into an AI analysis layer to generate actionable and more intelligent insights. Figure 2. AI-powered investment portfolio management MongoDB’s versatile document model unlocks a more intuitive way for the storage and retrieval of structured, semi-structured, and unstructured data. This is aligned with the way developers structure the objects inside the applications. In capital markets, time series are often used to store time-based trading data and market data. MongoDB time series collections are optimal for analyzing data over time, they are designed to efficiently ingest large volumes of market data with high performance and dynamic scalability. Discovering insights and patterns from MongoDB time series collections is easier and more efficient due to faster underlying ingestion and retrieval mechanisms. Taking advantage of MongoDB Atlas Charts' business intelligence dashboard and evaluating advanced AI-generated investment insights, portfolio managers gain access to sophisticated capabilities that integrate high-dimensional insights derived from diverse datasets, revealing new patterns that can lead to enhanced decision-making for alpha generation and higher portfolio performance. MongoDB Atlas Vector Search plays a critical role in the analysis of market news sentiment by enabling context-aware retrieval of related news articles. Traditional keyword-based searches often fail to capture semantic relationships between news stories, while vector search, powered by embeddings, allows for a more contextual understanding of how different articles relate to a stock sentiment. Storing news as vectors: When stock-related news are ingested, each news article is vectorized as a high-dimensional numerical representation using an embedding model. These embeddings encapsulate the meaning and context of the text, rather than just individual words. The raw news articles are embedded and stored in MongoDB as vectors. Finding related news: Vector search is used to find news articles based on similarity algorithms, even if they don’t contain the exact same stock information. This helps in identifying patterns and trends across multiple news articles based on contextual similarity. Enhancing sentiment calculation: Instead of relying on a single news sentiment, a final sentiment score is aggregated from multiple related news sources with similar and relevant content. This prevents one individual outlier news from influencing the result and provides a more holistic view of market news sentiment. Agentic AI foundation Agentic AI incorporates an orchestrator layer that manages task execution in workflows. AI Agents can operate either fully autonomous or semi-autonomous with a human-in-the-loop (HITL). AI agents are equipped with advanced tools, models, memory, and data storage. Memory leverages both long and short-term contextual data for informed decision-making and continuity of the interactions. Tools and models enable the AI agents to decompose tasks into steps and execute them cohesively. The data storage and retrieval are pivotal to AI agent effectiveness and can be advanced by embedding and vector search capabilities. Figure 3. Agentic AI foundation AI agents’ key characteristics: Autonomy: The ability to make decisions based on the situation dynamically and to execute tasks with minimal human intervention. Chain of thoughts: The ability to perform step-by-step reasoning and breaking complex problems into logical smaller steps for better judgement and decision-making. Context aware: AI agents continuously adapt their actions based on the environment changing conditions. Learning: AI agents improve their performance over time by adapting and enhancing. Intelligent investment portfolio management with AI agents AI agents are positioned to revolutionize portfolio management by shifting from rule-based to adaptive, context aware, and AI-powered decision-making. AI-enabled portfolio management applications continuously learn, adapt, and optimize investment strategies more proactively and effectively. The future isn’t about AI replacing portfolio managers, but rather humans and AI working together to create more intelligent, adaptive, and risk-aware portfolios. Portfolio managers who leverage AI, gain a competitive edge and deeper insights to significantly enhance portfolio performance. The solution, illustrated in Figure 4 below, includes a data ingestion application, three AI Agents, and a market insight application that work in harmony to create a more intelligent, insights-driven approach to portfolio management. Data ingestion application The data ingestion application runs continuously, captures various market data, and stores it in time series or as standard collections in MongoDB. Market data: Collects and processes real-time market data, including prices, volumes, trade activity, and volatility index. Market news: Captures and extracts market and stock-related news. News data is vectorized and stored in MongoDB. Market indicators: Retrieves key macroeconomic and financial indicators, such as GDP, interest rate, and unemployment rate. AI agents In this solution, there are 3 AI agents. Market analysis agent and market news agent have AI analytical workflows. They run based on a daily schedule in a fully automated fashion, producing the expected output and storing it in MongoDB. Market assistant agent has a more dynamic workflow and is designed to play the role of an assistant to a portfolio manager. It works based on prompt engineering and agentic decision making. Market assistant agent is capable of responding to questions about asset reallocation and market risks based on current market conditions and bringing the new AI-powered insights to the portfolio managers. Market analysis agent: Analyzes market trends, volatility, and patterns to generate insights related to the risk of portfolio assets. Market news agent: Assesses the news sentiment for each of assets by analyzing news that directly and indirectly can impact the portfolio performance. This agent is empowered by MongoDB vector search. Market assistant agent: On demand and through a prompt, answers portfolio manager’s questions about market trends, risk exposure, and portfolio allocation by using data sources and insights that other agents create. Market insight application The market insight application is a visualization layer that provides charts, dashboards, and reports for portfolio managers, a series of actionable investment insights from the outputs created by AI agents. This information is generated based on a predetermined daily schedule automatically and presented to portfolio managers. Figure 4. Investment portfolio management powered by MongoDB AI agents AI agents enable portfolio managers to have an intelligent and risk-based approach by analyzing the impact of market conditions on the portfolio and its investment goals. The AI Agents capitalize on MongoDB’s powerful capabilities, including the aggregation framework and vector search, combined with embedding and generative AI models to perform intelligent analysis and deliver insightful portfolio recommendations. Next steps According to Deloitte, by 2027, AI-driven investment tools will become the primary source of advice for retail investors, with AI-powered investment management solutions projected to grow to around 80% by 2028. 2 By leveraging AI agents and MongoDB, financial institutions can unlock the full potential of AI-driven portfolio management to obtain advanced insights that allow them to stay ahead of market shifts, optimize investment strategies, and manage risk with greater confidence. MongoDB lays a strong foundation for Agentic AI journey and the implementation of next-gen investment portfolio management solutions. To learn more about how MongoDB can power AI innovation, check out these additional resources: Transforming capital markets with MongoDB | Solutions page Launching an agentic RAG chatbot with MongoDB and Dataworkz | Solutions page Demystifying AI Agents: A Guide for Beginners 7 Practical Design Patterns for Agentic Systems 1 Sun, D., " Capitalize on the AI Agent Opportunity ”, Gartner, February 27, 2025. 2 AI, wealth management and trust: Could machines replace human advisors? , World Economic Forum, Mar 17, 2025.
Multi-Agentic Systems in Industry with XMPro & MongoDB Atlas
In 2025, agentic AI applications are no longer pet projects—companies around the world are investing in software to incorporate AI agents into their business workflows. The most common use of an AI agent is to assist with research analysis or writing code. LangChain’s recent survey of over 1000 professionals across multiple industries showed that over 51% have already deployed agents in production, with 60% using the agents for research and summarization tasks. However, leveraging an AI agent for more complex tasks than research and summarization—and implementing them in industrial environments like manufacturing—presents certain challenges. For example, as new technology is introduced into already established companies, the visibility of brownfield deployments increases. This installation and configuration of new hardware or software must coexist with legacy IT systems. And, while it is easy to run an AI agent in a sandboxed environment, it is harder to integrate agents with machines and Operational Technology (OT) systems speaking industrial protocols like Modbus, PROFINET, and BACnet due to existing legacy infrastructure and an accumulation of tech debt. To ensure governance and security in industrial environments, data security policies, regulatory compliance, and governance models are essential. Agent profiles with defined goals, rules, responsibilities, and constraints must be established before agents are deployed. Additionally, addressing real-world constraints—like LLM latency—and strategically selecting use cases and database providers can enhance Al agent effectiveness and optimize response times. What’s more, the successful implementation of AI agents in industrial environments requires a number of foundational elements, including: Flexible data storage and scalability: An agent requires different types of data to function, such as agent profile, short-term memory, and long-term memory. Industrial AI agents require even more types of data, such as time series data from sensors and PLCs. They require efficient and scalable data storage that adapts to the dynamic needs of the environment. Continuous monitoring and analysis: An agent deployed in a manufacturing environment requires real-time observability of ever-changing data generated by the factory. It also needs to keep humans in the loop for any critical decisions that might affect production. High availability: Industrial environments demand near-zero downtime, making system resilience and failover capabilities essential. XMPro joins forces with MongoDB To address these challenges, we are pleased to announce XMPro’s partnership with MongoDB. XMPro offers APEX AI , a low-code control room for creating and managing advanced AI agents for industrial applications. To ensure seamless control over these autonomous agents, XMPro APEX serves as the command center for configuring, monitoring, and orchestrating agent activities, empowering operators to remain in control. Figure 1. XMPro APEX AI platform working with MongoDB Atlas. APEX AI, combined with MongoDB Atlas and MongoDB Atlas Vector Search , addresses a variety of challenges faced by developers when building AI agents for industrial environments. XMPro complements this by seamlessly integrating with industrial equipment such as SCADA systems, PLCs, IoT sensors, and ERPs, enabling continuous monitoring of operations. This integration ensures real-time data acquisition, contextualization, and advanced analytics, transforming raw data into actionable insights. XMPro’s capabilities include condition monitoring, predictive maintenance, anomaly detection, and process optimization, which help reduce downtime and improve operational efficiency while maintaining compliance and safety standards. XMPro’s industrial AI agents rely on memory persistence for contextual decision-making. MongoDB Atlas acts as the database for storing and retrieving agent memories. Using a flexible document database for storing agentic memories enables agents to store different types of data, such as conversational logs, state transitions, and telemetry data, without requiring schema re-design. The capabilities of MongoDB Atlas Vector Search empower APEX AI agents with a retrieval-augmented generation (RAG) tool, which helps to reduce LLM hallucinations. This integration allows agents to access and retrieve verified data, grounding their responses. Having database and vector search tools together in MongoDB Atlas also helps reduce agent latency and speeds up development. APEX AI-enabled multi-agent systems working together in an industrial setting. These context-aware agents can work in tandem, retrieving relevant knowledge stored in MongoDB Atlas to enable meaningful collaboration and better decision-making. XMPro APEX AI also leverages MongoDB Atlas’s robust security and high availability to ensure that agents can securely access and leverage data in real time; features such as role-based access controls, network isolation, encryption in transit and at-rest are key to why this agent-based AI solution is ideal for securing industrial production environments. MongoDB’s highly available and horizontal scalability ensures seamless data access at scale as organizations scale up their APEX AI deployments. Unlocking the future of AI in industrial automation XMPro APEX AI and MongoDB Atlas are a winning combination that paves the way for a new era of industrial automation. By tackling the core challenges of AI agents' deployment in industrial environments, we’re enabling organizations to deploy robust, intelligent, and autonomous industrial AI agents at scale. To learn more about MongoDB’s role in the manufacturing industry, please visit our manufacturing and automotive webpage . Ready to boost your MongoDB skills? Head over to our MongoDB Atlas Learning Hub to start learning today.