Industry Solutions
Industry solutions and innovations driven by MongoDB.
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.
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.
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.
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.
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.
The 5-Step Guide to Mainframe Modernization for Banks
Enriched, convenient, and personalized are the watchwords for any business building a modern, digital customer experience. It’s no different for traditional retail banks, especially as they try to fend off challenger banks and design their own online banking and in-branch experiences to win new business and retain existing customers.