Artificial Intelligence
Building AI-powered Apps with MongoDB
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 Atlas 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.” MongoDB Atlas was hosted on Amazon Web Services (AWS) as part of VPBank’s cloud transformation journey. The bank chose MongoDB Atlas 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 Atlas, 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 Atlas 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 Atlas’s 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.
MongoDB Powers M-DAQ’s Anti-Money Laundering Compliance Platform
Founded and headquartered in Singapore, M-DAQ Global is a fintech powerhouse providing seamless cross-border transactions for businesses worldwide. M-DAQ’s comprehensive suite of foreign exchange, collections, and payments solutions help organizations of all sizes navigate the complexities of global trade, offering FX clarity, certainty, and payment mobility. M-DAQ also offers AI-powered services like Know Your Business (KYB), onboarding, and advanced risk management tools. Amidst ever-evolving requirements, these enable business transactions across borders with ease, while staying compliant. One of M-DAQ's most innovative solutions, CheckGPT , is an AI-powered platform designed to streamline Anti-Money Laundering (AML) compliance. It was built on MongoDB Atlas , providing a strong foundation for designing multitenant data storage. This approach ensures that each client has a dedicated database, effectively preventing any data co-mingling. Traditional AML processes often involve tedious, time-consuming tasks, from document review, to background checks, to customer onboarding. By building CheckGPT, M-DAQ’s aim was to change this paradigm, and to leverage AI to automate (and speed) these manual processes. Today, CheckGPT allows businesses to process onboarding 30 times faster than traditional human processing. The platform also leverages MongoDB Atlas’s native Vector Search capabilities to power intelligent semantic searches across unstructured data. The challenge: Managing unstructured, sensitive data, and performing complex searches One of CheckGPT’s priorities was to improve processes around collecting, summarizing, and analyzing data, while flagging potential risks to customers quickly and accurately. Considering the vast number and complexity of data sets its AI platform had to handle, and the strict regulatory landscape the company operates in, it was crucial that M-DAQ chose a robust database. CheckGPT needed a database that could efficiently and accurately handle unstructured data, and adapt rapidly as the data evolved. The database also had to be highly secure; to function, the AI tool would have to handle highly sensitive data, and would need to be used by companies operating in highly regulated industries. Finally, CheckGPT was looking for the ability to perform complex, high-dimensional searches to power a wide range of complex searches and real-time information analysis. MongoDB Atlas: A complete platform with unique features According to M-DAQ, there are many benefits of using MongoDB Atlas’ document model: Flexibility: MongoDB Atlas’s document model accommodates the evolving nature of compliance data, providing the flexibility needed to manage CheckGPT's dynamic data structures, such as onboarding documents and compliance workflows. Security and performance: The MongoDB Atlas platform also ensures that data remains secure throughout its lifecycle. M-DAQ was able to implement a multi-tenancy architecture that securely isolates data across its diverse client base. This ensures that the platform can handle varying compliance demands while maintaining exceptional performance, giving M-DAQ’s customers the confidence that the AML processes handled by CheckGPT are compliant with stringent regulatory standards. Vector search capabilities: MongoDB Atlas provides a unified development experience. Particularly, MongoDB Atlas Vector Search enables real-time searches across a vast amount of high-dimensional datasets. This makes it easier to verify documents, conduct background checks, and continuously monitor customer activity, ensuring fast and accurate results during AML processes. “AI, together with the flexibility of MongoDB, has greatly impacted CheckGPT, enabling us to scale operations and automate complex AML compliance processes,” said Andrew Marchen, General Manager, Payments and Co-founder, Wallex at M-DAQ Global. “This integration significantly reduces onboarding time, which typically took between 4-8 hours to three days depending on the document’s complexity, to less than 10 minutes. With MongoDB, M-DAQ is able to deliver faster and more accurate results while meeting customer needs in a secure and adaptable environment." The future of CheckGPT, powered by MongoDB M-DAQ believes that AI and data-driven technologies and tools will continue to play a central role in automating complex processes. By employing AI, M-DAQ aims to improve operational efficiency, enhance customer experiences, and scale rapidly—while maintaining high service standards. MongoDB’s flexibility and multi-cloud support will be key as M-DAQ plans to use single/multi-cluster and multi-region capabilities in the future. M-DAQ aims to explore additional features that could enhance CheckGPT's scalability and performance. The company, for example, plans to expand its use of MongoDB for future projects involving automating complex processes like compliance, onboarding, and risk management in 2025. Learn more about CheckGPT on their site . Visit our product page to learn more about MongoDB Atlas. Get started with MongoDB Atlas Vector Search today with our Atlas Vector Search Quick Start guide .
Building Gen AI with MongoDB & AI Partners | February 2025
February was big for MongoDB—and, more importantly, for anyone looking to build AI applications that deliver highly accurate, relevant information (in other words, for everyone building AI apps). MongoDB announced the acquisition of Voyage AI , a pioneer in state-of-the-art embedding and reranking models that power next-generation AI applications. Because generative AI is by nature probabilistic, models can “hallucinate”, and generate false or misleading information. This can lead to serious risks, especially in cases or industries (e.g., financial services) where accurate information is paramount. To address this, organizations building AI apps need high-quality retrieval; they need to trust that the most relevant information is extracted from their data with precision. Voyage AI’s advanced embedding and reranking models enable applications to extract meaning from highly specialized and domain-specific text and unstructured data. With roots at Stanford and MIT, Voyage AI’s world-class team is trusted by AI innovators like Anthropic, LangChain, Harvey, and Replit. Integrating Voyage AI’s technology with MongoDB will enable organizations to easily build trustworthy, AI-powered applications by offering highly accurate and relevant information retrieval deeply integrated with operational data. For more, check out MongoDB CEO Dev Ittycheria’s blog post about Voyage AI , and what this means for developers and businesses (in short, delivering high-quality results at scale). Onward! P.S. If you’re in Vegas for HumanX this week, stop by booth 412 to say hi to MongoDB! Welcoming new AI and tech partners The Voyage AI news was hardly the only exciting development last month. In February 2025, MongoDB welcomed three new AI and tech partners that offer product integrations with MongoDB. Read on to learn more about each great new partner! CopilotKit Seattle-based CopilotKit provides open source infrastructure for in-app AI copilots. CopilotKit helps organizations build production-ready copilots and agents effortlessly. “We’re excited to be partnering with MongoDB to help companies build best-in-class copilots that leverage RAG & take action based on internal data,” said Uli Barkai, Co-Founder and Chief Marketing Officer at CopilotKit. “MongoDB made it dead simple to build a scalable vector database with operational data. This collaboration enables developers to easily ship production-grade RAG applications.” Varonis Varonis is the leader in data security, protecting data wherever it lives—across SaaS, IaaS, and hybrid cloud environments. Varonis’ cloud-native Data Security Platform continuously discovers and classifies critical data, removes exposures, and detects advanced threats with AI-powered automation. “Varonis’s mission is to protect data wherever it lives,” said David Bass, Executive Vice President of Engineering and Chief Technology Officer at Varonis. “We are thrilled to further advance our mission by offering AI-powered data security and compliance for MongoDB, the database of choice for high-performance application and AI development. With this integration, joint customers can automatically discover and classify sensitive data, detect abnormal activities, secure AI data pipelines, and prevent data leaks.” Xlrt Xlrt is an automated insight-generation platform that enables financial institutions to create innovative financial credit products at scale by simplifying the financial spreading process. “We are excited to partner with MongoDB Atlas to transform AI-driven financial workflows,” said Rupesh Chaudhuri, Chief Operating Officer and Co-Founder of Xlrt. “XLRT.ai leverages agentic AI, combining graph-based contextualization, vector search, and LLMs to redefine data-driven decision-making. With MongoDB's robust NoSQL and vector search capabilities, we’re delivering unparalleled efficiency, accuracy, and scalability in automating financial processes.” 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. And visit the MongoDB AI Applications Program (MAAP) page to learn how MongoDB and the MAAP ecosystem helps organizations build applications with advanced AI capabilities.
ORiGAMi: A Machine Learning Architecture for the Document Model
The document model has proven to be the optimal paradigm for modern application schemas. At MongoDB, we've long understood that semi-structured data formats like JSON offer superior expressiveness compared to traditional tabular and relational representations. Their flexible schema accommodates dynamic and nested data structures, naturally representing complex relationships between data entities. However, the machine learning (ML) community has faced persistent challenges when working with semi-structured formats. Traditional ML algorithms, as implemented in popular libraries like scikit-learn and pandas , operate on the assumption of fixed-dimensional tabular data consisting of rows and columns. This fundamental mismatch forces data scientists to manually convert JSON documents into tabular form—a time-consuming process that requires significant domain expertise. Recent advances in natural language processing (NLP) demonstrate the power of Transformers in learning from unstructured data but their application to semi-structured data has been under-studied. To bridge this gap, MongoDB's ML research group has developed a novel Transformer-based architecture designed for supervised learning on semi-structured data (e.g., JSON data in a document model database). We call this new architecture ORiGAMi (Object Representation through Generative, Autoregressive Modelling), and we're excited to make it available to the community at github.com/mongodb-labs/origami . It includes components that make training a Transformer model feasible on datasets entailing as few as 200 labeled samples. By combining this data efficiency with the flexibility of Transformers, ORiGAMi enables prediction directly from semi-structured documents, without the cumbersome flattening and manual feature extraction required for tabular data representation. You can read more about our model on arXiv . Technical innovation The key insight behind ORiGAMi lies in its tokenization strategy: documents are transformed into sequences of key-value pairs and special structural tokens that encode nested types like arrays and subdocuments: These token sequences serve as input to the Transformer model trained to predict the next token given a portion of the document, similar to how large language models (LLMs) are trained on text tokens. What’s more, our modifications to the standard Transformer architecture include guardrails to ensure that the model only generates valid, well-formed documents, and a novel position encoding strategy that respects the order invariance of key/value pairs in JSON. These modifications also allow for much smaller models compared to LLMs, which can thus be trained on consumer hardware in minutes to hours depending on dataset size and complexity, versus days to weeks for LLMs. By reformulating classification as a next-token prediction task, ORiGAMi can predict any field within a document, including complex types like arrays and nested subdocuments. This unified approach eliminates the need for separate models or preprocessing pipelines for different prediction tasks. Example use case Our initial focus has been supervised learning: training models from labeled data to make predictions on unseen documents. Let's explore a practical example of user segmentation. Consider a collection where each document represents a user profile, containing both simple fields and complex nested structures: { "_id": "user_7842", "email": "sarah.chen@example.com", "signup_date": "2024-01-15", "device_history": [ { "device": "mobile_ios", "first_seen": "2024-01-15", "last_seen": "2024-02-11" }, { "device": "desktop_chrome", "first_seen": "2024-01-16", "last_seen": "2024-02-10" } ], "subscription": { "plan": "pro", "billing_cycle": "annual", "features_used": ["analytics", "api_access", "team_sharing"], "usage_metrics": { "storage_gb": 45.2, "api_calls_per_day": 1250, "active_projects": 8 } }, "user_segment": "enterprise_power_user" // <-- target field } Suppose you want to automatically classify users into segments like "enterprise_power_user", "smb_growth", or "early_stage_startup" based on their behavior and characteristics. Some documents in your collection already have correct labels, perhaps assigned through manual analysis or customer interviews. Traditional ML approaches would require flattening this rich document structure, leading to very sparse tables and potentially losing important hierarchical relationships. With ORiGAMi, you can: Train directly on the raw documents with existing labels Preserve the full context of nested structures and arrays Make predictions for the "user_segment" field on new users immediately after signup Update predictions as user behavior evolves without rebuilding feature pipelines Getting started with ORiGAMi We're excited to be open-sourcing ORiGAMi ( github.com/mongodb-labs/origami ) and you can read more about our model on arXiv . We've also included a command-line interface that lets users make predictions without writing any code. Training a model is as simple as pointing ORiGAMi to your MongoDB collection: origami train <mongo-uri> -d app -c users Once trained, you can generate predictions and seamlessly integrate them back into your MongoDB workflow. For example, to predict user segments for new signups (from the analytics.signups collection ) and write the resulting predictions back to MongoDB to an analytics.predicted collection: origami predict <mongo-uri> -d analytics -c signups --target user_segment --json | mongoimport -d analytics -c predicted For those looking to dive deeper, we've also included several Jupyter notebooks in the repository that demonstrate advanced features and customization options. Model performance can be improved by adjusting the hyperparameters. We're just scratching the surface of what's possible with document-native machine learning, and have many more use cases in mind. We invite you to explore the repository, contribute to the project, and share how you use ORiGAMi to solve real-world problems. Head over to the ORiGAMi github repo , play around with it, and tell us about new ways of applying it and problems it’s well-suited to solving.
AI-Powered Java Applications With MongoDB and LangChain4j
MongoDB is pleased to introduce its integration with LangChain4j , a popular framework for integrating large language models (LLMs) into Java applications. This collaboration simplifies the integration of MongoDB Atlas Vector Search into Java applications for building AI applications. The advent of generative AI has opened up many new possibilities for developing novel applications. These advancements have led to the development of AI frameworks that simplify the complexities of orchestrating and integrating LLMs and the various components of the AI stack , where MongoDB plays a key role as an operational and vector database. Simplifying AI development for Java The first AI frameworks to emerge were developed for Python and JavaScript, which were favored by early AI developers. However, Java remains widespread in enterprise software. This has led to the development of LangChain4j to address the needs of the Java ecosystem. While largely inspired by LangChain and other popular AI frameworks, LangChain4j is independently developed. As with other LLM frameworks, LangChain4j offers several advantages for developing AI systems and applications by providing: A unified API for integrating LLM providers and vector stores. This enables developers to adopt a modular approach with an interchangeable stack while ensuring a consistent developer experience. Common abstractions for LLM-powered applications, such as prompt templating, chat memory management, and function calling, offering ready-to-use building blocks for common AI applications like retrieval-augmented generation (RAG) and agents. Powering RAG and agentic systems with MongoDB and LangChain4j MongoDB worked with the LangChain4j open-source community to integrate MongoDB Atlas Vector Search into the framework, enabling Java developers to develop AI-powered applications from simple RAG to agentic applications. In practice, this means developers can now use the unified LangChain4j API to store vector embeddings in MongoDB Atlas and use Atlas Vector Search capabilities for retrieving relevant context data. These capabilities are essential for enabling RAG pipelines, where private, often enterprise data is retrieved based on relevancy and combined with the original prompt to get more accurate results in LLM-based applications. LangChain4j supports various levels of RAG, from basic to advanced implementations, making it easy to prototype and experiment before customizing and scaling your solution to your needs. A basic RAG setup with LangChain4j typically involves loading and parsing unstructured data from documents stored locally or on remote services like Amazon S3 or Azure Storage using the Document API. The process then transforms and splits the data, then embeds it to capture the semantic meaning of the content. For more details, check out the documentation on core RAG APIs . However, real-world use cases often demand solutions with advanced RAG and agentic systems. LangChain4j optimizes RAG pipelines with predefined components designed to enhance accuracy, latency, and overall efficiency through techniques like query transformation, routing, content aggregation, and reranking. It also supports AI agent implementation through dedicated APIs, such as AI Services and Tools , with function calling and RAG integration, among others. Learn more about the MongoDB Atlas Vector Search integration in LangChain4j’s documentation . MongoDB’s dedication to providing the best developer experience for building AI applications across different ecosystems remains strong, and this integration reinforces that commitment. We will continue strengthening our integration with LLM frameworks enabling developers to build more-innovative AI applications, agentic systems, and AI agents. Ready to start building AI applications with Java? Learn how to create your first RAG system by visiting our tutorial: How to Make a RAG Application With LangChain4j .