Industry Solutions
Industry solutions and innovations driven by MongoDB.
The Hidden Security Gap in Smart Grid Telemetry (And How to Fix It)
Smart grids are getting smarter. Millions of connected meters, batteries, and electric vehicle chargers now talk back to the systems that run the global grid, and that two-way flow is exactly what makes demand response, distributed energy resources (DER), and real-time balancing possible. It's also what makes this interconnected grid a bigger target than ever before.
August 17, 2026
Industry Solutions
Beyond the Cloud: Building an Offline-First Mobile Wallet with MongoDB Atlas and ObjectBox
The global transition toward a digital-first economy has a critical blind spot: the assumption of continuous, high-speed internet. From subterranean transit systems to rural geographies with spotty coverage, network dependency leaves users stranded and halts commerce.
The Hidden Security Gap in Smart Grid Telemetry (And How to Fix It)
Smart grids are getting smarter. Millions of connected meters, batteries, and electric vehicle chargers now talk back to the systems that run the global grid, and that two-way flow is exactly what makes demand response, distributed energy resources (DER), and real-time balancing possible. It's also what makes this interconnected grid a bigger target than ever before.
Enhancing Energy Management with the Power of MongoDB and Agentic AI
As we race toward 2050, the U.S. electric grid faces a massive capacity surge, with transformer needs projected to increase by 160%–260%, according to the U.S. Department of Energy. For utility operators, grid modernization is no longer a long-term goal; it’s an urgent operational necessity.
Powering Open Banking Consent with MongoDB and Agentic AI
Open Banking and Open Finance (OB&OF) only scale if customers can grant and renew consent in a way that feels safe, transparent, and effortless—yet today consent friction causes more than half of users to abandon otherwise high-value digital experiences. This blog shows how financial institutions can use MongoDB, agentic AI, MongoDB Atlas Vector Search, and the MongoDB MCP Server to turn fragmented consent flows into a single, auditable journey that reduces drop-off and unlocks open finance growth.
Financial Crime Mitigation with MongoDB / Part IV: Agentic AI for Case Management
Welcome back to the last part of our series on Building a Financial Crime Mitigation Platform. We have shown how MongoDB ideally supports the technical capabilities of modern digital financial operations as a unified data platform. In case you missed it, be sure to check out the series overview.
Fraud Hides in Patterns. Vectors See Them.
Fraud has always been a behavioral problem. The signals that distinguish a genuine payment from a fraudulent one are rarely visible in a single field. Instead, they live in the pattern of how an account holder behaves over time: the merchants they frequent, the rhythm of their spending, and the sequence of events before and after each payment. Banks have known this for decades. The question has always been how to put it into practice.
From Intent to What-If: AI Agents for Network Operations and Planning
It’s a busy Wednesday evening in Munich. A new Alpenmarkt retail store has just opened its doors at Marienplatz, with POS terminals coming online and customers beginning to flow through. Deep in the network infrastructure, however, a subtle queue-scheduling collision is quietly building pressure. In less than a minute, this issue will push POS latency beyond the agreed SLA threshold, potentially disrupting transactions and customer experience. At the same moment, thousands of miles away, a network planning engineer at US Mobile sits at their desk and poses a question through a chat interface: “What happens to our NYC and LA cells if we raise prepaid downlink speeds from 7.2 to 20 Mbps this Saturday evening?”
Circular Commerce in Retail: Building the Data Foundation for AI-Driven Recommerce
Retail is entering a new phase where products are no longer sold once and forgotten. Instead, retailers are increasingly seeking to participate in the second, or even third, transaction of a product's lifecycle through trade-in, refurbishment, resale, or recycling. This shift is being driven by the rapid growth of recommerce platforms that are capturing significant portions of the secondary market. To remain competitive, retailers are increasingly investing in their own circular commerce capabilities, extending customer relationships far beyond the initial sale.
Financial Crime Mitigation with MongoDB / Part III: Behavioral Risk Modeling for Event-Driven Fraud Detection
Welcome back to our series on Building a Financial Crime Mitigation Platform, in which we demonstrate how MongoDB ideally supports the technical capabilities of modern digital financial operations as a unified data platform. In case you missed it, be sure to check out the series overview.
Data-Driven, Interoperable Core Banking: A BIAN and MongoDB Perspective
Core banking is the operational heart of a bank: the system of record for accounts, payments, and ledger entries that every other capability depends on. Yet core banking modernization remains one of the industry’s hardest priorities because the problem is not just legacy infrastructure. Every new product, channel, regulatory requirement, or acquisition adds more complexity to the systems that run the bank.
Modern QR Payment Architecture with MongoDB
Digital-first commerce has fundamentally re-engineered the global money movement. While plastic cards remain the primary consumer gateway in many markets, the democratization of real-time transactions across high-growth ecosystems (such as APAC and LATAM) has occurred at the point of sale via QR Payment Codes (QRPC). As a low-cost, zero-hardware interface, QR Payment Codes let merchants accept digital payments using nothing more than a printed placard or a basic screen.
Real-Time Contextual Merchandising for Retailers with MongoDB
Imagine this. The World Cup is taking place. The weather in a customer’s location will be pleasant during the big match. We know the same customer has a strong affinity for outdoor living and is physically close to one of our stores right now. We also have a sharp, real-time understanding of BBQ products in stock in that specific store location. Instead of sending a generic promotion to a broad segment, we deliver a timely, useful mobile app notification that reflects the customer’s moment, their location, their interests, and what the store can actually fulfill.
Three Things Retailers Should Consider When Planning to Support Agentic Commerce
Retail is moving from a world of search and clicks to one of intent and authorization. Google’s Universal Commerce Protocol (UCP) is emerging as an open standard for agentic commerce, creating a common language for discovery, checkout, and post-purchase flows, while Agent Payment Protocol (AP2) introduces the trust and payment layer through verifiable digital mandates. At Google I/O 2026, Google pushed that direction further with Universal Cart, broader UCP expansion, and new AP2-enabled payment experiences inside its own products.
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.
MongoDB and Delbridge: Unlocking Flexible and Custom Data Integration
Modern applications are growing in complexity and scale, and seamless data integration is becoming a vital business priority. It’s critical to provide developers with tools that enable efficient access to data, allowing them to build powerful applications that deliver exceptional user experiences. MongoDB Atlas’s unified database platform empowers teams to build the next generation of applications with the flexibility and performance required for today’s fast-moving, data-driven world.
Empower Retail Associates With Unified Commerce on MongoDB Atlas
With technology advancing and innovations emerging daily, customer expectations are also rising. What once served as a differentiator has now become a baseline, like personalization or omnichannel capabilities. Retail, as one of the fastest-moving industries, is often quick to adopt and deploy the latest innovations. But this agility comes with a challenge: keeping pace with technological advancements at every touchpoint while still delivering a high-quality, customer-centric experience that feels seamless and consistent across all channels.
Revolutionizing Inventory Classification with Generative AI
In today's volatile geopolitical environment, the global automotive industry faces compounding disruptions that require a fundamental rethink of data and operations strategy. After decades of low import taxes, the return of tariffs as a tool of economic negotiations has led the global automotive industry to delay model-year transitions and disrupt traditional production and release cycles. As of June 2025, only 3% of US automotive inventory comprises next-model-year vehicles—less than half the number seen at this time in previous years.
Improving Industrial Safety with Game Theory and MongoDB
In industrial operations, safety is both a business and a human imperative. Heavy-asset industries like aerospace, shipbuilding, and construction constantly invest in better safety systems and policies to keep their staff safe. But a variety of factors—tight physical environments, time pressures, and steep production targets—can lead workers to take unsafe shortcuts to meet quotas. For instance, the European Maritime Safety Agency (EMSA) cited 650 fatalities and over 7,600 injuries linked to marine incidents involving EU-registered ships between 2014 and 2023, and human factors contributed to 80% of these incidents.
Why Relational Databases Are So Expensive to Enterprises
Relational databases were designed with a foundational architecture based on the premise of normalization. This principle—often termed “3rd Normal Form”—dictates that repeating groups of information are systematically cast out into child tables, allowing them to be referenced by other entities. While this design inherently reduces redundancy, it significantly complicates underlying data structures.
Data Modeling Strategies for Connected Vehicle Signal Data in MongoDB
Today’s connected vehicles generate massive amounts of data. According to an article from S&P Global Mobility, a single modern car produces nearly 25GB of data per hour. To put that in perspective: that’s like each car pumping out the equivalent of six full-length Avatar movies in 4K—every single day! Now scale that across millions of vehicles, and it’s easy to see the challenge ahead. Of course, not all of that data needs to be synchronized to the cloud—but even a fraction of it puts significant pressure on the systems tasked with processing, storing, and analyzing it at scale.
Digital Receipts: Mining for Customer & Business Insight with MongoDB
Imagine walking out of your favorite store and moments later receiving a personalized recommendation for a matching item, based not only on what you just bought, but your entire purchase history. This level of tailored experience has long been difficult to achieve in brick-and-mortar retail, but that’s changing thanks to digital receipts. Digital receipts are gaining traction, with Realtimes UK reporting that a quarter of UK retailers now offer them exclusively.
Enhancing AI Observability with MongoDB and Langtrace
Building high-performance AI applications isn’t just about choosing the right models—it’s also about understanding how they behave in real-world scenarios. Langtrace offers the tools necessary to gain deep insights into AI performance, ensuring efficiency, accuracy, and scalability.
Secure Your RAG Workflows with MongoDB Atlas + Enkrypt AI
Generative AI is no longer a futuristic concept—it's already transforming industries from healthcare and finance, to software development and media. According to a 2023 McKinsey report, generative AI could add up to $4.4 trillion annually to the global economy across a wide range of use cases. At the core of this transformation are vector databases, which act as the "memory" that powers retrieval-augmented generation (RAG), semantic search, intelligent chatbots, and more.
Agentic Workflows in Insurance Claim Processing
In 2025, agentic AI is transforming the insurance industry, enabling autonomous systems to perceive, reason, and act independently to achieve complex objectives. Insurers are heavily investing in these technologies to overcome legacy system limitations, deliver hyper-personalized customer experiences, and to capitalize on the $79.86 billion AI insurance market projected by 2032.
How MongoDB and Google Cloud Power the Future of In-Car Assistants
The automotive industry is evolving fast: electrification, the rise of autonomous driving, and advanced safety systems are reshaping vehicles from the inside out. But innovation isn’t just happening to the drivetrain. Drivers (and passengers) now expect more intelligent, intuitive, and personalized experiences whenever they get into a car.
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.
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.
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.
Driving Retail Loyalty with MongoDB and Cognigy
Retail is one of the fastest moving industries, often the very first to leverage cutting-edge AI to create next-gen experiences for their customers. One of the latest areas we’re seeing retailers invest in is agentic AI: they are creating conversational chatbot “agents” that are pulling real-time information from their systems, using Natural Language processing to create conversational responses to customer queries, and then taking action- completing tasks and solving problems.
Why MongoDB is the Perfect Fit for a Unified Namespace
Smart manufacturing is transforming the industrial world by combining IoT, AI, and cloud technologies to create connected, data-driven production environments. Manufacturers embracing this shift are seeing real, measurable benefits: Deloitte reports that smart factory initiatives can boost manufacturing productivity by up to 12% and improve overall equipment effectiveness by up to 20%.
Embracing Open Finance Innovation with MongoDB
The term "open finance" is increasingly a topic of discussion among banks, fintechs, and other financial services providers—and for good reason. Open finance, as the next stage of open banking, expands the scope of data sharing beyond traditional banking to include investments, insurance, pension funds, and more. To deliver these enhanced capabilities, financial service providers need a versatile and flexible data store that can seamlessly manage a wide array of financial data.
Modernizing Telecom Legacy Applications with MongoDB
The telecommunications industry is currently undergoing a profound transformation, fueled by innovations in 5G networks, the growth of Internet of Things applications, and the rapid rise of AI. To capitalize on these technologies, companies must effectively handle increasing volumes of unstructured data, which now represents up to 90% of all information, while also developing modern applications that are flexible, high-performance, and scalable. However, the telecommunications industry's traditional reliance on relational databases such as PostgreSQL presents a challenge to modernization. Their rigid structures limit adaptability and can lead to decreased performance as table complexity grows.
Hasura: Powerful Access Control on MongoDB Data
Across industries—and especially in highly regulated sectors like healthcare, financial services, and government—MongoDB has been a preferred modern database solution for organizations handling large volumes of sensitive data that require strict compliance adherence. In such enterprises, secure access to data via APIs is critical, particularly when information is distributed across multiple MongoDB databases and external data stores. Hasura extends and enhances MongoDB's access control capabilities by providing granular permissions at the column and field level across multiple databases through its unified interface. At the same time, designing a secure API system from scratch to meet this need takes significant development resources and becomes a burden to maintain and update. Hasura solves this problem for enterprises by elegantly serving as a federated data layer, with robust access control policies built-in.
Multi-Agent Collaboration for Manufacturing Operations Optimization
While there are some naysayers across the media landscape who doubt the potential impact of AI innovations, for those of us immersed in implementing AI on a daily basis, there’s wide agreement that its potential is huge and world-altering. It’s now generally accepted that Large Language Models (LLMs) will eventually be able to perform tasks as well—if not better—than a human. And the size of the potential AI market is truly staggering. Bain’s AI analysis estimates that the total addressable market (TAM) for AI and gen AI-related hardware and software will grow between 40% and 55% annually, reaching between $780 billion and $990 billion by 2027. This growth is especially relevant to industries like manufacturing, where generative AI can be applied across the value chain. From inventory categorization to product risk assessments, knowledge management, and predictive maintenance strategy generation, AI's potential to optimize manufacturing operations cannot be overstated.
Smarter Care: MongoDB & Microsoft
Healthcare is on the cusp of a revolution powered by data and AI. Microsoft, with innovations like Azure OpenAI, Microsoft Fabric, and Power BI, has become a leading force in this transformation. MongoDB Atlas complements these advancements with a flexible and scalable platform for unifying operational, metadata, and AI data, enabling seamless integration into healthcare workflows.
Automate Network Management Using Gen AI Ops with MongoDB
Imagine that it’s a typical Tuesday afternoon and that you’re the operations manager for a major North American telecommunications company. Suddenly, your Network Operations Center (NOC) receives an alert that web traffic in Toronto has surged by hundreds of percentage points over the last hour—far above its usual baseline. At nearly the same moment, a major Toronto-based client complains that their video streams have been buffering nonstop.
Securing Digital Transformation with MongoDB and RegData
Data security and privacy have long been paramount to the financial industry, but they are especially critical for institutions undergoing digital transformations or those implementing new technology. For example, the integration of artificial intelligence (AI) and machine learning (ML) into organizations’ infrastructure and offerings introduces security and privacy complexities, making it all the more essential for financial organizations to safeguard sensitive information while complying with regulations.
Empower Financial Services Developers with the Document Model
In financial services, having a proper data modeling strategy is crucial. The volume of data that banks collect and create is continuously growing, partly due to the expansion of digital banking and payments. Financial institutions rely heavily on data to power applications, analyze risks, and make decisions. The way data is modeled can significantly impact the performance, scalability, and accuracy of these systems, as well as the speed of development for new financial products or services.
AI-Powered Retail With Together AI and MongoDB
Generative AI (gen AI) is changing retail in fascinating ways. It’s providing new avenues for IT leaders at retailers to enhance customer experiences, streamline operations, and grow revenue in a fast-paced environment. Recently, we’ve been working closely with a fascinating organization in this space—Together AI. In this blog, we’ll explore how Together AI and MongoDB Atlas tremendously accelerated the adoption of gen AI by combining the capabilities of both platforms to bring high-impact retail use cases to life.
Leveraging an Operational Data Layer for Telco Success
The emergence of 5G network communication, IoT devices, edge computing, and AI have accelerated structural changes within the telecommunications industry, creating new needs and opportunities. To remain competitive, telcos must embrace this technology-driven transformation by defining a robust data strategy. Such a strategy should enhance operational efficiency and provide unique value to customers, and should ultimately enable telcos to set themselves apart from their competitors.
Retail Insights With MongoDB: Shoptalk Fall
The retail industry has continued to evolve into an omnichannel marketplace since the 2020 pandemic sparked a surge of online shipping. Now, nearly five years later, the line between in-person shopping and e-commerce has grown thinner, thanks to technological advancements and shifting consumer expectations.
Improving Omnichannel Ordering: BOPIS & Delivery with MongoDB
Today's customers expect a seamless shopping experience across both online and physical channels. The ability to Buy Online, Pick Up in Store (BOPIS)—or to receive deliveries at home—has become essential for meeting modern demands and staying competitive.
Using Agentic RAG to Transform Retail With MongoDB
In the competitive world of retail and ecommerce, it’s more important than ever for brands to connect with customers in meaningful, personalized ways. Shoppers today expect relevant recommendations, instant support, and unique experiences that feel tailored just for them.
MongoDB Atlas Integration with Ably Unlocks Real-time Capabilities
Enterprises across sectors increasingly realize that data, like time, doesn’t wait. Indeed, harnessing and synchronizing information in real time is the new currency of business agility. Enter the alliance between MongoDB and Ably—a partnership that has led to Ably's new database connector for MongoDB Atlas.
AI-Powered Call Centers: A New Era of Customer Service
Customer satisfaction is critical for insurance companies. Studies have shown that companies with superior customer experiences consistently outperform their peers. In fact, McKinsey found that life and property/casualty insurers with superior customer experiences saw a significant 20% and 65% increase in Total Shareholder Return, respectively, over five years.
Better Digital Banking Experiences with AI and MongoDB
Interactive banking represents a new era in financial services where customers engage with digital platforms that anticipate, understand, and meet their needs in real-time. This approach encompasses AI-driven technologies such as chatbots, virtual assistants, and predictive analytics that allow banks to enhance digital self-service while delivering personalized, context-aware interactions.
MongoDB: Powering Digital Natives
Today's rapidly evolving digital landscape is dominated by digital native companies, driving innovation. These are companies born in the digital age and who operate through digital channels with a business model enabled by technology and data. They are not only adept at using technology but are also reshaping the way software is developed and deployed.
Empower Innovation in Insurance with MongoDB and Informatica
For insurance companies, determining the right technology investments can be difficult, especially in today's climate where technology options are abundant but their future is uncertain. As is the case with many large insurers, there is a need to consolidate complex and overlapping technology portfolios. At the same time, insurers want to make strategic, future-proof investments to maximize their IT expenditures.
From Chaos to Control: Real-Time Data Analytics for Airlines
Delays are a significant challenge for the airline industry. They disrupt travel plans, erode customer loyalty, and inflict significant financial losses. In an industry built on precision and punctuality, even minor setbacks can have cascading effects. Whether due to adverse weather conditions or unforeseen technical issues, these delays ripple through flight schedules, affecting both passengers and operations managers. While neither group is typically at fault, the ability to quickly reallocate resources and return to normal operations is crucial.
MongoDB Atlas + PowerSync: The Future of Offline-First Enterprise Apps
Picture this: your field team is miles from the nearest cell tower, but their apps still work flawlessly—tracking assets, updating data, and syncing the moment they're back online. Since even a second of downtime can cost millions, the future of enterprise apps is one where "offline" doesn't mean "out of commission."
Saving Energy, Smarter: MongoDB and Cedalo for Smart Meter Systems
The global energy landscape is undergoing a significant transformation, with energy consumption rising 2.2% in 2023, surpassing the 2010-2019 average of 1.5% per year. This increase is largely due to global developments in BRICS member countries—Brazil, Russia, India, China, and South Africa.
Mobile and Edge Solutions with MongoDB and Ditto
Mobile and edge solutions offer impressive opportunities for profit and growth for a variety of businesses around the world. Companies have consistently found ways to use mobile applications to grow revenue, cut costs, and stay ahead of the competition.
The Dual Journey: Healthcare Interoperability and Modernization
Interoperability in healthcare isn’t just a buzzword; it’s a fundamental necessity. It refers to the ability of IT systems to enable the timely and secure access, integration, and use of electronic health data. However, integrating data across different applications is a pressing challenge, with 48% of US hospitals reporting a one-sided sharing relationship in which they share patient data with other providers who do not, in turn, share patient data with the hospital.
Revolutionizing Retail with RFID and MongoDB Atlas
In today's fast-paced retail environment, keeping pace with both customer expectations and competition, maintaining accurate, real-time inventory is more critical than ever. Radio Frequency Identification (RFID) technology has emerged as a transformative solution, enabling retailers to track inventory with unprecedented precision and efficiency.
Better Business Loans with MongoDB and Generative AI
Business loans are a cornerstone of banking operations, providing significant benefits to both financial institutions and broader economies. For example, in 2023 the value of commercial and industrial loans in the United States reached nearly $2.8 trillion. However, these loans can present unique challenges and risks that banks must navigate. Besides credit risk, where the borrower may default, banks also face business risk, in which economic downturns or sector-specific declines can impact borrowers' ability to repay loans.
Meeting the UK’s Telecommunications Security Act with MongoDB
Emerging technologies like AI, IoT, and 5G have transformed the value that telecommunications companies provide the world. However, these new technologies also present new security challenges. As telcos continue to amass large amounts of sensitive data, they become an increasingly attractive target for cybercriminals — making both companies and countries vulnerable to cyberattacks. Fortunately, developers can protect user data which comes with strong security requirements on a modern database. By offering features to meet stringent requirements with robust operational and security controls, telcos can protect their customers’ private information.
Enhancing Retail with Retrieval-Augmented Generation (RAG)
In the rapidly evolving retail landscape, tech innovations are reshaping how businesses operate and interact with customers. Generative AI could add up to $275 billion of profit to the apparel, fashion, and luxury sectors’ by 2028, according to McKinsey analysis. One of the most promising developments in this realm is retrieval-augmented generation (RAG), a powerful application of artificial intelligence (AI) that combines the strength of data retrieval with generative capabilities to supercharge retail enterprises.
The Converged AI and Application Datastore for Insurance
In the inherently information-driven insurance industry, companies ingest, analyze, and process massive amounts of data, requiring extensive decision-making. To manage this, they rely on a myriad of technologies and IT support staff to keep operations running smoothly but often lack effectiveness due to their outdated nature.
Anti-Money Laundering and Fraud Prevention With MongoDB Vector Search and OpenAI
Fraud and anti-money laundering (AML) are major concerns for both businesses and consumers, affecting sectors like financial services and e-commerce. Traditional methods of tackling these issues, including static, rule-based systems and predictive artificial intelligence (AI) methods, work but have limitations, such as lack of context and feature engineering overheads to keeping the models relevant, which can be time-consuming and costly.
Building Gen AI-Powered Predictive Maintenance with MongoDB
In today’s fast-evolving industrial landscape, digital transformation has become a necessity. From manufacturing plants to connected vehicles, the push towards predictive maintenance excellence is driving organizations to embrace smarter, more efficient ways of managing operations. One of the most compelling advancements in this domain is predictive maintenance powered by generative AI, a cutting-edge approach that will revolutionize how industries maintain and optimize their equipment.
Unlock PDF Search in Insurance with MongoDB & Superduper.io
As industries go, the insurance industry is particularly document-driven. Insurance professionals, including claim adjusters and underwriters, spend considerable time handling documentation with a significant portion of their workday consumed by paperwork and administrative tasks. This makes solutions that speed up the process of reviewing documents all the more important.
Unified Namespace Implementation with MongoDB and MaestroHub
In the complex world of modern manufacturing, a crucial challenge has long persisted: how to seamlessly connect the physical realm of industrial control systems with the digital landscape of enterprise operations. The International Society of Automation's ISA-95 standard, often visualized as the automation pyramid, has emerged as a guiding light. As shown below, this five-level hierarchical model empowers manufacturers to bridge the gap between these worlds, unlocking a path toward smarter, more integrated operations.
Transforming Predictive Maintenance with AI: Real-Time Audio-Based Diagnostics with Atlas Vector Search
Wind turbines are a critical component in the shift away from fossil fuels toward more sustainable, green sources of energy. According to the International Energy Agency (IEA), the global capacity of wind energy has been growing rapidly, reaching over 743 gigawatts by 2023. Wind energy, in particular, has one of the greatest potentials to increase countries' renewable capacity growth. Solar PV and wind additions are forecast to more than double by 2028 compared with 2022, continuously breaking records over the forecast period.
Payments Modernization and the Role of the Operational Data Layer
To stay relevant and competitive, payment solution providers must enhance their payment processes to adapt to changing customer expectations, regulatory demands, and advancing technologies. The imperative for modernization is clear: payment systems must become faster, more secure, and seamlessly integrated across platforms.
Search PDFs at Scale with MongoDB and Nomic
Data is only valuable if it’s accessible. For example, storing photos, audio files, or PDFs without the ability to extract information from them is like keeping junk in your basement, thinking you might need it someday. The problem is finding what you need to dig through your junk when the day comes.
Transforming Industries with MongoDB and AI: Healthcare
This is the sixth in a six-part series focusing on critical AI use cases across several industries. The series covers the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries.
Retrieval Augmented Generation for Claim Processing: Combining MongoDB Atlas Vector Search and Large Language Models
Following up on our previous blog, AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases, we’ll pick up the conversation right where we left it. We discussed extensively how Atlas Vector Search can benefit the claim process in insurance and briefly covered Retrieval Augmented Generation (RAG) and Large Language Models (LLMs).
Transforming Industries with MongoDB and AI: Insurance
This is the fifth in a six-part series focusing on critical AI use cases across several industries. The series covers the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries.
Enabling Commerce Innovation with the Power of MongoDB and Google Cloud
Across all industries, business leaders are grappling with economic uncertainty, cost concerns, disruption to supply chains, and pressure to embrace new technologies like generative AI. In this dynamic landscape, having a performant and future-proofed technology foundation is critical to your business’s success.
Transforming Industries with MongoDB and AI: Financial Services
This is the fourth in a six-part series focusing on critical AI use cases across several industries. The series covers the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries.
Transforming Industries with MongoDB and AI: Retail
This is the third in a six-part series focusing on critical AI use cases across several industries. The series covers the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries.
Transforming Industries with MongoDB and AI: Telecommunications and Media
This is the second in a six-part series focusing on critical AI use cases across the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries. Read part one here.
Transforming Industries with MongoDB and AI: Manufacturing and Motion
This is the first in a six-part series focusing on critical AI use cases across several industries. The series covers the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries.
From Relational Databases to AI: An Insurance Data Modernization Journey
Imagine you’re a data architect, a developer, or a data engineer at an insurance company. Management has asked you and your team to build a new AI claim adjustment system, a customer-facing LLM-powered chatbot, and an application to streamline the underwriting process.
How MongoDB Enables Digital Twins in the Industrial Metaverse
The integration of MongoDB into the metaverse marks a pivotal moment for the manufacturing industry, unlocking innovative use cases across design and prototyping, training and simulation, and maintenance and repair. MongoDB's powerful capabilities — combined with Augmented Reality (AR) or Virtual Reality (VR) technologies — are reshaping how manufacturers approach these critical aspects of their operations, while also enabling the realization of innovative product features.
RegData & MongoDB: Streamline Data Control and Compliance
While navigating the requirements of keeping data secure in highly regulated markets, organizations can find themselves entangled in a web of costly and complex IT systems. Whether it's the GDPR safeguarding European personal data or the Monetary Authority of Singapore's guidelines on outsourcing and cloud computing, the greater the number of regulations organizations are subjected to, particularly across multiple geographical locations, the more intricate their IT infrastructure becomes, and organizations today face the challenge of adapting immediately or facing the consequences.
Powering Vector Search Maturity in Retail with Pureinsights
In a competitive retail market, with customer demands higher than ever, retailers are on a constant journey toward search maturity. With the recent announcement of MongoDB’s Vector Search offering, retailers are implementing smarter search solutions to provide customers and staff with delightful experiences. Here we’ll explore how partners like Pureinsights are helping retailers to understand what true search maturity entails, and how to start their vector search journey on MongoDB Atlas.
Every Operational Data Layer (ODL) Can Benefit From Search
In today's digital landscape, organizations frequently encounter the daunting challenge of managing complex data architectures. Multiple systems, diverse technologies, and a variety of programming languages become entwined, making smooth operations a significant struggle. A frequent example of this issue is seen in some major banks still relying on a banking system built in the 1970s, continuing to run on a mainframe with minimal updates. The consequence is a complex architecture as seen in Figure 1, where data is scattered across various systems, creating inefficiencies and hindering seamless operations.
AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases
We’re just under a year since OpenAI released ChatGPT, unleashing a wave of hype, investment, and media frenzy around the potential of generative AI to transform how we do business and interact with the world. But while the majority of the investment dollars and media attention zeroed in on the disruptive capabilities of large language models (LLMs), there’s a crucial component underpinning this breakthrough technology that hasn’t received the attention it deserves; the humble vector database.
How to Enhance Inventory Management with Real-Time Data Strategies
In the competitive retail landscape, having the right stock in the right place at the right time is crucial. However, the retail industry faces significant challenges in achieving this goal. In 2022, unsold stock in the US surged by a staggering $78 billion, reaching approximately $740 billion—a shocking 12 percent increase.
Real-Time Inventory Tracking with Computer Vision & MongoDB Atlas
In today’s rapidly evolving manufacturing landscape, digital twins of factory processes have emerged as a game-changing technology. But why are they so important? Digital twins serve as virtual replicas of physical manufacturing processes, allowing organizations to simulate and analyze their operations in a virtual environment. By incorporating artificial intelligence and machine learning, organizations can interpret and classify objects, leading to cost reductions, faster throughput speeds, and improved quality levels. Real-time data, especially inventory information, plays a crucial role in these virtual factories, providing up-to-the-minute insights for accurate simulations and dynamic adjustments.
Amplifying Retail Operations with Generative AI and Vector Search: The Unexplored Potential
In the hyper-competitive world of retail, industry leaders are continually looking for new ways to revolutionize the customer experience and optimize operations. That's where generative AI and vector search come into play. Both offer transformative potential in myriad retail use cases, from personalized marketing campaigns to efficient inventory management, making them indispensable for those aiming to stay at the industry's cutting edge.
Real-Time Energy Monitoring for Smart Buildings with MongoDB and HiveMQ
The Internet of Things (IoT) has ushered in a new era of energy efficiency, enabling the deployment of energy-efficient sensors for energy conservation and resource utilization. With over 1.5 billion connected IoT devices already installed in commercial smart buildings in 2022 and a projected surge to 3.25 billion devices by 2028, the volume of data generated is staggering. To put it in perspective, an average home in 2020 would generate approximately 4.7 terabytes of data annually. However, managing and harnessing this immense amount of real-time streaming data poses a significant challenge for developers. In smart buildings, where a multitude of IoT sensors continuously gather event streaming data, developers often grapple with integrating disparate technologies and investing significant time into data streaming integration.
How Telcos Drive Mission-Critical Innovation and Cost Savings Through Automation
Modern telecommunications customers don’t only expect flawless network performance, they demand it. For global enterprises that hold the key to human connection, the industry standard is nothing less than a fully integrated, customer-centric approach to service delivery.
MongoDB and BigID Delivering Scalable Data Privacy Compliance for Financial Services
Ensuring data privacy compliance has become a critical priority for banks and financial services. Safeguarding customer data is not only crucial for maintaining trust and reputation but also a legal and ethical obligation. In this blog, we will dive into why and how the financial services industry can adopt an approach to data privacy compliance effectively using BigID and MongoDB.
Fueling Pricing Strategies with MongoDB and Databricks
Deploying real-time analytics solutions with the right tech stack can have transformative benefits. Retailers want to grow their brand name or improve customer experience with value based pricing, whilst remaining competitive and cost effective.
Modernize Your Factory Operations: Build a Virtual Factory with MongoDB Atlas in 5 Simple Steps
Virtual factories are revolutionizing the manufacturing landscape. Coined as the "Revolution in factory planning" by BMW Group at NVIDIA, this cutting-edge approach is transforming the way companies like BMW and Hyundai operate, thanks to groundbreaking partnerships with technology companies such as NVIDIA and Unity. At the heart of this revolution lies the concept of virtual factories, computer-based replicas of real-world manufacturing facilities. These virtual factories accurately mimic the characteristics and intricacies of physical factories, making them a powerful tool for manufacturers to optimize their operations. By leveraging AI, they unlock a whole new world of possibilities, revolutionizing the manufacturing landscape, paving the way for improved productivity, cost savings, and innovation.
Accelerating to T+1 - Have You Got the Speed and Agility Required to Meet the Deadline?
On May 28, 2024, the Securities and Exchange Commission (SEC) will implement a move to a T+1 settlement for standard securities trades, shortening the settlement period from 2 business days after the trade date to one business day. The change aims to address market volatility and reduce credit and settlement risk.
Connected Devices - How GE HealthCare Uses MongoDB to Manage IoT Device Lifecycle
GE HealthCare, a global leader in medical technology, has turned to MongoDB to manage the lifecycle of its IoT devices, from deployment (Beginning of Life or BoL) to retirement (End of Life or EoL).
How MongoDB, A*STAR, and Industry Partners are Collaborating on Singapore’s Supply Chain 4.0 Initiative
Greater uncertainty in global trade flows and black swan events, such as COVID-19, have challenged the linear supply chain business model. Digital technologies are being recognized as a key enabler for resilient and responsive supply chains. Supply Chain 4.0 is the reorganization of supply chain – plan, source, make, deliver, return and enable from a linear business model to an integrated one using concepts of Industry 4.0 (I4.0). In this article, we’ll explore how MongoDB, together with our industry partners in Singapore, help businesses integrate technological innovations into their operations to deal with diverse challenges posed by growing supply chains.
Three Ways Retailers Use Search Beyond the Ecommerce Store
When consumers think of retail search, the first thing that comes to mind is typically the search bar of an ecommerce website. This is for a good reason: a Salesforce commerce study shows that 87% of shoppers begin their shopping journey in the search bar, and Forrester has found that as many as 68% of shoppers would not return to a site that provided a poor experience.
Build a ML-Powered Underwriting Engine in 20 Minutes with MongoDB and Databricks
The insurance industry is undergoing a significant shift from traditional to near-real-time data-driven models, driven by both strong consumer demand, and the urgent need for companies to process large amounts of data efficiently. Data from sources such as connected vehicles and wearables are utilized to calculate precise and personalized premium prices, while also creating new opportunities for innovative products and services.
Real-Time ESG Data Management
ESG (Environmental, Social, and Governance) data collection and reporting has become a corporate priority, with over 96% of S&P 500 companies publishing sustainability reports in 2021, according to research from the Governance and Accountability Institute.
Green Lending, Green Data - The Impact on Banks Explained
In its Work Program 2025, the European Banking Authority (EBA) emphasizes the integration of Environmental, Social, and Governance (ESG) considerations into the EU banking framework. A key task is the implementation of the final guidelines on the management of ESG risks, published on 9 January 2025. These guidelines require institutions to identify, measure, manage, and monitor ESG risks, ensuring short, medium, and long-term resilience.
Modernizing Core Banking: A Shift Toward Composable Systems
Modernizing core banking systems with MongoDB can bring many benefits such as faster innovation, flexible deployment, and instant scalability. According to McKinsey & Company, it is critical for banks to modernize their core banking platforms with a “flexible back end” in order to stay competitive and adapt to new business models. With the emergence of better data infrastructure based on JSON and the ongoing evolution of software design, the next generation of composable core banking processes can be built on MongoDB's modern database, offering greater flexibility and adaptability than traditional systems.
Forrester Study: How IT Decision Makers Are Using Next-Generation Data Platforms
Data is critical to every financial institution; it is recognized as a core asset to drive customer growth and innovation. As the need to leverage data efficiently increases, however, the legacy technology that still underpins many organizations is not built to support today’s requirements. Not only is this infrastructure costly and complex, it doesn’t support the diversity of workloads and functions that modern applications require.
Telco Scaling Strategies: Modernizing Business Support Systems for Flexible Revenue Growth
Consumers and businesses alike are driving huge demand for innovative telecommunications technology that tests the limits of monolithic, traditional business support system (BSS) architecture.
Achieving Industrial Connectivity at Scale with Wimera and MongoDB
Industry 4.0 (I4.0) represents the beginning of the Fourth Industrial Revolution. It includes the current trend of automation technologies in the manufacturing industry as well as disruptive technologies and concepts, such as cyber-physical systems (CPS), Industrial Internet of Things (IIoT), cloud computing, and immersive visualization.
4 Ways Telcos Deliver Mission-Critical Network Performance and Reliability
Tech leaders like Google, Apple, and Netflix set a new standard for customer service. Today’s customers expect intuitive, always-on, seamless service that challenges telecommunications companies’ network performance and reliability. This article examines several ways that companies can meet these challenges through an automated, data-driven approach.
4 Critical Features for a Modern Payments System
The business systems of many traditional banks rely on solutions that are decades old. These systems, which are built on outdated, inflexible relational databases, prevent traditional banks from competing with industry disruptors and those already adopting more modern approaches.
Connected Data: How IoT Will Save Healthcare and Why MongoDB Matters
Over the next decade, healthcare systems around the world will face a two-fold challenge: Delivering higher quality care while managing rising costs, and doing so for increasingly larger populations of patients.
Mobile Edge Computing, Part 2: Computing in the Real World
It would be easy to conceptualize mobile edge computing (MEC) as a telecommunications-specific technology; but, in fact, edge computing has far-reaching implications for real-world use cases across many different industries. Any organization that requires a solution to common data usage challenges, such as low-latency data processing, cloud-to-network traffic management, Internet of Things (IoT) application development, data sovereignty, and more, can benefit from edge-based architectures.
How Telcos Are Transforming to Digital Services Providers
The telecommunications industry is in the midst of a digital revolution, shifting from a traditional service delivery model to one that is increasingly customer-centric and that extends beyond the provision of traditional connectivity services to include diverse digital services.
Open Banking: How to Future-Proof Your Banking Strategy
Open banking is on the minds of many in the fintech industry, leading to basic questions such as: What does it mean for the future? What should we do today to better serve customers who expect native open banking services? How can we align with open banking standards while they’re still evolving?
MACH Aligned for Retail: Microservices
MACH is an approach to architecting modern applications through open tech ecosystems. It is an acronym representing Microservices, API-first, Cloud-native SaaS, and Headless. With the accelerating digitalization of retail experiences requiring new technology stacks that provide agility, flexibility, and performance at scale, MACH is especially relevant for retail and ecommerce, a far cry from current legacy, monolithic architectures.
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.
Content Discovery: How to Win the Battle for Attention
Think of the last app you used today. For me, it was searching for the latest episode of Sesame Street on HBOMax for my toddler. For someone else, it was finding a YouTube video on how to bake a cake. Or listening to a song recommended by Spotify. All of these instances, steps we barely put any thought into, are examples of content discovery , the bidirectional process by which users and applications interact, ensuring users’ known and unknown content consumption needs are fulfilled. As content is being generated at a nearly unfathomable and exponential pace ( think 500 hours of videos uploaded to YouTube every single minute ), catching and holding consumers’ attention with content is only going to become more difficult. Delivering great content discovery experiences that meet evolving customer expectations will be the only way to keep up. Content discovery happens in two ways, resembling push and pull forces: Push (recommendation engines): Content is suggested to the user. This can look like personalized landing pages or content recommendations. Pull (search): The customer searches for content, typically via a search bar. The user leads the action, and a new opportunity for suggesting relevant content is created. Consider how you consume content. Maybe you’re searching for a show you want to watch, or once that show is completed, the app you’re using recommends another similar show you might like. If media providers can master both of these processes – accurate search and intuitive recommendations – you can expect to fuel user engagement and decrease churn. Simple enough, right? Unfortunately, developing and deploying cutting-edge search and recommendation engines is easier said than done. A few major challenges stand in the way, like integrating data from multiple sources with excruciating extract, transform, load (ETL) pipelines, adding and maintaining a separate search engine solution, reduction in both time-to-value and developer productivity, and more. Having a unified data platform that can handle analytics at scale and search natively is a massive advantage for effective content discovery. Let’s look at how an advanced data platform like MongoDB Atlas makes the push and pull of content discovery possible. The push: Real-time, relevant recommendations Hitting users with the content they want when they want it (whether they know it's the content they want or not) is the aim of any recommendation engine. It’s particularly important in the media content consumption game, since there are so many competing platforms vying for user attention. As the volume and variety of user data increases by the second — generated from what they’re watching, what they stopped watching, what devices they’re using to interact with content — recommendations engines need to move beyond simple if-then-else statement based on historical data to advanced machine learning model that learns with data captured in real time, such as a causal inference models that predict what people might want to watch based on what other users with similar profiles and viewing habits are currently watching. MongoDB integrates natively with machine learning and artificial intelligence engines, using change streams to update the ML models to provide recommendations. The consumer profile is updated and saved in MongoDB, acting as the persistence layer and effectively becoming the single-view consumer data platform, a critical component in the pursuit of real-time analytics informing recommendations. Developers now have a single view of data, and machine learning models use that unsoiled data to make lightning-fast, accurate recommendations to keep users engaged with content. MongoDB acts as the catalyst for real-time recommendations informed by customer behavior triggers. The pull: Solving the search bar Virtually every application today has a search function — but it is also challenging to get right. Unlike database queries, where the user knows exactly what they are querying for, search has to give fast and relevant results to open ended, natural language inputs, tolerating typos and partial search terms, and essentially inferring users intent. Ultimately, consumers expect a Google-level search experience, and if they don’t get it, they’ll move to the next content platform. Building your own search engine, that will meet user expectations even as those expectations evolve, is costly in terms of time and resources spent developing and maintaining the engine. Many more database indexes need to be added to support search queries, and the search workload will start to contend for system resources with the core data persistence and processing demands of the application. To avoid resource contention between these two workloads, the database needs to be carefully sized and closely monitored and scaled, driving up operational overhead and cost. Also developing a database search solution won’t offer you any advantage over the competition, since there are dedicated search engines in the market that can do that heavy lifting for you. This reality has led companies to bolt-on a specialized search engine to their database – not that this is a simple solution either. Bolting on a search cluster to your database requires adding a new query language to integrate your application with the search engine, which increases the operational and architectural complexity of your current environment. This results in an elongated time for market for what could be a suboptimal search engine. Atlas Search solves the architecture and operational challenges of adding a separate search engine, since it’s fully integrated with the MongoDB Atlas Data Platform. Powered by the market-leading search engine Apache Lucene, it provides advanced search capabilities, while reducing architectural sprawl. Customers have reported improved development velocities of 30% to 50% after adopting Atlas Search. Atlas manages the required search infrastructure and automatically keeps the search indexes in sync with data mastered in the MongoDB database. Developers interact with search using the same universal interface that they are accustomed to using when interacting with other data in the platform, which means no new solutions to learn or decrease in developer productivity. Maintaining two separate systems adds complexity and lowers productivity, compared to the unified platform offered by MongoDB Atlas. With MongoDB Atlas, you can deliver the right recommendations at the most opportune time, and provide a best-in-class search experience to keep users engaged. No secondary solutions. No months of wasted development. Just a single, simplified process for game-changing content discovery. Take a deeper look into content discovery powered by MongoDB in our recent guide, Simplifying Content Discovery .
How the Healthcare Industry Benefits From Platform Thinking
As the healthcare industry uncovers new ways to utilize health data to drive value-based care, digital platforms will bring applications together into a seamless and single-user experience for patients and providers.
Manufacturing at Scale: MongoDB & IIoT
In recent years, we’ve seen a massive shift in digital transformation. As people, we’re all “connected”, by our smart devices, smart homes, smart cars, smart cities, and so on. We interact with smart devices because of the convenience it offers us- automating daily tasks and giving insight into daily movements, from how much pressure is in each car tire to whether we left our stove on before leaving the house.
What is MACH Architecture for ecommerce?
In the past, retailers faced the looming battle of brick and mortar vs. digital buying experiences. While most in the retail industry accepted the inevitability of needing some kind of digital experience, COVID-19 forced retailers to refocus efforts to digital-first, or at the very least, hybrid digital and in-person buying options.
For Banks, KYC Should Mean More than Just "Knowing Your Client"
Banks in the loan or mortgage business believe they know their clients well, yet they struggle to offer services that capitalize on customer data or tailor the loan origination experience to the individual based on the existing volume of information they have.
Drowning in Data: Why It's Time to End the Healthcare Data Lake
From digital check-ins, to connected devices and telehealth programs, patients expect the benefits of a more digitized healthcare experience. At the same time, they’re also demanding a more personalized approach from healthcare providers. This duality - the need to provide a more convenient experience with one that’s more tailored to the patient - is fueling a wave of technology modernization efforts and the replacement of monolithic legacy IT systems.
The Four Data Platform Must Haves for Personalized Retail
Even before the events of 2020, consumers were increasingly embracing a hybrid shopping experience that mixed the digital and the physical. Rather than making a binary choice between “bricks” or “clicks”, the customer journey can start online, before moving to a physical store for an in person demo, and then back online for the final purchase.
MongoDB and BigID Deliver Scalable Data Intelligence for Enterprise Data
Data never sleeps. Every time someone clicks on a site or sign-up for a newsletter, bytes are created or tracked, in the staggering amount of more than 2.5 quintillion every day. All that data is a key business driver and the foundation for decision-making, but most enterprises aren’t even aware of everything they’re collecting, or where it’s stored. As global protection and privacy regulations evolve and the sheer amount of data grows, they’re struggling to keep up.