Industry Solutions
Industry solutions and innovations driven by MongoDB.
Build Trust in Agentic AI: From POC to Production
The enterprise adoption of artificial intelligence has reached an inflection point. Organizations are rapidly moving into the era of agentic AI, autonomous systems capable of executing complex reasoning and making operational decisions independently. Yet as executives attempt to transition agents from sandbox environments into mission-critical production channels, they inevitably collide with an AI trust gap.
Designing the Future of Banking: The AI Enterprise Platform
As financial services enter an AI-first era, banks are rethinking how intelligence is embedded across every workflow—from onboarding and payments to trading, compliance, and portfolio management. Moving beyond isolated pilots to enterprise-scale AI production delivery requires a modern data platform that is secure, governed, and deeply integrated with the data layer.
Powering Energy Markets with MongoDB Atlas: A Modern Stack
European power companies are operating one infrastructure stack while their business has moved to another. Renewables now dominate new power generation. Settlement happens every fifteen minutes, and on intraday contracts every five. Regulators ask questions that look five years back. Trading desks want decision support that reads the live book and the regulation in the same breath.
Agentic Supplier Management with MongoDB Atlas, Voyage AI, and Multi-Modal Search
Retail supply chains are not a back-office logistics function; they are a high-stakes, board-level concern. Imagine learning suddenly that shipment rerouting surcharges have doubled due to new regional escalations; the impact on competitive differentiation and consumer trust is immediate. As a result, a long-standing focus on linear efficiency and lean inventory is being disrupted by a mandate for resilience and AI-driven responsiveness. To survive, retailers must move beyond the rigidity of legacy systems and embrace an AI-ready data platform that can pivot as fast as headlines change. Indeed, a 2026 study by KPMG reported that businesses are establishing new performance metrics, centered around post-disruption recovery time, supplier diversification, sourcing agility, revenue growth from improved experiences, cost savings, and employee engagement. Now, retailers are modernizing their supplier management capabilities. An effective supplier management application that boosts visibility, builds resilience, and delivers material business benefits must be underpinned by unified supplier data and AI copilots. To unlock these next-generation capabilities, retail leaders use MongoDB as a unified data foundation, enabling the high-velocity intelligence and material results required in today’s volatile landscape. However, the business agility of many organizations remains restricted by their enterprise resource planning (ERP) systems, which were designed for an era when stability was assumed, laborious data access was the norm, and delays due to batch processing were acceptable. These legacy foundations have become an operational bottleneck and a strategic threat that prevents real-time responsiveness to external shocks. The speed of supply chain decision-making is hard-capped by the difficulty of getting fast, accurate answers from supplier information buried in legacy systems, PDFs, spreadsheets, and email chains. These systems fail because they are not able to force incompatible data profiles into a one-size-fits-all table structure. Any multi-modal data, such as images and PDFs, is not queryable. By the time a supplier manager has gathered the data required to make a decision, hours, if not days, have passed. Benefits of supplier management modernization The opportunity for retailers that move decisively to modernize is measured in both profitability and market share. IDC predicts that 70% of large retailers will invest in data modernization to unlock better insights and resilience by 2027. To achieve true resilience, retailers must decouple supplier management from the ERP core and deliver a high-impact capability for the business. MongoDB facilitates low-latency data access, geospatial data, and multi-modal AI-assisted discovery that can deliver a world-class supplier management capability. By creating a dedicated application with MongoDB as its consolidated operational data layer, retailers gain the flexibility to handle modern complexities without the legacy overhead. Imagine a geopolitical escalation has triggered a 50% tariff on aluminium imports from South Korea from midnight tonight. The external event propagates its way into your modernized system, triggering a real-time identification of your impacted suppliers. The business assesses this impact and decides whether to seek alternatives. Instead of typing in a specific supplier attribute, they describe the need: "Alternative dairy partner in a tariff-neutral zone." The system scans thousands of supplier profiles and digitized contracts stored as high-dimensional vectors. Within seconds, it identifies a mid-sized supplier that hasn't been used in two years. The business delves deeper into the supplier details and decides they are a suitable alternative. The risk has been mitigated; the disruption avoided. Breaking free from the pitfalls inherent within legacy systems has ensured the business remains operationally agile in the face of external change. Figure1. An Agentic Supplier Management solution, with multi-modal search, powered by MongoDB. Agentic Supplier Management Blog - Image 1 media Operational flexibility for supplier attributes Suppliers are complex entities with varied and evolving attributes. A textile supplier in Vietnam will have very specific data requirements when compared with a packaging partner in Poland. New requirements will emerge over time, like the need to track a custom "Tariff Exposure Rating" or "Sustainability Score" for 500 suppliers in a specific region. Business users will expect a modern application to add those fields instantly to the relevant supplier profiles without taking the system offline or rewriting the schema. MongoDB’s flexible data model allows different supplier data attributes to be stored inside a single collection of suppliers. This polymorphic capability allows data to evolve at the same pace as global trade policy, without impacting core operations. Sourcing agility with semantic discovery When a primary supplier is sidelined by a localized lockdown or a shipping bottleneck, the clock starts ticking. Traditionally, finding an alternative meant a manual, frantic search through spreadsheets. In a modern system, business users will expect semantic search capabilities, low-latency experiences, and intelligent, AI-powered assistance. MongoDB provides multi-modal intelligence with Voyage AI, a specialized retrieval layer for AI applications that provides API-based embedding models and re-rankers. It enables unstructured data like documents and images to be defined as high-dimensional vectors, all stored right beside standard operational data in the same MongoDB platform. When a supplier in a disrupted region fails, MongoDB Vector Search can instantly identify alternative suppliers across your global network who have the most similar attributes. Think product attributes, lead times, and sustainability credentials. Because semantic search is based on mathematical "closeness" rather than exact keyword matches, it can surface a high-potential partner in a different region that your team might have otherwise overlooked. This transforms searching from a reactive, manual scramble into a proactive, intelligent capability Real-time, low-latency visibility In 2026, visibility is no longer a luxury; it is the heartbeat of operational survival. Most retailers are paralyzed by disconnected systems that trap critical data points in isolated silos, leaving decision-makers to act on data that is difficult to access or out-of-date. In a disruption scenario, this disconnect is fatal. Unifying supply chain data into a single, coherent layer is the only way to ensure that customer promises are grounded in current reality. Through MongoDB Change Streams, the data platform acts as a high-speed nervous system, propagating updates from legacy cores to a modernized supplier application with near-zero latency. Because MongoDB does not require a rigid, pre-defined structure for every incoming piece of data, you can instantly ingest a flow of data directly into your supplier profiles. This immediacy fundamentally changes the dynamic of an impending crisis: instead of managing the aftermath of an external issue over an extended period, the business can address the impact in minutes. Decision-making shifts from reactive guesswork to high-confidence execution, allowing businesses to reroute shipments or trigger alternative sourcing before the disruption reaches the bottom line. The foundation of resilience By leveraging MongoDB’s AI-ready data platform to modernize supplier management, retailers will achieve business outcomes that were previously impossible. When supply chain disruption inevitably occurs, the business can be empowered with AI-driven impact assessment, semantic discovery of alternative supplier options, and multi-modal data access, combining to mitigate risk and maintain consumer confidence. Figure 2. An AI-driven Supplier Management workflow with MongoDB. Agentic Supplier Management Blog - Image 2 media Market data from Congruence shows that 72% of leading retailers are investing in AI-integrated platforms, including supply chain. While the 2026 macroenvironment generates supply chain issues that result in manual struggles and customer frustration, competitors will use MongoDB to treat their supplier management agility as a dynamic engine for resilience and value. Our recommendation is simple: start your migration to a flexible, AI-ready data platform now, or prepare to be outmaneuvered by competitors that are already moving on. Agentic Supplier Management Blog - Aside aside References KPMG (2026), Key trends impacting supply chains in 2026 IDC (2025), IDC FutureScape: Worldwide Retail 2026 Predictions Congruence Market Insights (2025), Next-Gen Retail Technology Market Report: Growth Drivers, Market Dynamics & Future Potential (2026–2033)
Financial Crime Mitigation with MongoDB / Part II: Comprehensive Analysis
Welcome back to our series on Building a Financial Crime Mitigation Platform, in which we show how MongoDB ideally supports the demands from modern digital financial operations as a unified data platform. In case you missed it, be sure to check out the series overview.
Modernizing Enterprise Content Management with MongoDB
Enterprise Content Management (ECM) is mission-critical to almost every large organization. It underpins how contracts are signed, invoices are archived, citizen records are retained, and how billions of documents are searched and governed every day. Yet most ECM platforms still run on architectures designed decades ago, struggling to keep up with today’s scale, agility, and AI expectations.
Scaling Data Campaign Services for Rivian EV Fleets with MongoDB
Vehicle data campaigns are the fastest way to answer new, high-impact questions about vehicles in the field without permanently increasing baseline telemetry volume. At Rivian, campaigns must target large cohorts, route through different gateway paths for different vehicle variants, and produce results that are queryable by operational applications with predictable latency. The system also has to support multi-region high availability, high volume, high throughput, and comply with privacy and region-aware data governance regulations. This article explains what a data campaign service is, why scaling it can be challenging for automakers, and how Rivian solved those scalability challenges using MongoDB. We also share forward-looking development directions, such as using semantic search to shorten triage cycles and improve the reuse of past campaign outcomes.
LG Uplus Works With MongoDB to Expand AI Services and Modernize Architecture
LG Uplus, a key subsidiary of LG Corporation and a leader in mobile, internet, and AI transformation, today announced that it will work with MongoDB to expand the use of generative AI and accelerate its modernization strategy across the company.
Paving the Connected Vehicle Data Highway
You can engineer the fastest, most advanced vehicle in the world, but without a paved road, it is not going anywhere. In many ways, this reflects the current state of the automotive industry. Sophisticated, software-driven vehicles generate vast amounts of data. Yet the digital “roads” needed to move, structure, and use that data remain fragmented and, well, unpaved. Each automaker exposes vehicle signals differently. This makes it difficult to build services that scale across brands, platforms, and ecosystems.
Plugging the Gap in Automotive Data Interoperability
Imagine if every electric vehicle (EV) came with its own dedicated charging connector unique to its brand or model. Similar to the early days with mobile phones, charging operators would need to support a range of incompatible plugs—leaving drivers to wonder whether they could charge at a given station. Managing this disparity would quickly become impractical, slowing the ecosystem’s growth.
MongoDB as the Mandate Ledger for Agentic Commerce: Supporting A2A, AP2 & UCP
Agentic commerce is here! Retailers and technologists are faced with the task of creating new architectures to support trustworthy, secure, and auditable agentic commerce. The tech sector has moved quickly to meet this challenge with a new wave of agentic protocols. The industry is moving fast: following the launch of Agent to Agent Protocol (A2A) in April 2025, Google launched Agents Payments Protocol (AP2) in Sept 2025, followed by Unified Commerce Protocol (UCP) in January 2026.
Why I Prefer MongoDB For AI Applications
MongoDB is excited to feature this guest post from Andrei Radulescu-Banu, the builder behind DocRouter.AI and SigAgent.AI. Andrei walks through why MongoDB works well for his AI applications, especially for document- and log-heavy data and shares practical implementation details, including how he approaches migrations, indexing, and vector search–powered knowledge bases in production.
Reduce AI Hallucinations with Tavily and MongoDB Hybrid Search
Artificial intelligence applications increasingly rely on retrieval‑augmented generation (RAG) to keep large language models grounded in trusted information. But not all RAG systems are created equal. Many rely solely on internal databases, while others depend exclusively on external APIs. Both approaches can introduce hallucinations, outdated information, or limited control over content sources.
The Modern End-to-End Digital Lending Journey Powered by MongoDB and Agentic AI
Traditional lending systems rely on disconnected legacy applications that were never designed for real-time data, automation, or digital-first customer experiences. Today, customers expect instant decisions, seamless digital experiences, and immediate transparency, while lenders must manage rising risk, regulatory pressure, and data complexity. Modern digital lending platforms are transforming this reality by unifying origination, decisioning, funding, and servicing into a single, intelligent workflow. In this article, we break down the end-to-end digital lending lifecycle and show how data-driven architectures are redefining how loans are created, approved, funded, and managed instantaneously.
Modelence: A Complete Platform for Agentic App Development
As modern applications become increasingly data-driven and AI-powered, development teams face a growing challenge: how to move quickly from idea to production without stitching together multiple tools, managing complex infrastructure, or reinventing backend workflows. Modelence offers a new approach. It is a full‑stack, AI‑native development platform that brings together every core component needed to build, run, and scale modern applications in one unified system. Whether teams want to start a new project using a traditional development workflow or prefer a vibe‑coding approach powered by its AI-native App Builder, Modelence supports both seamlessly. To show you how this works in practice, we have included two examples for each workflow that you can explore in more detail later in this blog.
Asset Tokenization in Financial Services: MongoDB as the Data Foundation
For nearly more than a decade, tokenization has been one of the most talked-about concepts in financial services. From early blockchain pilots, to experimenting with real-world asset trading, to the DeFi boom (Decentralized Finance), the idea has been the same: transform traditional assets such as stocks, bonds, deposits, treasuries, or real estate into digital tokens that can move instantly and trade globally.
Automotive After Sales Diagnostics Using GraphRAG and Multimodal AI
Modern vehicles act as distributed computing systems and generate terabytes of telemetry. However, the majority of after-sales diagnostic and repair workflows still depend on static documentation and basic keyword search. In 2025, J.D. Power reported that 12% of repairs are not completed correctly on the first visit.1 These repeat repairs increase costs, reduce workshop throughput, and erode customer trust.
Financial Crime Mitigation with MongoDB / Part I: Dynamic Customer Profile
Welcome to our series on Building a Financial Crime Mitigation Platform using MongoDB as the unified data platform able to support the demands from modern digital financial operations. In case you missed it, be sure to check out the Series overview.
Unlocking Agentic Power to Modernize Cross-Border Payment Systems
The global payments landscape is a complex web of independent systems enabling international trade. According to Juniper Research, the market reached a value of $187 trillion in 2025 and is projected to hit $224 trillion by 2030. However, operational friction undermines this scale. Failed payments drain the global economy of over $100 billion annually, according to a study by LexisNexis.
Edge AI Made Easy: MongoDB and ObjectBox Data Synchronization
AI is currently undergoing a shift, from massive centralized models to distributed, real-world deployments. While the cloud remains the foundation for large-scale AI training and analytics, AI’s next evolution lies at the edge—where data is created, where decisions require instant action, and where connectivity cannot be guaranteed.
Redefine Airline Loyalty: Innovation for the Modern Traveler
Driven by shifts in traveler behavior across the globe, airline loyalty is undergoing a profound transformation. Travelers now expect better digital services from airlines. Retailers, streaming platforms, mobility apps, and financial services applications have raised expectations for what a personalized, easy, and meaningful experience should feel like. Those expectations are also carried over into the travel industry.
Modernizing Capital Markets Data Foundation with MongoDB
Capital markets run on massive amounts of data that is complex and mission-critical. Every day, exchanges, clearing houses, and broker-dealers handle millions of messages, trades, and risk events across front, middle, and back-office systems. But legacy architectures built on rigid, siloed relational databases are reaching their limits, unable to keep pace with the demands of real-time analytics, horizontal scalability, and AI-driven market intelligence.
Powering the Next Generation of Digital Assets Platforms with MongoDB
For the past several years, banks and financial services institutions have accelerated their adoption of digital asset technologies. This push is driven by market momentum and supportive regulatory shifts. According to an EY survey¹ 59% of institutional investors plan to allocate over 5% of their assets under management (AUM) to cryptocurrencies this year. U.S. respondents and hedge funds lead this trend.
AI-Powered Customer Retention with MongoDB: Real-Time Behavioral Triggers for Retail IT Leaders
Every customer relationship is an investment, and like any investment, its value grows over time—when relationships are properly nurtured. Industry research has shown that retaining existing customers is far more cost-effective than acquiring new ones. For example, a 2025 article by Startup Guru Lab states that “businesses spend 5 to 7 times more to acquire new customers compared to keeping existing ones.”
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.
Financial Crime Mitigation with MongoDB / Series Overview
Where money moves, crime evolves. As banking becomes fully digital—with instant payments, open finance APIs, and mobile-first onboarding—it has opened new roads for cyberattacks, forcing financial institutions to respond in real-time. With just seconds to detect, prevent, and react to suspicious activity—without disrupting the customer experience—banks face an immense challenge that only modern, AI-powered solutions can meet.
How Rierino Scales AI-Native Low-Code with MongoDB
Launched in 2020, the AI-native low-code development platform Rierino was founded to close a critical gap in enterprise technology. Businesses demanded faster innovation, but existing development tools lagged behind the pace of change.
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.
Personalized Retail Media Platforms—Powered by MongoDB
Retailers today have access to a wealth of data that is an invaluable, unique asset that only they possess—such as first-party and customer interaction data. With the rapid evolution of digital commerce and the depreciation of third-party cookies, retail media networks (RMNs) have emerged as a critical channel for monetization and customer engagement. Through the strategic use of shopper data (which spans both digital and physical retail channels), retailers are empowered to provide brands with a unique opportunity to engage in direct advertising with in-market consumers, achieving an unprecedented level of precision and conversion. You can find more information on this in our blog post on Unified Commerce.
Accelerating Stablecoin Innovation in US Banking
The stablecoin market has become a global center of attention, with total market capitalization surging to over $250 billion today (including $155 billion by Tether, and $60 billion by Circle) from $120 billion just 18 months ago. What’s more, according to McKinsey1, the stablecoin market is forecast to reach more than $400 billion by the end of 2025 and $2 trillion by 2028. This rapid growth, however, brings considerable opportunities and challenges.
Building an Agentic AI Fleet Management Solution
Artificial intelligence is revolutionizing the manufacturing and motion industry, with AI-powered solutions now capable of delivering precise, real-time insights that can optimize everything from route planning to predictive maintenance.
Unlock Multi-Agent AI Predictive Maintenance with MongoDB
The manufacturing sector is navigating a growing number of challenges: evolving customer demands, intricate software-mechanical product integrations, just-in-time global supply chains, and a shrinking skilled labor force. Meanwhile, the entire sector is working under intense pressure to improve productivity, manage energy consumption, and keep costs in check. To stay competitive, the industry is undergoing a digital transformation—and data is at the center of that shift.
Boost Connected Car Developments with MongoDB Atlas and AWS
As vehicles continue to evolve from mechanical systems to connected, software-defined platforms, the automotive industry is continuously being reshaped by data. With modern cars generating terabytes of sensor data daily, a key challenge facing the industry is how to extract timely, actionable insight from that data. And a recent survey by McKinsey underscored the degree to which strong connectivity is important to car buyers—close to 40% of US survey respondents indicated that they are willing to switch OEMs over better connectivity options. Though connectivity preferences vary widely by country, autonomous driving and safety features are top of mind for many customers.