Artificial Intelligence
Building AI-powered Apps with MongoDB
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 inthe 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.
VPBank Builds OpenAPI Platform With MongoDB
Open banking is the practice of banks sharing some of their financial data and services to developers for third-party financial service providers through an API. Open banking has accelerated the digitization of the financial services and banking industries. It also helps foster innovation and enhance customer experience by helping create customer-centric, personalized services and experiences. MongoDB has been at the forefront of this revolution. Specifically, MongoDB helps financial institutions worldwide take advantage of OpenAPI . This open-source technology enables an organization’s applications, software, and digital platforms to connect and exchange data with third-party services efficiently and securely. An example is VPBank . One of Vietnam’s largest private banks, it serves over 30 million customers. In 2020, VPBank was the first Vietnamese bank to adopt MongoDB for OpenAPI. Working with MongoDB, VPBank moved to a microservices architecture , which supported the creation of its own OpenAPI platform and set a new standard for digital banking in Vietnam. Speaking at MongoDB Day in Vietnam in November 2024 , Anh K. Pham, Head of Database Services and Operations for VPBank, shared how MongoDB set up the bank for success with open banking. Migrating from the relational model to the document model Before working with MongoDB, VPBank operated in SQL . The COVID pandemic and the rise of models such as open banking in the early 2020s mandated rapid digitization of banking operations and services. VPBank realized it needed to build the next generation of intelligent banking services to remain competitive. This was not feasible with traditional relational database management systems and the SQL model. VPBank’s primary goal was to harness the power of data and to more efficiently manage unstructured data . This meant switching to an agile architecture based on microservices. “When I was introduced to NoSQL, it made sense,” said Pham. “Data is not always structured. There’s a bunch of different data points here and there, and you can’t make anything of it. But it has to be stored somewhere, it has to be read, and it has to be fed into your applications.” VPbank chose MongoDB for its ability to handle multiple workload types, which had been inadequately supported by its relational databases. These workloads include time series data , event data, real-time analytics, notifications, and big data (like transaction histories, catalog data, and JSON data). Powering 220 microservices with flexibility, scalability, and performance VPBank’s OpenAPI platform consists of over 220 microservices, and it processes more than 100 million transactions per month. By supporting these transactions, MongoDB is ultimately helping VPBank enhance customer experiences and streamline operations. By using MongoDB, VPBank can better unlock the power of its data to quickly build data-driven applications and services on its microservices architecture. It experienced three substantial benefits by using MongoDB: Flexibility: MongoDB empowers VPBank to handle complex data, conduct rapid development and iterations, and facilitate efficient API development with BSON. Scalability: MongoDB enables dynamic scaling to handle increasing workloads. Additionally, horizontal scaling distributes data across multiple servers to handle high volumes, spikes in transactions, and API requests. Performance: MongoDB performance capabilities enable VPBank to manage large volumes of data in real time, regardless of acute throughput and latency demands. We have flexibility; we have scalability; we have performance. Those are the main things we want to look at when we’re talking about banking. I need to be flexible. I need to be scalable. I need my performance to be high, because I want my customers to not wait and see if their money is going to go through or not, Ahn K. Pham, Head of Database Services and Operations, VPBank Using OpenShift Container Platform (OCP), VPBank deployed a microservices architecture to run its Open Banking services. “Choosing MongoDB as the modern database was the best choice since it can handle multiple types of data workloads with the performance we needed,” said Pham. Looking to the future VPBank plans to continue its cloud transformation journey. “We’re continuing to migrate our applications from on-premises into the cloud, and we’re continuing to modernize our applications as well,” said Pham. “That means that maybe those other databases that we used to have might be turning into MongoDB databases.” VPBank is also looking at MongoDB to support its AI-driven future: “We really want to focus on AI and data analytics, pulling information from all our customers’ transactions,” explained Pham. “We want to ensure that what we build caters to our 30-plus million customers.” Visit our MongoDB Atlas Learning Hub to boost your MongoDB skills. To learn more about MongoDB for financial services, visit our solutions page .
Transforming News Into Audio Experiences with MongoDB and AI
You wake up, brew your coffee, and start your day with a perfectly tailored podcast summarizing the latest news—delivered in a natural, engaging voice. No manual curation, no human narration, just seamless AI magic. Sounds like the future? It's happening now, powered by MongoDB and generative AI. In 2025, the demand for audio content—particularly podcasts—surged, with 9 million new active listeners in the United States alone, prompting news organizations to seek efficient ways to deliver daily summaries to their audiences. However, automating news delivery has proven to be a challenging task, as media outlets must manage dynamic article data and convert this information into high-quality audio formats at scale. To overcome these hurdles, media organizations can use MongoDB for data storage alongside generative AI for podcast creation, developing a scalable solution for automated news broadcasting. This approach unlocks new AI-driven business opportunities and can attract new customers while strengthening the loyalty of existing ones, contributing to increased revenue streams for media outlets. Check out our AI Learning Hub to learn more about building AI-powered apps with MongoDB. The secret sauce: MongoDB + AI In a news automation solution, MongoDB acts as the system’s backbone, storing news article information as flexible documents with fields like title, content, and publication date—all within a single collection. Alongside this, dynamic elements (such as the number of qualified reads) can be seamlessly integrated into the same document to track content popularity. Moreover, derived insights—e.g., sentiment analysis and key entities—can be generated and enriched through a gen AI pipeline directly within the existing collection. Figure 1. MongoDB data storage for media. This adaptable data structure ensures that the system remains both efficient and scalable, regardless of content diversity or evolving features. As a result, media outlets have created a robust framework to query and extract the latest news and metadata from MongoDB. They can now integrate AI with advanced language models to transform this information into an audio podcast. With this foundation in place, let's examine why MongoDB is well-suited for implementing AI-driven applications. Why MongoDB is the perfect fit News data is inherently diverse, with each article containing a unique mix of attributes, including main content fields (e.g. id, title, body, date, imageURL), calculated meta data (e.g. read count), generated fields with the help of GenAI (e.g. keywords, sentiment) and embeddings for semantic/vector search. Some of these elements originate from publishers, while others emerge from user interactions or AI-driven analysis. MongoDB’s flexible document model accommodates all these attributes—whether predefined or dynamically generated, within a single, adaptable structure. This eliminates the rigidity of traditional databases and ensures that the system evolves seamlessly alongside the data it manages. What’s more, speed is critical in news automation. By storing complete, self-contained documents, MongoDB enables rapid retrieval and processing without the need for complex joins. This efficiency allows articles to be enriched, analyzed, and transformed into audio content in near real-time. And scalability is built in. Whether handling a small stream of updates or processing vast amounts of constantly changing data, MongoDB’s distributed architecture ensures high availability and seamless growth, making it ideal for large-scale media applications. Last but hardly least, developers benefit from MongoDB’s agility. Without the constraints of fixed schemas, new data points—whether from evolving AI models, audience engagement metrics, or editorial enhancements—can be integrated effortlessly. This flexibility allows teams to experiment, iterate, and scale without friction, ensuring that the system remains future-proof as news consumption evolves. Figure 2. MongoDB benefits for AI-driven applications. Bringing news to life with generative AI Selecting MongoDB for database storage is just the beginning; the real magic unfolds when text meets AI-powered speech synthesis. In our labs, we have experimented with Google’s NotebookLM model to refine news text, ensuring smooth narration with accurate intonation and pacing. Putting all these pieces together, the diagram below illustrates the workflow for automating AI-based news summaries into audio conversions. Figure 3. AI-based text-to-audio conversion architecture. The process begins with a script that retrieves relevant news articles from MongoDB, using the Aggregation Framework and Vector Search to ensure semantic relevance. These selected articles are then passed through an AI-powered pipeline, where they are condensed into a structured podcast script featuring multiple voices. Once the script is refined, advanced text-to-speech models transform it into high-quality audio, which is stored as a .wav file. To optimize delivery, the generated podcast is cached, ensuring seamless playback for users on demand. The result? A polished, human-like narration, ready for listeners in MP3 format. Thanks to this implementation, media outlets can finally let go of the robotic voices of past automations. Instead, they can now deliver a listening experience to their customers that's human, engaging, and professional. The future of AI-powered news consumption This system isn’t just a technological innovation; it’s a revolution in how we consume news. By combining MongoDB’s efficiency with AI’s creative capabilities, media organizations can deliver personalized, real-time news summaries without human intervention. It’s faster, smarter, and scalable—ushering in a new era of automated audio content. Want to build the next-gen AI-powered media platform? Start with MongoDB and let your content speak for itself! To learn more about integrating AI into media systems using MongoDB, check out the following resources to guide your next steps: The MongoDB Solutions Library: Gen AI-powered video summarization The MongoDB Blog: AI-Powered Media Personalization: MongoDB and Vector Search
GraphRAG with MongoDB Atlas: Integrating Knowledge Graphs with LLMs
A key challenge AI developers face is providing context to large language models (LLMs) to build reliable AI-enhanced applications; retrieval-augmented generation (RAG) is widely used to tackle this challenge. While vector-based RAG, the standard (or baseline) implementation of retrieval-augmented generation, is useful for many use cases, it is limited in providing LLMs with reasoning capabilities that can understand relationships between diverse concepts scattered throughout large knowledge bases. As a result, the accuracy of vector RAG-enhanced LLM outputs in applications can disappoint—and even mislead—end users. Now generally available, MongoDB Atlas ’ new LangChain integration for GraphRAG—a variation of RAG architecture that integrates a knowledge graph with LLMs—can help address these limitations. GraphRAG: Connecting the dots First, a short explanation of knowledge graphs: a knowledge graph is a structured representation of information in which entities (such as people, organizations, or concepts) are connected by relationships. Knowledge graphs work like maps, and show how different pieces of information relate to each other. This structure helps computers understand connections between facts, answer complex questions, and find relevant information more easily. Traditional RAG applications split knowledge data into chunks, vectorize them into embeddings, and then retrieve chunks of data through semantic similarity search; GraphRAG builds on this approach. But instead of treating each document or chunk as an isolated piece of information, GraphRAG considers how different pieces of knowledge are connected and relate to each other through a knowledge graph. Figure 1. Embedding-based vector search vs. entity-based graph search. GraphRAG improves RAG architectures in three ways: First, GraphRAG can improve response accuracy . Integrating knowledge graphs into the retrieval component of RAG has shown significant improvements in multiple publications. For example, benchmarks in the AWS investigation, “ Improving Retrieval Augmented Generation Accuracy with GraphRAG ” demonstrated nearly double the correct answers compared to traditional embedding-based RAG. Also, embedding-based methods rely on numerical vectors and can make it difficult to interpret why certain chunks are related. Conversely, a graph-based approach provides a visual and auditable representation of document relationships. Consequently, GraphRAG offers more explainability and transparency into retrieved information for improved insight into why certain data is being retrieved. These insights can help optimize data retrieval patterns to improve accuracy. Finally, GraphRAG can help answer questions that RAG is not well-suited for—particularly when understanding a knowledge base's structure, hierarchy, and links is essential . Vector-based RAG struggles in these cases because breaking documents into chunks loses the big picture. For example, prompts like “What are the themes covered in the 2025 strategic plan?” are not well handled. This is because the semantic similarity between the prompt, with keywords like “themes,” and the actual themes in the document may be weak, especially if they are scattered across different sections. Another example prompt like, “What is John Doe’s role in ACME’s renewable energy projects?” presents challenges because if the relationships between the person, the company, and the related projects are mentioned in different places, it becomes difficult to provide accurate responses with vector-based RAG. Traditional vector-based RAG can struggle in cases like these because it relies solely on semantic similarity search. The logical connections between different entities—such as contract clauses, legal precedents, financial indicators, and market conditions—are often complex and lack semantic keyword overlap. Making logical connections across entities is often referred to as multi-hop retrieval or reasoning in GraphRAG. However, GraphRAG has its own limitations, and is use-case dependent to achieve better accuracy than vector-based RAG: It introduces an extra step: creating the knowledge graph using LLMs to extract entities and relationships. Maintaining and updating the graph as new data arrives becomes an ongoing operational burden. Unlike vector-based RAG, which requires embedding and indexing—a relatively lightweight and fast process—GraphRAG depends on a large LLM to accurately understand, map complex relationships, and integrate them into the existing graph. The added complexity of graph traversal can lead to response latency and scalability challenges as the knowledge base grows. Latency is closely tied to the depth of traversal and the chosen retrieval strategy, both of which must align with the specific requirements of the application. GraphRAG introduces additional retrieval options . While this allows developers more flexibility in the implementation, it also adds complexity. The additional retrieval options include keyword and entity-based retrieval, semantic similarity on the first node, and more. MongoDB Atlas: A unified database for operational data, vectors, and graphs MongoDB Atlas is perfectly suited as a unified database for documents, vectors, and graphs. As a unified platform, it’s ideal for powering LLM-based applications with vector-based or graph-based RAG. Indeed, adopting MongoDB Atlas eliminates the need for point or bolt-on solutions for vector or graph functionality, which often introduce unnecessary complexity, such as data synchronization challenges that can lead to increased latency and potential errors. The unified approach offered by MongoDB Atlas simplifies the architecture and reduces operational overhead, but most importantly, it greatly simplifies the development experience. In practice, this means you can leverage MongoDB Atlas' document model to store rich application data, use vector indexes for similarity search, and model relationships using document references for graph-like structures. Implementing GraphRAG with MongoDB Atlas and LangChain Starting from version 0.5.0, the langchain-mongodb package introduces a new class to simplify the implementation of a GraphRAG architecture. Figure 2. GraphRAG architecture with MongoDB Atlas and LangChain First, it enables the automatic creation of a knowledge graph. Under the hood, it uses a specific prompt sent to an LLM of your choice to extract entities and relationships, structuring the data to be stored as a graph in MongoDB Atlas. Then, it sends a query to the LLM to extract entities and then searches within the graph to find connected entities, their relationships, and associated data. This information, along with the original query, then goes back to the LLM to generate an accurate final response. MongoDB Atlas’ integration in LangChain for GraphRAG follows an entity-based graph approach. However, you can also develop and implement your own GraphRAG with a hybrid approach using MongoDB drivers and MongoDB Atlas’ rich search and aggregation capabilities. Enhancing knowledge retrieval with GraphRAG GraphRAG complements traditional RAG methods by enabling deeper understanding of complex, hierarchical relationships, supporting effective information aggregation and multi-hop reasoning. Hybrid approaches that combine GraphRAG with embedding-based vector search further enhance knowledge retrieval, making them especially effective for advanced RAG and agentic systems. MongoDB Atlas’ unified database simplifies RAG implementation and its variants, including GraphRAG and other hybrid approaches, by supporting documents, vectors, and graph representations in a unified data model that can seamlessly scale from prototype to production. With robust retrieval capabilities ranging from full-text and semantic search to graph search, MongoDB Atlas provides a comprehensive solution for building AI applications. And its integration with proven developer frameworks like LangChain accelerates the development experience—enabling AI developers to build more advanced and efficient retrieval-augmented generation systems that underpin AI applications. Ready to dive into GraphRAG? Learn how to implement it with MongoDB Atlas and LangChain. Head over to the Atlas Learning Hub to boost your MongoDB skills and knowledge.
Next-Generation Mobility Solutions with Agentic AI and MongoDB Atlas
Driven by advancements in vehicle connectivity, autonomous systems, and electrification, the automotive and mobility industry is currently undergoing a significant transformation. Vehicles today are sophisticated machines, computers on wheels, that generate massive amounts of data, driving demand for connected and electric vehicles. Automotive players are embracing artificial intelligence (AI), battery electrical vehicles (BEVs), and software-defined vehicles (SDVs) to maintain their competitive advantage. However, managing fleets of connected vehicles can be a challenge. As cars get more sophisticated and are increasingly integrated with internal and external systems, the volume of data they produce and receive greatly increases. This data needs to be stored, transferred, and consumed by various downstream applications to unlock new business opportunities. This will only grow: the global fleet management market is projected to reach $65.7 billion by 2030, growing at a rate of almost 10.8% annually. A 2024 study conducted by Webfleet showed that 32% of fleet managers believe AI and machine learning will significantly impact fleet operations in the coming years; optimizing route planning and improving driver safety are the two most commonly cited use cases. As fleet management software providers continue to invest in AI, the integration of agentic AI can significantly help with things like route optimization and driver safety enhancement. For example, AI agents can process real-time traffic updates and weather conditions to dynamically adjust routes, ensuring timely deliveries while advising drivers on their car condition. This proactive approach contrasts with traditional reactive methods, improving vehicle utilization and reducing operational and maintenance costs. But what are agents? In short, they are operational applications that attempt to achieve goals by observing the world and acting upon it using the data and tools the application has at its disposal. The term "agentic" denotes having agency, as AI agents can proactively take steps to achieve objectives without constant human oversight. For example, rather than just reporting an anomaly based on telemetry data analysis, an agent for a connected fleet could autonomously cross-check that anomaly against known issues, decide whether it's critical or not, and schedule a maintenance appointment all on its own. Why MongoDB for agentic AI Agentic AI applications are dynamic by nature as they require the ability to create a chain of thought, use external tools, and maintain context across their entire workflow. These applications generate and consume diverse data types, including structured and unstructured data. MongoDB’s flexible document model is uniquely suited to handle both structured and unstructured data as vectors. It allows all of an agent’s context, chain-of-thought, tools metadata, and short-term and long-term memory to be stored in a single database. This means that developers can spend more time on innovation and rapidly iterate on agent designs without being constrained by rigid schemas of a legacy relational database. Figure 1. Major components of an AI agent. Figure 1 shows the major components of an AI agent. The agent will first receive a task from a human or via an automated trigger, and will then use a large language model (LLM) to generate a chain of thought or follow a predetermined workflow. The agent will use various tools and models during its run and store/retrieve data from a memory provider like MongoDB Atlas . Tools: The agent utilizes tools to interact with the environment. This can contain API methods, database queries, vector search, RAG application, anything to support the model Models: can be a large language model (LLM), vision language model (VLM), or a simple supervised machine learning model. Models can be general purpose or specialized, and agents may use more than one. Data: An agent requires different types of data to function. MongoDB’s document model allows you to easily model all of this data in one single database. An agentic AI spans a wide range of functional tools and context. The underlying data structures evolve throughout the agentic workflow and as an agent uses different tools to complete a task. It also builds up memory over time. Let us list down the typical data types you will find in an agentic AI application. Data types: Agent profile: This contains the identity of the agent. It includes instructions, goals and constraints. Short-term memory: This holds temporary, contextual information—recent data inputs or ongoing interactions—that the agent uses in real-time. For example, short-term memory could store sensor data from the last few hours of vehicle activity. In certain agentic AI frameworks like Langgraph, short term memory is implemented through a checkpointer. The checkpointer stores intermediate states of the agent’s actions and/or reasoning. This memory allows the agent to seamlessly pause and resume operations. Long-term memory: This is where the agent stores accumulated knowledge over time. This may include patterns, trends, logs and historical recommendations and decisions. By storing each of these data types into rich, nested documents in MongoDB, AI developers can create a single-view representation of an agent’s state and behavior. This enables fast retrieval and simplifies development. In addition to the document model advantage, building agentic AI solutions for mobility requires a robust data infrastructure. MongoDB Atlas offers several key advantages that make it an ideal foundation for these AI-driven architectures. These include: Scalability and flexibility: Connected Car platforms like fleet management systems need to handle extreme data volumes and variety. MongoDB Atlas is proven to scale horizontally across cloud clusters, letting you ingest millions of telemetry events per minute and store terabytes of telemetry data with ease. For example, the German company ZF uses MongoDB to process 90,000 vehicle messages per minute (over 50 GB of data per day) from hundreds of thousands of connected cars. The flexibility of the document model accelerates development and ensures your data model stays aligned with the real-world entities it represents. Built-in vector search: AI agents require a robust set of tools to work with. One of the most widely used tools is vector search, which allows agents to perform semantic searches on unstructured data like driver logs, error codes descriptions, and repair manuals. MongoDB Atlas Vector Search allows you to store and index high-dimensional vectors alongside your documents and to perform semantic search over unstructured data. In practice, this means your AI embeddings live right next to the relevant vehicle telemetry and operational data in the database, simplifying architectures for use cases like the connected car incident advisor, in which a new issue can be matched against past issues before passing contextual information to the LLM. For more, check out this example of how an automotive OEM leverages vector search for audio based diagnostics with MongoDB Atlas Vector Search. Time series collections and real-time data processing: MongoDB Atlas is designed for real-time applications. It provides time series collections for connected car telemetry data storage, change streams, and triggers that can react to new data instantly. This is crucial for agentic AI feedback loops, where ongoing data ingestion and learning are happening continuously. Best-in-class embedding models with Voyage AI: In early 2025, MongoDB acquired Voyage AI , a leader in embedding and reranking models. Voyage AI embedding models are currently being integrated into MongoDB Atlas, which means developers will no longer need to manage external embedding APIs, standalone vector stores, or complex search pipelines. AI retrieval will be built into the database itself, making semantic search, vector retrieval, and ranking as seamless as traditional queries. This will reduce the time required for developing agentic AI applications. Agentic AI in action: Connected fleet incident advisor Figure 2 shows a list of use cases in the Mobility sector, sorted by various capabilities that an agent might demonstrate. AI agents excel at managing multi-step tasks via context management across tasks, they automate repetitive tasks better than Robotic process automation (RPA), and they demonstrate human-like reasoning by revisiting and revising past decisions. These capabilities enable a wide range of applications both during the manufacturing of a vehicle and while it's on the road, connected and sending telemetry. We will review a use case in detail below, and will see how it can be implemented using MongoDB Atlas, LangGraph, Open AI, and Voyage AI. Figure 2. Major use cases of agentic AI in the mobility and manufacturing sectors. First, the AI agent connects to traditional fleet management software and supports the fleet manager in diagnosing and advising the drivers. This is an example of a multi-step diagnostic workflow that gets triggered when a driver submits a complaint about the vehicle's performance (for example, increased fuel consumption). Figure 3 shows the sequence diagram of the agent. Upon receiving the driver complaint, it creates a chain of thought that follows a multi-step diagnostic workflow where the system ingests vehicle data such as engine codes and sensor readings, generates embeddings using the Voyage AI voyage-3-large embedding model, and performs a vector search using MongoDB Atlas to find similar past incidents. Once relevant cases are identified, those–along with selected telemetry data–are passed to OpenAI gpt-4o LLM to generate a final recommendation for the driver (for example, to pull off immediately or to keep driving and schedule regular maintenance). All data, including telemetry, past issues, session logs, agent profiles, and recommendations are stored in MongoDB Atlas, ensuring traceability and the ability to refine diagnostics over time. Additionally, MongoDB Atlas is used as a checkpointer by LangGraph, which defines the agent's workflow. Figure 3. Sequence diagram for a connected fleet advisor agentic workflow. Figure 4 shows the agent in action, from receiving an issue to generating a recommendation. So by leveraging MongoDB’s flexible data model and powerful Vector Search capabilities, we can agentic AI can transform fleet management through predictive maintenance and proactive decision-making. Figure 4. The connected fleet advisor AI agent in action. To set up the use case shown in this article, please visit our GitHub repository . And to learn more about MongoDB’s role in the automotive industry, please visit our manufacturing and automotive webpage . Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads? ” presented by MongoDB Field CTO, Rick Houlihan.