Artificial Intelligence
Building AI-powered Apps with MongoDB
Enterprise-Level, Scalable AI with Morphik and MongoDB
As AI continues to revolutionize how large enterprises operate, the most crucial startups are those capable of turning massive amounts of unstructured information into actionable intelligence. Morphik, one of the fastest-growing AI knowledge platforms for enterprises, uses MongoDB to deliver secure, high-performance, multitenant systems that power real-world automation at scale.
Revolutionize Asset Maintenance with MongoDB and MaintainX
We’re excited to announce that MongoDB and MaintainX are joining forces to help manufacturers achieve excellence in maintenance operations. This joint solution enables a digital thread from raw production data to maintenance execution.
Building Next-gen AI agents: The MongoDB Atlas-Microsoft Foundry Integration
Generative AI is rapidly evolving from experimenting with models to relying on intelligent, autonomous multi-agent workflows that can reason, act, and adapt in real time. Together, Microsoft and MongoDB are defining the future of AI by providing companies everywhere a robust, secure, scalable foundation for building innovative, next-generation AI agents.
Unlocking Financial Services Document Intelligence with Agentic AI and MongoDB
Driven by rising customer expectations and the demand for greater efficiency, accuracy, and agility, the financial services industry is undergoing a profound transformation. Gone are the days of painstaking manual document reviews, and welcome instead to the era of agentic AI, where intelligent systems and a robust data foundation redefine how financial data is processed and understood. Powered by MongoDB’s flexible, scalable platform, organizations can seamlessly manage multimodal data to unlock insights, automate workflows, and stay ahead in this evolving landscape.
Announcing the MongoDB Plugin for Firebase Genkit
We’re thrilled to introduce the MongoDB Plugin for Genkit, designed to accelerate your AI-powered applications with advanced search and database tooling—all within the Genkit ecosystem. Whether you're building chatbots, intelligent assistants, or recommendation engines, this plugin brings together MongoDB’s cutting-edge search capabilities and Genkit’s AI workflows, enabling seamless vector, full-text, and hybrid search with zero hassle.
Smarter AI Search, Powered by MongoDB Atlas and Pureinsights
We’re excited to announce that the integration of MongoDB Atlas with the Pureinsights Discovery Platform is now generally available—bringing to life a reimagined search experience powered by keyword, vector, and gen AI. What if your search box didn’t just find results, but instead understood intent? That’s exactly what this integration delivers! Beyond search: From matching to meaning Developers rely on MongoDB’s expansive knowledge ecosystem to find answers fast. But even with a rich library of technical blogs, forum threads, and documentation, traditional keyword search often falls short—especially when queries are nuanced, multilingual, or context-driven. That’s where the MongoDB-Pureinsights solution shines. Built on MongoDB Atlas and orchestrated by the Pureinsights Discovery platform, this intelligent search experience starts with the fundamentals: fast, accurate keyword results, powered by MongoDB Atlas Search . But as queries grow more ambiguous—say, “tutorials for AI”—the platform steps up. MongoDB Atlas Vector Search with Voyage AI , available as an embedding and reranking option (now part of MongoDB), goes beyond literal keywords to interpret intent—helping applications deliver smarter, more relevant results. The outcome: smarter, semantically aware responses that feel intuitive and accurate—because they are. What’s more, with generative answers enabled, the platform synthesizes information across MongoDB’s ecosystem (blog content, forums, and technical docs) to deliver clear, contextual answers using state-of-the-art language models. But it's not just pointing you to the right page. Instead, the platform is providing the right answer, with citations, ready to use. It’s like embedding a domain-trained AI assistant directly into your search bar. “As organizations look to move beyond traditional keyword search, they need solutions that combine speed, relevance, and contextual understanding,” said Haim Ribbi, Vice President, Global CSI, VAR & Tech Partner at MongoDB. “MongoDB Atlas provides the foundation for smarter discovery, and this collaboration with Pureinsights shows how easily teams can deliver gen AI-powered search experiences using their existing content.” Built for users everywhere But intelligence alone doesn’t make it transformational. What sets this experience apart is its adaptability. Whether you’re a developer troubleshooting in Berlin or a product owner building in São Paulo, the platform tailors responses to your preferences. Prefer concise summaries or deep technical dives? Want to translate answers in real time? Need responses that reflect your role and context? You’re in control. From tone and length to language and specificity, this is a search that truly understands you—literally and figuratively. Built on MongoDB. Elevated by Voyage AI. Delivered by Pureinsights. At the core of this solution is MongoDB Atlas, which unifies fast, scalable data access to structured content through Atlas Search and Atlas Vector Search. Looking ahead, by integrating with Voyage AI’s industry-leading embedding models, MongoDB Atlas aims to make semantic search and retrieval-augmented generation (RAG) applications even more accurate and reliable. While currently in private preview, this enhancement signals a promising future for developers building intelligent, AI-powered experiences. Pureinsights handles the orchestration layer. Their Discovery Platform ingests and enriches content, blends keyword, vector, and generative search into a seamless UI, and integrates with large language models like GPT-4. The platform supports multilingual capabilities, easy deployment, and enterprise-grade scalability out of the box. While generative answers are powered by integrated large language models (LLMs) and may vary by deployment, the solution is enterprise-ready, cloud-native, and built to scale. Bringing intelligent discovery to your own data Watch the demo video to see AI-powered search in action across 4,000+ pages of MongoDB content—from community forums and blog posts to technical documentation. While the demo features MongoDB’s content, the solution is built to adapt. You can bring the same AI-powered experience to your internal knowledge base, customer support portal, or developer hub—no need to build from scratch. Visit our partner page to learn more about MongoDB and Pureinsights and how we’re helping enterprises build smarter, AI-powered search experiences. Apply for a free gen AI demo using your enterprise content.
The Future of AI Software Development is Agentic
Today in New York, our flagship MongoDB.local event is bringing together thousands of developers and tech leaders to discuss the future of building with MongoDB. Among the many exciting innovations and product announcements shared during the event, one theme has stood out: empowering developers to reliably build with AI and create AI solutions at scale on MongoDB. This post will explore how these advancements are set to accelerate developer productivity in the AI era. Ship faster with the MongoDB MCP Server Software development is rapidly evolving with AI tools powered by large language models (LLMs). From AI-driven editors like VS Code with GitHub Copilot and Windsurf, to terminal-based coding agents like Claude Code, these tools are transforming how developers work. While these tools bring tremendous productivity gains already, coding agents are still limited by the context they have. Since databases hold the core of most application-related data, access to configuration details, schemas, and sample data from databases is essential for generating accurate code and optimized queries. With Anthropic’s introduction of the Model Context Protocol (MCP) in November 2024, a new way emerged to connect AI agents with data sources and services. Database connection and interaction quickly became one of the most popular use cases for MCP in agentic coding. Today, we’re excited to announce the general availability (GA) of the MongoDB MCP Server, giving AI assistants and agents access to the context they need to explore, manage, and generate better code with MongoDB. Building on our public preview used by thousands of developers, the GA release introduces key capabilities to strengthen production readiness: Enterprise-grade authentication (OIDC, LDAP, Kerberos) and proxy connectivity. Self-hosted remote deployment support, enabling shared deployments across teams, streamlined setup, and centralized configuration. Note that we recommend following security best practices , such as implementing authentication for remote deployments. Accessible as a bundle with the MongoDB for VS Code extension , it delivers a complete experience: visually explore your database with the extension or interact with the same connection through your AI assistant, all without switching context. Figure 1. Overview of the MongoDB MCP Server. Meeting developers where they are with n8n and CrewAI integrations AI is transforming how developers build with MongoDB, not just in coding workflows, but also in creating AI applications and agents. From retrieval-augmented generation (RAG) to powering agent memory, these systems demand a database that can handle diverse data types—such as unstructured text (e.g., messages, code, documents), vectors, and graphs—all while supporting comprehensive retrieval mechanisms at scale like vector and hybrid search. MongoDB delivers this in a single, unified platform: the flexible document model supports the varied data agents need to store, while advanced, natively integrated search capabilities eliminate the need for separate vector databases. With Voyage AI by MongoDB providing state-of-the-art embedding models and rerankers, developers get a complete foundation for building intelligent agents without added infrastructure complexity. As part of our commitment to making MongoDB as easy to use as possible, we’re excited to announce new integrations with n8n and CrewAI . n8n has emerged as one of the most popular platforms for building AI solutions, thanks to its visual interface and out-of-the-box components that make it simple and accessible to create reliable AI workflows. This integration adds official support for MongoDB Atlas Vector Search , enabling developers to build RAG and agentic RAG systems through a flexible, visual interface. It also introduces an agent chat memory node for n8n agents, allowing conversations to persist by storing message history in MongoDB. Figure 2. Example workflow with n8n and MongoDB powering an AI agent. Meanwhile, CrewAI—a fast-growing open-source framework for building and orchestrating AI agents—makes multi-agent collaboration more accessible to developers. As AI agents take on increasingly complex and productive workflows such as online research, report writing, and enterprise document analysis, multiple specialized agents need to interact and delegate tasks with each other effectively. CrewAI provides an easy and approachable way to build such multi-agent systems. Our official integration adds support for MongoDB Atlas Vector Search , empowering developers to build agents that leverage RAG at scale. Learn how to implement agentic RAG with MongoDB Atlas and CrewAI. The future is agentic AI is fundamentally reshaping the entire software development lifecycle, including for developers building with MongoDB. New technology like the MongoDB MCP Server is paving the way for database-aware agentic coding, representing the future of software development. At the same time, we’re committed to meeting developers where they are: integrating our capabilities into their favorite frameworks and tools so they can benefit from MongoDB’s reliability and scalability to build AI apps and agents with ease. Start building your applications with the MongoDB MCP Server today by following the Get Started guide . Visit the AI Learning Hub to learn more about building AI applications with MongoDB.
Supercharge Self-Managed Apps With Search and Vector Search Capabilities
MongoDB is excited to announce the public preview of search and vector search capabilities for use with MongoDB Community Edition and MongoDB Enterprise Server. These new capabilities empower developers to prototype, iterate, and build sophisticated, AI-powered applications directly in self-managed environments with robust search functionality. This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Versatility is one of the reasons why developers love MongoDB. MongoDB can run anywhere. 1 This includes local setups where many developers kickstart their MongoDB journey, to the largest enterprise data centers when it is time to scale, and MongoDB’s fully managed cloud service, MongoDB Atlas . Regardless of where development takes place, MongoDB effortlessly integrates with any developer's workflow. MongoDB Community Edition is the free, source-available version of MongoDB that millions of developers use to learn, test, and grow their skills. MongoDB Enterprise Server is the commercial version of MongoDB’s core database. It offers additional enterprise-grade features for companies that prefer to self-manage their deployments on-premises or in public, private, or hybrid cloud environments. With native search and vector search capabilities now available for use with Community Edition and Enterprise Server, MongoDB aims to deliver a simpler and consistent experience for building great applications wherever they are deployed. What is search and vector search? Similar to the offerings in MongoDB Atlas, MongoDB Community Edition and MongoDB Enterprise Server now support two distinct yet complementary search capabilities: Full-text search is an embedded capability that delivers a seamless, scalable experience for building relevance-based app features. Vector search enables developers to build intelligent applications powered by semantic search and generative AI using native, full-featured vector database capabilities. There are no functional limitations on the core search aggregation stages in this public preview. Therefore, $search , $searchMeta , and $vectorSearch are all supported with functional parity to what is available in Atlas, excluding features in a preview state. For more information, check out the search and vector search documentation pages. Solving developer challenges with integrated search Historically, integrating advanced search features into self-managed applications often required bolting on external search engines or vector databases to MongoDB. This approach created friction at every stage for developers and organizations, leading to: Architectural complexity: Managing and synchronizing data across multiple, disparate systems added layers of complexity, demanded additional skills, and complicated development workflows. Operational overhead: Handling separate provisioning, security, upgrades, and monitoring for each system placed a heavy load on DevOps teams. Decreased developer productivity: Developers are forced to learn and use different query APIs and languages for both the database and the search engine. This resulted in frequent context switching, steeper learning curves, and slower release cycles. Consistency challenges: Aligning the primary database with separate search or vector indexes risked producing out-of-sync results. Despite promotions of transactional guarantees and data consistency, these indexes were only eventually consistent. This led to incomplete results in rapidly changing environments. With search and vector search now integrated into MongoDB Community Edition and MongoDB Enterprise Server, these trade–offs disappear. Developers can now create powerful search capabilities using MongoDB's familiar query framework, removing the synchronization burden and the need to manage multiple single-purpose systems. This release simplifies data architecture, reduces operational overhead, and accelerates application development. With these capabilities, developers can harness sophisticated out-of-the-box capabilities to build a variety of powerful applications. Potential use cases include: table, th, td { border: 1px solid black; border-collapse: collapse; } th, td { padding: 5px; } Use Case Description Keyword/Full-text search Autocomplete and fuzzy search Create real-time suggestions and correct spelling errors as users type, improving the search experience Search faceting Apply quick filtering options in applications like e-commerce, so users can narrow down search results based on categories, price ranges, and more Internal search tools Build search tools for internal use or for applications with sensitive data that require on-premises deployment Vector search AI-powered semantic search Implement semantic search and recommendation systems to provide more relevant results than traditional keyword matching Retrieval-augmented generation (RAG) Use search to retrieve factual data from a knowledge base to bring accurate, context-aware data into large language model (LLM) applications AI agents Create agents that utilize tools to collect context, communicate with external systems, and execute actions Hybrid search Hybrid search Combine keyword and vector search techniques Data processing Text analysis Perform text analysis directly in the MongoDB database MongoDB offers native integrations with frameworks such as LangChain , LangGraph , and LlamaIndex . This streamlines workflows, accelerates development, and embeds RAG or agentic features directly into applications. To learn more about other AI frameworks supported by MongoDB, check out this documentation . MongoDB’s partners and champions are already experiencing the benefits from utilizing search and vector search across a wider range of environments: “We’re thrilled that MongoDB search and vector search are now accessible in the already popular MongoDB Community Edition. Now our customers can leverage MongoDB and LangChain in either deployment mode and in their preferred environment to build cutting-edge LLM applications.”—Harrison Chase, CEO, LangChain. “MongoDB has helped Clarifresh build awesome software, and I’ve always been impressed with its rock-solid foundations. With search and vector search capabilities now available in MongoDB Community Edition, we gain the confidence of accessible source code, the flexibility to deploy anywhere, and the promise of community-driven extensibility. It’s an exciting milestone that reaffirms MongoDB’s commitment to developers.”—Luke Thompson, MongoDB Champion, Clarifresh. “We’re excited about the next interaction of search experiences in MongoDB Community Edition. Our customers want the highest flexibility to be able to run their search and gen AI-enabled applications, and bringing this functionality to Community unlocks a whole new way to build and test anywhere.”—Jerry Liu, CEO, LlamaIndex. “Participating in the Private Preview of Full-text and Vector Search for MongoDB Community has been an exciting opportunity. Having $search, $searchMeta, and $vectorSearch directly in Community Edition brings the same powerful capabilities we use in Atlas—without additional systems or integrations. Even in early preview, it’s already streamlining workflows and producing faster, more relevant results.”—Michael Höller, MongoDB Champion, akazia Consulting. Accessing the public preview The public preview is available for free and is intended for testing, evaluation, and feedback purposes only. Search and Vector Search with MongoDB Community Edition. The new capabilities are compatible with MongoDB version 8.2+, and operate on a separate binary, mongot, which interacts with the standard mongodb database binary. To get started, ensure that: A MongoDB Community Server cluster is running using one of the following three methods: Download MongoDB Community Server version 8.2 from the MongoDB Downloads page . As of public preview, this feature is available for self-managed deployments on supported Linux distributions and architectures for MongoDB Community Edition version 8.2+. Download the ```mongot``` binary from the MongoDB Downloads page . Pull the container image for Community Server 8.2 from a public Docker Hub repository . Coming soon: Deploy using the MongoDB Controllers for Kubernetes Operator (Search Support for Community Server is planned for version 1.5+ ). Search and Vector Search for use with MongoDB Enterprise Server . The new capabilities are deployed as self-managed search nodes in a customer's Kubernetes environment. This will seamlessly connect to any MongoDB Enterprise Server clusters, residing inside or outside Kubernetes itself. To get started, ensure that: A MongoDB Enterprise Server cluster is running. version 8.0.10+ (for MongoDB Controllers for Kubernetes operator 1.4). version 8.2+ (for MongoDB Controllers for Kubernetes operator 1.5+). A Kubernetes environment. The MongoDB Controllers for Kubernetes Operator are installed in the Kubernetes cluster. Find installation instructions here . Comprehensive documentation for setup for MongoDB Community Edition and MongoDB Enterprise Server is also available. What's next? During the public preview, MongoDB will deliver additional updates and roadmap features based on customer feedback. After the public preview, these search and vector search capabilities are anticipated to be generally available for use with on-premise deployments. For Community Edition, these capabilities will be available at no additional cost as part of the Server Side Public License (SSPL) . For MongoDB Enterprise Server, these capabilities will be included in a new paid subscription offering that will launch in the future. Pricing and packaging details for the subscription will be available closer to launch. For developers seeking a fully managed experience in the cloud, MongoDB Atlas offers a production-ready version of these capabilities today. MongoDB would love to hear feedback! Suggest new features or vote on existing ideas at feedback.mongodb.com . The input is critical for shaping the future of this product. Users can contact their MongoDB account team to provide more comprehensive feedback. Check out MongoDB’s documentation to learn how to get started with Search and Vector Search in MongoDB Community Edition and MongoDB Enterprise Server . 1 MongoDB can be deployed as a fully managed multi-cloud service across all major public cloud providers, in private clouds, locally, on-premises and hybrid environments.
MongoDB AMP: An AI-Driven Approach to Modernization
Why should a database company be your modernization partner? It’s a fair question. From over a decade of experience with database migrations, we've learned that the database is often the single biggest blocker preventing digital transformation. It's where decades of business logic have been embedded, where critical dependencies multiply, and where the complexity that blocks innovation actually lives. But by working with MongoDB, customers have found that transforming their data layer removed the barriers that had stalled previous modernization attempts. This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Now, with today’s launch of the MongoDB Application Modernization Platform (AMP), we're providing customers a proven approach to full-stack modernization. MongoDB AMP is an AI-powered solution that rapidly and safely transforms legacy applications into modern, scalable services. MongoDB AMP integrates agentic AI workflows into our modernization methodology, alongside reusable, battle-tested tooling, and the expertise we've developed through customer engagements over the past decade—a powerful combination of tools, technique, and talent. By combining AMP tooling with MongoDB’s proven, repeatable framework, customers have seen tasks like code transformation sped up by 10x or more—with overall modernization projects implemented 2–3 times faster on average. Figure 1. The MongoDB Application Modernization Platform. The common challenges Many of our customers are facing the same impossible choice: accept growing technical debt that slows every business initiative, or risk disruption with a full system rewrite. Their teams are stuck maintaining legacy code instead of building new capabilities. These legacy systems have evolved into interconnected webs (“spaghetti messes”) where even simple changes require coordination across multiple systems and teams. Database changes require corresponding updates to middleware integrations, application business logic, and user interface components. Teams struggle to update systems because any change brings risks breaking something else they don't fully understand. Innovation initiatives often get blocked because new capabilities struggle to integrate within the constraints of legacy systems. Technical debt accumulates with every workaround, making each subsequent change more complex and risky than the last. Before working with MongoDB, Intellect Design's Wealth Management platform exemplified this challenge perfectly . Key business logic was locked in hundreds of SQL stored procedures, leading to batch processing delays of up to eight hours and limiting scalability as transaction volumes grew. The platform’s rigid architecture hindered innovation and blocked integration with other systems, such as treasury and insurance platforms, preventing the delivery of unified financial services that their enterprise clients demanded. In cases like this, the result is stagnation disguised as stability. Systems "work" but can't evolve. Applications can handle today's requirements, but can't adapt to tomorrow's opportunities. Legacy architectures have become the foundation on which everything else depends—and the constraint that prevents everything else from changing. Battle-tested solutions By working through challenges with customers, we've built a comprehensive methodology for modernization, backed by sophisticated tools that address the messy reality of legacy applications. Our approach empowers application teams with proven processes and purpose-built technology to systematically address key challenges. Central to our methodology is a test-first philosophy that has proven essential for safe, reliable modernization. Before any transformation begins, we develop comprehensive test coverage for existing applications, creating a baseline that captures how legacy systems actually behave in production. This upfront investment in testing becomes the foundation for everything that follows, providing guardrails that ensure modernized code performs identically to the original while giving teams the confidence to make changes without fear of breaking critical business processes. Our test-driven approach ensures modernization is a methodical, validated process where every change is verified. Before we make any code changes, we establish a complete picture of the legacy system. We've built sophisticated analysis tools that comprehensively map legacy application architectures. These tools uncover the complex interdependencies and embedded logic that make legacy applications far more intricate than they appear on the surface. This deep analysis isn't just about cataloging complexity; it's about understanding the true scope, informing execution of the transformation, and identifying potential risks before they derail projects. Analysis is just the start. By working with customers, we've learned that successful modernization requires careful sequencing and planning. Our dependency analysis capabilities help teams understand not just what needs to be migrated, but the critical order of operations and what safeguards need to be in place at each step. It's critical to avoid the temptation to migrate everything at once. MongoDB’s approach is designed to make complex modernizations successful by transforming applications incrementally with robust validation. Instead of crossing your fingers and hoping everything works after months of development, our methodology decomposes large modernization efforts into manageable components where every component is iteratively tested and verified. Issues are caught early when they're easy to fix, not after months of development when rollback becomes costly and complex. Each successful iteration reduces risk rather than accumulating it. The agentic AI acceleration MongoDB AMP represents over two years of dedicated effort to integrate AI-powered automation into our battle-tested processes, dramatically accelerating modernization while maintaining the reliability our customers depend on. AI powerfully expands our validation processes by generating additional test cases to validate modernized applications against their legacy counterparts. This dramatically improves confidence in migration results while reducing the time teams spend manually creating test cases for the complex business logic they are trying to preserve. Our existing analysis tools, which decompose embedded logic into smaller segments, now feed directly into AI systems that can automatically transform the code components they discover. What once required weeks of manual code conversion can now happen in hours, with testing frameworks providing the same rigorous validation we've always insisted on. For example, Bendigo and Adelaide Bank reduced the development time to migrate a banking application by up to 90% . The difference is speed and scale, without sacrificing quality or safety. Figure 2. The AMP process. Years of customer engagement and refined processes provide the foundation and guardrails that make AI-powered modernization effective and safe. With MongoDB AMP, AI becomes a force multiplier that transforms our proven approach into something that can tackle modernization challenges at unprecedented speed and scale. Migrating simple code is now 50 to 60 times quicker, and we can migrate small applications 20 times faster to MongoDB. Regression testing also went from three days to three hours with automated test generation. Fabrice Bidard, Head of Technical Architecture, Lombard Odier Ready to begin your modernization journey? Legacy application modernization doesn't have to be a leap of faith. With MongoDB as your partner, you gain access to proven methodologies, battle-tested tools, and the accelerated capabilities that agentic AI brings to our existing expertise. Contact our team to discuss your specific challenges and learn how our proven methodology can be applied to your environment.
Unlock AI With MongoDB and LTIMindtree’s BlueVerse Foundry
Many enterprises are eager to capitalize on gen AI to transform operations and stay competitive, but most remain stuck in proofs of concept that never scale. The problem isn’t ambition. It’s architecture. Rigid legacy systems, brittle pipelines, and fragmented data make it hard to move from idea to impact. That’s why LTIMindtree partnered with MongoDB to create BlueVerse Foundry : a no-code, full-stack AI platform powered by MongoDB Atlas , built to help enterprises quickly go from prototype to production without compromising governance, performance, or flexibility. The power of MongoDB: Data without limits At the heart of this platform is MongoDB Atlas, a multi-cloud database that redefines how enterprises manage and use data for AI. Unlike traditional relational databases, MongoDB’s document model adapts naturally to complex, evolving data, without the friction of rigid schemas or heavy extract, transform, and load pipelines. For AI workloads that rely on diverse formats like vector embeddings, images, or audio, MongoDB is purpose built. Its real-time data capabilities eliminate delays and enable continuous learning and querying. Search is another differentiator. With MongoDB Atlas Search and Atlas Vector Search , MongoDB enables enterprises to combine semantic and keyword queries for highly accurate, context-aware results. GraphRAG adds another layer, connecting relationships in data through retrieval-augmented generation (RAG) to reveal deeper insights. Features like semantic caching ensure performance remains high even under pressure, while built-in support for both public and private cloud deployments makes it easy to scale. Together, these capabilities turn MongoDB from a data store into an AI acceleration engine, supporting everything from retrieval to real-time interaction to full-stack observability. The challenge: Building with limitations Traditional systems were never designed for the kind of data modern AI requires. As enterprises embrace gen AI models that integrate structured and unstructured data, legacy infrastructure shows its cracks. Real-time processing becomes cumbersome, multiple environments create redundancy, and rising computing needs inflate costs. Building AI solutions often demands complex coding, meticulous model training, and extensive infrastructure planning, resulting in a delayed time to market. Add to that the imperative of producing responsible AI, and the challenge becomes even steeper. Models must not only perform but also be accurate, unbiased, and aligned with ethical standards. Enterprises are left juggling AI economics, data security, lineage tracking, and governance, all while trying to deliver tangible business value. This is precisely why a flexible, scalable, and AI-ready data foundation like MongoDB is critical. Its ability to handle diverse data types and provide real-time access directly addresses the limitations of traditional systems when it comes to gen AI. The solution: A smarter way to scale AI With BlueVerse Foundry and MongoDB Atlas, enterprises get the best of both worlds: LTIMindtree’s rapid no-code orchestration and MongoDB’s flexible, scalable data layer. This joint solution eliminates common AI bottlenecks and accelerates deployment, without the need for complex infrastructure or custom code. BlueVerse Foundry’s modular, no-code architecture enables enterprises to quickly build, deploy, and scale AI agents and apps without getting bogged down by technical complexity. This is significantly amplified by MongoDB’s inherent scalability, schema flexibility, and native RAG capabilities, which were key reasons for LTIMindtree choosing MongoDB as the foundational data layer. With features like the no-code agent builder, agent marketplace, and business-process-automation blueprints, enterprises can create tailored solutions that are ready for production, all powered by MongoDB Atlas. A synergistic partnership: Smarter together The collaboration between MongoDB and LTIMindtree’s BlueVerse Foundry brings together powerful AI capabilities with a future-ready database backbone. This partnership highlights how MongoDB’s AI narrative and broader partner strategy focus on enabling enterprises to build intelligent applications faster and more efficiently. Together, they simplify deployment, enable seamless integration with existing systems, and create a platform that can scale effortlessly as enterprise needs evolve. What makes this partnership stand out is the ability to turn ideas into impact faster. With no-code tools, prebuilt agents, and MongoDB’s flexible data model, enterprises don’t need to wait months to see results. They can use their existing infrastructure, plug in seamlessly, and start delivering real-time AI-driven insights almost immediately. Governance, performance, and scalability aren’t afterthoughts; they’re built into every layer of this ecosystem. “We’re seeing a shift from experimentation to execution—enterprises are ready to scale gen AI, but they need the right data foundation,” said Haim Ribbi, Vice President of Global CSI, VAR and Tech Partner at MongoDB. “That’s where MongoDB Atlas fits in, and where an agentic platform like LTIMindtree’s BlueVerse Foundry uses it to its full potential for innovation.” Real-world impact: From data to differentiated experiences This joint solution is already delivering real-world impact. A leading streaming platform used LTIMindtree’s solution, powered by MongoDB, to personalize content recommendations in real time. With MongoDB handling the heavy lifting of diverse data management and live queries, the company saw a 30% rise in user engagement and a 20% improvement in retention. Central to this transformation is the platform’s content hub, which acts as a unified data catalog, organizing enterprise information so it’s accessible, secure, and ready to power next-generation AI solutions with MongoDB’s robust data management. Whether dealing with text, images, or audio, the platform seamlessly manages multimodal data, eliminating the need for separate systems or processes. For businesses looking to accelerate development, BlueVerse Foundry and Marketplace offer a no-code builder, prebuilt agents, and templates, enabling teams to go from concept to deployment in a fraction of the time compared to traditional methods. BlueVerse Foundry’s RAG pipelines simplify building smart applications, using MongoDB Atlas Search and MongoDB Atlas Vector Search for highly effective RAG. Advanced orchestration connects directly with AI models, enabling rapid experimentation and deployment. A globally acclaimed media company has been using BlueVerse Foundry to automate content tagging and digital asset management, cutting its discovery time by 40% and reducing overheads by 15%—clear evidence of gen AI’s bottom-line impact when implemented right. BlueVerse Foundry’s strength lies in combining speed and control. By providing everything from ready-to-use user-experience kits, over 25 plug-and-play microservices, token-based economic models, 100+ safe listed large language models (LLMs), tools and agents, and full-stack observability, BlueVerse Foundry and Marketplace enables enterprises to move faster without losing sight of governance. Its support for voice interfaces, regional languages, Teams, mobile, and wearables like Meta AI Glasses ensures an omnichannel experience out of the box. Responsible AI: A built-in capability LTIMindtree doesn’t just build AI faster; it builds it responsibly. With built-in measures like LLM output evaluation, moderation, and audit trails, the platform ensures enterprises can trust the results their models generate. This is further supported by MongoDB’s robust security features and data governance capabilities, ensuring a secure and ethical AI ecosystem. It’s not just about preventing hallucinations or bias; it’s about creating an ecosystem where quality, transparency, and ethics are fundamental, not optional. Scaling: Streamlined for the long term The platform’s libraries, app galleries, and FinOps tooling enable businesses to test, deploy, and expand with confidence. Powered by MongoDB Atlas’s inherent scalability and multi-cloud flexibility, BlueVerse Foundry is built for long-term AI success, not just early experimentation. Enterprise AI: From possibility to production The BlueVerse Foundry and Marketplace, powered by MongoDB, is more than a technological partnership; it’s a new standard for enterprise AI. It combines deep AI expertise with an agile data foundation, helping organizations escape the trap of endless proofs of concept and unlock meaningful value. For enterprises still unsure about gen AI’s return on investment, this solution offers a proven path forward, grounded in real-world success, scalability, and impact. The future of AI isn’t something to wait for. With LTIMindtree and MongoDB, it’s already here. Explore how LTIMindtree and MongoDB are transforming gen AI from a concept into an enterprise-ready reality. Learn more about building AI applications with MongoDB through the AI Learning Hub .