ArtificialIntelligence

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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. But in order to realize the transformative economic potential of AI, applications powered by LLMs need to evolve beyond chatbots that leverage retrieval-augmented generation (RAG). Truly transformative AI-powered applications need to be objective-driven, not just responding to user queries but also taking action on behalf of the user. This is crucial in complex manufacturing processes. In other words, they need to act like agents. Agentic systems, or compound AI systems, are currently emerging as the next frontier of generative AI applications. These systems consist of a single or multiple AI agents that collaborate with each other and use tools to provide value. An AI agent is a computational entity containing short- and long-term memory, which enables it to provide context to an LLM. It also has access to tools, such as web search and function calling, that enable it to act upon the response from an LLM or provide additional information to the LLM. Figure 1. Basic components of an agentic system. An agentic system can have more than one AI agent. In most cases, AI agents may be required to interact with other agents within the same system or external systems., They’re expected to engage with humans for feedback or review of outputs from execution steps. AI agents can also comprehend the context of outputs from other agents and humans, and change their course of action and next steps. For example, agents can monitor and optimize various facets of manufacturing operations simultaneously, such as supply chain logistics and production line efficiency. There are certain benefits of having a multi-agent collaboration system instead of having one single agent. You can have each agent customized to do one thing and do it well. For example, one agent can create meeting minutes while another agent writes follow-up emails. It can also be implemented on predictive maintenance, with one agent analyzing machine data to find mechanical issues before they occur while another optimizes resource allocation, ensuring materials and labor are utilized efficiently. You can also provision dedicated resources and tools for different agents. For example, one agent uses a model to analyze and transcribe videos while the other uses models for natural language processing (NLP) and answering questions about the video. Figure 2. Multi-agent collaboration system. MongoDB can act as the memory provider for an agentic system. Conversation history alongside vector embeddings can be stored in MongoDB leveraging the flexible document model. Atlas Vector Search can be used to run semantic search on stored vector embeddings, and our sharding capabilities allow for horizontal scaling without compromising on performance. Our clients across industries have been leveraging MongoDB Atlas for their generative AI use cases , including agentic AI use cases such as Questflow , which is transforming work by using multi-agent AI to handle repetitive tasks in strategic roles. Supported by MiraclePlus and MongoDB Atlas, it enables startups to automate workflows efficiently. As it expands to larger enterprises, it aims to boost AI collaboration and streamline task automation, paving the way for seamless human-AI integration. The concept of a multi-agent collaboration system is new, and it can be challenging for manufacturing organizations to identify the right use case to apply this cutting-edge technology. Below, we propose a use case where three agents collaborate with each other to optimize the performance of a machine. Multi-agent collaboration use case in manufacturing In manufacturing operations, leveraging multi-agent collaboration for predictive maintenance can significantly boost operational efficiency. For instance, consider a production environment where three distinct agents—predictive maintenance, process optimization, and quality assurance—collaborate in real-time to refine machine operations and maintain the factory at peak performance. In Figure 3, the predictive maintenance agent is focused on machinery maintenance. Its main tasks are to monitor equipment health by analyzing sensor data generated from the machines. It predicts machine failures and recommends maintenance actions to extend machinery lifespan and prevent downtime as much as possible. Figure 3. A multi-agent system for production optimization. The process optimization agent is designed to enhance production efficiency. It analyzes production parameters to identify inefficiencies and bottlenecks, and it optimizes said parameters by adjusting them (speed, vibration, etc.) to maintain product quality and production efficiency. This agent also incorporates feedback from the other two agents while making decisions on what production parameter to tune. For instance, the predictive maintenance agent can flag an anomaly in a milling machine temperature sensor reading; for example, if temperature values are going up, the process optimization agent can review the cutting speed parameter for adjustment. The quality assurance agent is responsible for evaluating product quality. It analyzes optimized production parameters and checks how those parameters can affect the quality of the product being fabricated. It also provides feedback for the other two agents. The three agents constantly exchange feedback with each other, and this feedback is also stored in the MongoDB Atlas database as agent short-term memory. In contrast, vector embeddings and sensor data are persisted as long-term memory. MongoDB is an ideal memory provider for agentic AI use case development thanks to its flexible document model, extensive security and data governance features, and horizontal scalability. All three agents have access to a "search_documents" tool, which leverages Atlas Vector Search to query vector embeddings of machine repair manuals and old maintenance work orders. The predictive maintenance agent leverages this tool to figure out additional insights while performing machine root cause diagnostics. Set up the use case shown in this article using our repo . To learn more about MongoDB’s role in the manufacturing industry, please visit our manufacturing and automotive webpage . To learn more about AI agents, visit our Demystifying AI Agents guide .

February 19, 2025

Building Gen AI with MongoDB & AI Partners | January 2025

Even for those of us who work in technology, it can be hard to keep track of the awards companies give and receive throughout the year. For example, in the past few months MongoDB has announced both our own awards (such as the William Zola Award for Community Excellence ) and awards the company has received—like the AWS Technology Partner of the Year NAMER and two awards from RepVue. And that’s just us! It can be a lot! But as hard as they can be to follow, industry awards—and the recognition, thanks, and collaboration they represent—are important. They highlight the power and importance of working together and show how companies like MongoDB and partners are committed to building best-in-class solutions for customers. So without further ado, I’m pleased to announce that MongoDB has been named Technology Partner of the Year in Confluent’s 2025 Global Partner Awards ! As a member of the MongoDB AI Applications Program (MAAP) ecosystem, Confluent enables businesses to build a trusted, real-time data foundation for generative AI applications through seamless integration with MongoDB and Atlas Vector Search. Above all, this award is a testament to MongoDB and Confluent’s shared vision: to help enterprises unlock the full potential of real-time data and AI. Here’s to what’s next! Welcoming new AI and tech partners It's been an action-packed start to the year: in January 2025, we welcomed six new AI and tech partners that offer product integrations with MongoDB. Read on to learn more about each great new partner! Base64 Base64 is an all-in-one solution to bring AI into document-based workflows, enabling complex document processing, workflow automation, AI agents, and data intelligence. “MongoDB provides a fantastic platform for storing and querying all kinds of data, but getting unstructured information like documents into a structured format can be a real challenge. That's where Base64 comes in. We're the perfect onramp, using AI to quickly and accurately extract the key data from documents and feed it right into MongoDB,” said Chris Huff, CEO of Base64. “ This partnership makes it easier than ever for businesses to unlock the value hidden in their documents and leverage the full power of MongoDB." Dataloop Dataloop is a platform that allows developers to build and orchestrate unstructured data pipelines and develop AI solutions faster. " We’re thrilled to join forces with MongoDB to empower companies in building multimodal AI agents”, said Nir Buschi, CBO and co-founder of Dataloop. “Our collaboration enables AI developers to combine Dataloop’s data-centric AI orchestration with MongoDB’s scalable database. Enterprises can seamlessly manage and process unstructured data, enabling smarter and faster deployment of AI agents. This partnership accelerates time to market and helps companies get real value to customers faster." Maxim AI Maxim AI is an end-to-end AI simulation and evaluation platform, helping teams ship their AI agents reliably and more than 5x faster. “ We're excited to collaborate with MongoDB to empower developers in building reliable, scalable AI agents faster than ever,” said Vaibhavi Gangwar, CEO of Maxim AI. “By combining MongoDB’s robust vector database capabilities with Maxim’s comprehensive GenAI simulation, evaluation, and observability suite, this partnership enables teams to create high-performing retrieval-augmented generation (RAG) applications and deliver outstanding value to their customers.” Mirror Security Mirror Security offers a comprehensive AI security platform that provides advanced threat detection, security policy management, continuous monitoring ensuring compliance and protection for enterprises. “ We're excited to partner with MongoDB to redefine security standards for enterprise AI deployment,” said Dr. Aditya Narayana, Chief Research Officer, at Mirror Security. “By combining MongoDB's scalable infrastructure with Mirror Security's end-to-end vector encryption, we're making it simple for organizations to launch secure RAG pipelines and trusted AI agents. Our collaboration eliminates security-performance trade-offs, empowering enterprises in regulated industries to confidently accelerate their AI initiatives while maintaining the highest security standards.” Squid AI Squid AI is a full-featured platform for creating private AI agents in a faster, secure, and automated way. “As an AI agent platform that securely connects to MongoDB in minutes, we're looking forward to helping MongoDB customers reveal insights, take action on their data, and build enterprise AI agents,” said Leslie Lee, Head of Product at Squid AI. “ By pairing Squid's semantic RAG and AI functions with MongoDB's exceptional performance , developers can build powerful AI agents that respond to new inputs in real-time.” TrojAI TrojAI is an AI security platform that protects AI models and applications from new and evolving threats before they impact businesses. “ TrojAI is thrilled to join forces with MongoDB to help companies secure their RAG-based AI apps built on MongoDB,” said Lee Weiner, CEO of TrojAI. “We know how important MongoDB is to helping enterprises adopt and harness AI. Our collaboration enables enterprises to add a layer of security to their database initialization and RAG workflows to help protect against the evolving GenAI threat landscape.” But what, there’s more! In February, we’ve got two webinars coming up with MAAP partners that you don’t want to miss: Build a JavaScript AI Agent With MongoDB and LangGraph.js : Join MongoDB Staff Developer Advocate Jesse Hall and LangChain Founding Software Engineer Jacob Lee for an exclusive webinar that highlights the integration of LangGraph.js, LangChain’s cutting-edge JavaScript library, and MongoDB - live on Feb 25 . Architecting the Future: RAG and Al Agents for Enterprise Transformation : Join MongoDB, LlamaIndex, and Together AI to explore how to strategically build a tech stack that supports the development of enterprise-grade RAG and AI agentic systems, explore technical foundations and practical applications, and learn how the MongoDB Applications Program (MAAP) will enable you to rapidly innovate with AI - content on demand . To learn more about building AI-powered apps with MongoDB, check out our AI Learning Hub and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.

February 11, 2025

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. Just a few years ago, a scenario like this would trigger a frantic scramble: teams digging into logs, manually writing queries, and attempting to correlate thousands of lines of data in different formats to find a single root cause. Today, there’s a more streamlined, AI-driven approach. By combining MongoDB’s developer data platform with large language models (LLMs) and a retrieval-augmented generation (RAG) architecture, you can move from reactive “firefighting” to proactive, data-informed diagnostics. Instead of juggling multiple monitoring dashboards or writing complicated queries by hand, you can simply ask for insights—and the system retrieves and analyzes the necessary data automatically. Facing the unexpected traffic spike Now let’s imagine the same situation, but this time with AI-assisted network management. Shortly after you spot a traffic surge in Toronto, your NOC chatbot pings you with a situation report: requests from one neighborhood are skyrocketing, and an unusually high percentage involve video streaming paths or caching servers. Under the hood, MongoDB automatically ingests every log entry and telemetry event in real time—capturing IP addresses, geographic data, request paths, timestamps, router logs, and sensor data. Meanwhile, textual content (such as error messages, user complaints, and chat transcripts) is vectorized and stored in MongoDB for semantic search. This setup enables near-instant access to relevant information whenever a keyword like “buffering,” “video streams,” or “streaming lag” is mentioned, ensuring a fast, end-to-end diagnosis. Refer to this article to learn more about semantic search. Zeroing in on the root cause Instead of rummaging through separate logging tools, you pose a simple natural-language question to the system: “What might be causing the client’s video stream buffering problem in Toronto?” The LLM responds by generating a custom MongoDB Aggregation Pipeline —written in Python code—tailored to your query. It might look something like this: a $match stage to filter for the last twenty-four hours of data in Toronto, a $group stage to roll up metrics by streaming services, and a $sort stage to find the largest error counts. The code is automatically served back to you, and with a quick confirmation, you execute it on your MongoDB cluster. A moment later, the chatbot returns with a summarized explanation that points to an overloaded local CDN node, along with higher-than-expected requests from older routers known to misbehave under peak load. Next, you ask the system to explain the core issue in simpler terms so you can share it with a business stakeholder. The LLM takes the numeric results from the Aggregation Pipeline, merges them with textual logs that mention “firmware out-of-date,” and then outputs a cohesive explanation. It even suggests that many of these older routers are still running last year’s firmware release—a known contributor to buffering issues on video streams during traffic spikes. How retrieval-augmented generation (RAG) helps The power behind this effortless insight is a RAG architecture, which marries semantic search with generative text responses. First, the LLM uses vector search in MongoDB to retrieve only those log entries, complaint records, and knowledge base articles that directly relate to streaming. Once it has these key data chunks, the LLM can generate—and continually refine—its analysis. Figure 1. Network chatbot architecture with MongoDB. When the system references historical data to confirm that “similar spikes occurred during the playoffs last year” or that “users with older firmware frequently complain about buffering,” it’s not blindly guessing. Instead, it’s accessing domain-specific logs, user feedback, and diagnostic documents stored in MongoDB, and then weaving them together into a coherent explanation. This eliminates guesswork and slashes the time your team would otherwise spend on low-level data cleanup, correlation, and interpretation. Executing automated remediation Armed with these insights, your team can roll out a targeted fix, possibly involving an auto-update to the affected routers or load-balancing traffic to alternative CDN endpoints. MongoDB’s Change Streams can monitor for future anomalies. If a traffic spike starts to look suspiciously similar to the scenario you just solved, the system can raise a proactive alert or even initiate the fix automatically. Refer to the official documentation to learn more about the change streams. Meanwhile, the cost savings add up. You no longer need engineers manually piecing data together, nor do you endure prolonged user dissatisfaction while you try to figure out what’s happening. Everything from anomaly detection to root-cause analysis and recommended mitigation steps is fed through a single pipeline—visible and explainable in plain language. A future of AI-driven operations This scenario highlights how (gen) AI Ops and MongoDB complement each other to transform network management: Schema flexibility: MongoDB’s document-based model effortlessly stores logs, performance metrics, and user feedback in a single, consistent environment. Real-time performance: With horizontal scaling, you can ingest the massive volumes of data generated by network logs and user requests at any hour of the day. Vector search integration: By embedding textual data (such as logs, user complaints, or FAQs) and storing those vectors in MongoDB, you enable instant retrieval of semantically relevant content—making it easy for an LLM to find exactly what it needs. Aggregation + LLM: An LLM can auto-generate MongoDB Aggregation Pipelines to sift through numeric data with ease, while a second pass to the LLM composes a final summary that merges both numeric and textual analysis. Once you see how much time and effort this end-to-end workflow saves, you can extend it across the entire organization. Whether it’s analyzing sudden traffic spikes in specific geographies, diagnosing a security event, or handling peak online shopping loads during a holiday sale, the concept remains the same: empower people to ask natural-language questions about complex data, rely on AI to craft the specialized queries behind the scenes, and store it all in a platform that can handle unbounded complexity. Ready to embrace gen AI ops with MongoDB? Network disruptions will never fully disappear, but how quickly and intelligently you respond can be a game-changer. By uniting MongoDB with LLM-based AI and a retrieval-augmented generation (RAG) strategy, you transform your network operations from a tangle of logs and dashboards into a conversational, automated, and deeply informed system. Sign up for MongoDB Atlas to start building your own RAG-based workflows. With intelligent vector search, automated pipeline generation, and natural-language insight, you’ll be ready to tackle everything from video streams buffering complaints to the next unexpected traffic surge—before users realize there’s a problem. If you would like to learn more about how to build gen AI applications with MongoDB, visit the following resources: Learn more about MongoDB capabilities for artificial intelligence on our product page. Get started with MongoDB Vector Search by visiting our product page. Blog: Leveraging an Operational Data Layer for Telco Success Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads? ” presented by MongoDB Field CTO, Rick Houlihan.

February 5, 2025

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. The consequences of a data breach are extensive and significantly impactful. These incidents have transformed from simple cybersecurity concerns into catalysts for financial losses, reputational harm, legal challenges, regulatory penalties, and a significant decline in consumer trust. Even with an increased focus on data security, organizations must adopt modern data architecture to effectively mitigate these risks. For example, using a database solution like MongoDB with built-in encryption, role-based access control, and audit logging can help organizations safeguard sensitive data and respond proactively to potential vulnerabilities. Check out our AI Learning Hub to learn more about building AI-powered apps with MongoDB. The challenge of data security in finance Financial institutions face numerous challenges in protecting data integrity during modernization efforts. The increasing sophistication of cyberattacks, coupled with the need to comply with evolving regulations like the General Data Protection Regulation (GDPR) and the Digital Operational Resilience Act (DORA), creates a complex environment for data management. Institutions must also navigate technical sprawl, where diverse applications and data management systems complicate compliance and operational efficiency. Addressing these challenges requires a holistic approach that integrates data protection into the core design of digital transformation initiatives. Financial institutions need to adopt robust data management practices, ensure the encryption of sensitive data, and maintain vigilant cybersecurity measures. Collaboration with trusted third-party vendors, adopting a privacy-first strategy, and complying with global data protection regulations are essential steps toward safeguarding data privacy in this rapidly evolving digital landscape. Discover how the RegData Protection Suite (RPS), built on MongoDB , enables you to balance technological advancement with regulatory requirements. The solution: MongoDB and RegData MongoDB offers unparalleled reliability, scalability, and flexibility, making it an ideal choice for financial services. MongoDB enables financial institutions to combine operational and AI data in a unified interface and can be deployed on-premises with Enterprise Advanced or across any major cloud provider with MongoDB Atlas , multi-cloud, and hybrid cloud when needed. When combined with RegData's Protection Suite (RPS), organizations can effectively tackle the challenges of digital transformation. RPS is a cloud-native application security platform designed to protect sensitive data through advanced techniques such as encryption, anonymization, and tokenization. Figure 1. Simplified architecture of the RPS solution. Key Features of RegData Protection Suite: Core Configuration: Provides services and a user interface to configure the protection of data. RPS Engine: A sophisticated core engine equipped with various data protection tools. This module is the heart of the application and is responsible for all data protection. Consists of encryption, anonymization, tokenization, and pseudonymization RPS Reporting: A vital component focused on data protection oversight. It gathers and analyzes information on the business application activities protected by RPS to generate a range of valuable reports RPS Manager: Provides end-to-end monitoring capabilities for the components of the RPS platform. RPS Integration: RPS seamlessly integrates with various applications, ensuring that sensitive data is protected across diverse environments. The synergy between MongoDB and RegData shines through in practical applications. For instance, a private bank can leverage hybrid cloud deployments to modernize its operations while maintaining data security. By utilizing RPS, the bank can protect sensitive information during cloud migrations and ensure compliance with regulatory requirements. Additionally, as financial institutions explore outsourcing, RPS helps mitigate risks by anonymizing sensitive data, allowing organizations to maintain control over their data even when leveraging external service providers. Embracing a zero-trust approach for gen AI applications With the rise of AI (and particularly gen AI), banks are developing increasingly more AI- and gen AI-powered applications. While on-premise AI/gen AI model development and testing provides a high level of data security and confidentiality, it may not be within the bank’s budget to afford a production-grade GPU compute pool or one that is large enough to offer sufficient scalability and economy of scale. With this dilemma, banks have begun developing models in private clouds and then deploying on the public cloud to leverage its scalability and economy of scale. MongoDB can serve as that unified operational data layer for a variety of data sources, structured, semi-structured, or unstructured that may also come in different forms (eg. tabular, geospatial, network graph, time series, etc.) for the model development, training, fine-tuning and/or testing. When the model is tested and found to be working, it can then be deployed to the public cloud to serve the AI/gen AI applications. The figure below shows the high-level architecture of how a private bank implemented its gen AI application with MongoDB and RPS. Figure 2. Gen AI data flow architecture focused on data protection. The road to modernization As financial institutions navigate the complexities of digital transformation, the partnership between MongoDB and RegData offers a robust solution for securing data. By adopting a comprehensive data protection strategy, organizations can innovate confidently while ensuring compliance with regulatory standards. Embracing these technologies not only enhances data security but also paves the way for a more resilient and agile financial sector. Establishing a robust data architecture with a modern data platform like MongoDB Atlas enables financial institutions to effectively modernize by consolidating and analyzing data in any format in real-time, driving value-added services and features to consumers while ensuring privacy and security concerns are adequately addressed with built-in security controls across all data. Whether managed in a customer environment or through MongoDB Atlas, a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive financial data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies. Are you prepared to harness these capabilities for your projects or have any questions about this? Then please reach out to us at industry.solutions@mongodb.com or nfo@regdata.ch . You can also take a look at the following resources: RegData & MongoDB: Securing Digital Transformation Streamline Data Control and Compliance with RegData & MongoDB Implementing an Operational Data Layer Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads? ” presented by MongoDB Field CTO, Rick Houlihan.

January 23, 2025

Building a Unified Data Platform for Gen AI

In today’s digital-first world, data is the lifeblood of innovation and decision-making. Yet, businesses often find themselves constrained by outdated and fragmented systems that fail to meet the demands of a fast-paced, interconnected landscape. Legacy architectures—such as the 1970s-era mainframes still used in industries like banking—create inefficiencies, siloed data, and operational bottlenecks, leaving organizations struggling to deliver timely, actionable insights. The pressure to adapt is mounting, as customer expectations for real-time interactions and personalized services continue to grow. To thrive in this competitive environment, organizations must embrace a transformative approach to managing their data estates—one that integrates advanced technologies seamlessly. Check out our AI Learning Hub to learn more about building AI-powered apps with MongoDB. Unified data platforms powered by operational data layers (ODLs), generative AI (gen AI), and vector search are the solution. These innovations do more than just modernize data handling; they unlock new opportunities for agility, efficiency, and value creation, empowering businesses to make informed decisions, improve customer experiences, and drive growth. Let’s explore how these technologies are reshaping the way businesses consume, integrate, and leverage their data. Figure 1. Conceptual model of a Converged AI Data Store, showing multimodal data ingest. From stale to real-time data: The case for operational data layers In the rapidly evolving digital landscape, businesses can no longer afford to rely on outdated, batch-processed data. The demands of modern operations require instant access to fresh, accurate information. Yet many organizations continue to struggle with fragmented systems that deliver stale data, creating roadblocks in decision-making and customer engagement. This is where the concept of an ODL becomes transformative. Acting as a centralized hub, an ODL integrates data from multiple transactional systems in real-time, ensuring businesses have a unified and up-to-date view of their operations. Let’s explore how ODLs can revolutionize business processes: 1. Enabling real-time customer interactions Imagine a customer service representative handling a support call. Without real-time access to the latest data—such as a customer’s recent transactions, support history, or preferences—the interaction may feel disconnected and inefficient. An ODL solves this problem by consolidating and providing real-time data. For example, a telecom provider could use an ODL to ensure its agents have immediate access to recent billing information, technical issues reported, and ongoing resolutions. This not only empowers the agents but also leaves the customer with a seamless and satisfactory experience. 2. Streamlining account management Real-time data isn’t just about resolving customer issues; it’s also critical for proactive engagement. In industries like banking and retail, customers often need immediate updates on their accounts, such as current balances, transaction details, or loyalty points. By integrating APIs with the ODL, businesses can offer instantaneous responses to these queries. For instance, a retail bank could enable customers to check recent purchases or transfers through a chatbot that queries the ODL in real-time, delivering fast, accurate results. 3. Enhancing compliance and reporting Highly regulated industries, such as finance and healthcare, face additional challenges in managing large volumes of historical data for audits and compliance. Traditional systems often struggle to handle such demands, resulting in time-consuming manual processes. ODLs, when combined with gen AI, enable businesses to extract, summarize, and structure this data efficiently. For instance, a financial institution could use an ODL to generate compliance reports that pull data from diverse sources—both structured and unstructured—and ensure they meet regulatory standards with minimal manual intervention. 4. Supporting metadata and governance Another often overlooked advantage of an ODL is its ability to support metadata management and data governance. For large enterprises operating across multiple geographies, changes in localized data models are frequent and complex. An ODL can act as a centralized repository, capturing these updates and enabling advanced search functionalities for impact analysis. For example, a global enterprise could use an ODL to track changes in data definitions, understand usage patterns, and ensure compliance with governance policies across regions—all while reducing the risk of errors. The transformative power of gen AI and vector search As businesses transition to real-time data strategies powered by ODLs, the potential to unlock even greater insights lies in adopting cutting-edge tools like gen AI and vector search. These technologies are revolutionizing the way organizations consume and interpret data, enabling unprecedented efficiency and intelligence. Gen AI: By generating actionable insights, predictions, and content, gen AI enables businesses to turn static data into a strategic resource. For example, a retailer could use gen AI to analyze customer purchase histories and recommend personalized product bundles. Vector search: This technology translates high-dimensional data like text, images, and audio into vectors, enabling accurate, intuitive searches. For instance, healthcare providers can search for similar patient cases by symptoms, enhancing diagnostics and treatment planning. By incorporating these tools into an ODL, businesses can go beyond basic data integration, creating smarter, more agile operations capable of delivering value in real-time. Figure 2. Retrieval Augmented Generation (RAG) implementation, using the converged AI data store to provide context to the LLM prompt. New opportunities: Revolutionizing operations with gen AI and operational data layers The integration of gen AI and vector search into ODLs opens up a world of opportunities for businesses to enhance customer experience, streamline operations, and innovate at scale. Here’s how these technologies drive transformation: Enhanced data discovery: With vector search, organizations can quickly and accurately retrieve relevant data from massive datasets, simplifying complex searches. Improved customer experience: Gen AI–powered ODLs analyze customer behavior to deliver personalized recommendations, building stronger customer relationships. Increased operational efficiency: Automating routine data tasks with gen AI reduces manual effort, enabling teams to focus on strategic initiatives. Enhanced agility and innovation: By enabling rapid development of AI-driven applications, businesses can quickly adapt to market changes and stay ahead of the competition. As organizations embrace these capabilities, they position themselves to thrive in an increasingly competitive and data-driven world. Architectural options for data processing Modernizing data platforms requires a robust architecture that can handle both batch and real-time processing. Depending on their needs, organizations often choose between lambda or kappa architectures, and MongoDB can serve as a flexible operational layer for both. The lambda architecture The lambda architecture is ideal for organizations that need to process both batch and real-time data. It consists of three layers: Batch layer: This layer processes large volumes of historical data offline. Gen AI can enrich this data by generating insights and predictions. Speed layer: This layer handles real-time data streams, enabling immediate responses to changes. Serving layer: This layer combines batch and real-time data into a unified view, powered by MongoDB for seamless queries and data access. The kappa architecture For businesses focused on real-time analytics, the kappa architecture simplifies operations by using a single stream for data processing. MongoDB excels as the operational speed layer in this setup, supporting high-speed, real-time data updates enhanced by gen AI. By choosing the right architecture and leveraging MongoDB’s capabilities, businesses can ensure their data platforms are future ready. A journey toward data modernization Data modernization is a progressive journey, transforming businesses step by step into smarter, more agile systems. It begins with a basic operational data store , where read-heavy workloads are offloaded from legacy systems into MongoDB, boosting performance and accessibility. Next comes the enriched ODL , adding real-time analytics to turn raw data into actionable insights. Then, as needs grow, parallel writes enable MongoDB to handle write-heavy operations, enhancing speed and reliability. In the transition to the system of transaction , monolithic systems are replaced with agile microservices directly connected to MongoDB, simplifying operations and accelerating innovation. Finally, businesses reach the system of record , a domain-driven architecture where MongoDB provides unmatched scalability, flexibility, and efficiency. Each phase of this journey unlocks new opportunities, transforming data into a dynamic asset that powers innovation, operational excellence, and growth. Figure 3. A conceptual model showcasing the joint implementation of the Kappa (Data in Motion) and Lambda (Data at Rest) frameworks on MongoDB Atlas, utilizing Stream Processing for real-time data and Online Archive/Federation features for historical data management. The unified and intelligent future of data As businesses embrace real-time data architectures and advanced AI capabilities, the potential for innovation is boundless. With solutions like MongoDB, organizations can seamlessly integrate and harness their data, driving operational excellence and delivering exceptional customer experiences. Now is the time to modernize, innovate, and unlock the full potential of your data. Discover how TCS and MongoDB are harmonizing technologies for the future. Start your data modernization journey today! Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads ? ” presented by MongoDB Field CTO, Rick Houlihan.

January 15, 2025

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. Check out our AI Learning Hub to learn more about building AI-powered apps with MongoDB. Introduction to Together AI and MongoDB Atlas From the first look, it’s impressive how well Together AI is designed for gen AI projects. It’s a powerful platform that lets developers train, fine-tune, and deploy open-source AI models with just a few lines of code. This is a critical component for retrieval-augmented generation (RAG) . With RAG, AI can pull real-time business-specific data from MongoDB Atlas , which means retailers get more reliable and relevant outputs. That’s crucial when dealing with data as dynamic as customer behavior or inventory movement from online and physical stores. With its flexible data model, MongoDB Atlas is an ideal database engine for handling diverse data needs. It’s fully managed, multi-cloud, and exceptional at managing different data types, including the vector embeddings that power AI applications. One important feature is MongoDB Atlas Vector Search , a smart library that stores and indexes vector embeddings, making it simple to integrate with Together AI. This lets retailers generate timely, personalized responses to customer queries, creating a better experience all around. Identifying retail use cases With Together AI and MongoDB Atlas working together, the possibilities for retail are huge. Here are some of the use cases we’ve been exploring and testing with clients, each bringing measurable value to the table: Product description generation Product onboarding to a retail e-commerce portal is a time-consuming effort for many retailers. They need to ensure they’ve created a product description that matches the image, then deploy it to their e-commerce portal. For multilingual portals and multiple operating geographies, this challenge of accuracy increases. With Together AI’s support for multimodal models (e.g. Llama 3.2) and MongoDB Atlas’s vector embeddings, we can create accurate product descriptions in multiple languages. Check out a demo app to see it in action. Figure 1. Demo application for generating product descriptions. Personalized product recommendations Imagine being able to offer each customer exactly what they’re looking for, without them even asking. With Together AI’s retrieval and inference endpoints and MongoDB Atlas Vector Search, we can create highly personalized product recommendations. Analyzing individual preferences, browsing history, and past purchases becomes seamless, giving customers exactly what they need, possibly exactly when they need it. Conversational AI-powered tools (a.k.a. chatbots) We’re also deploying intelligent conversational tools that can understand complex questions, offer personalized assistance, and drive conversions. Together AI, paired with MongoDB Atlas, makes these bots responsive and relevant so customers feel like they’re talking to a knowledgeable adviser rather than a chatbot. When real-time data informs the responses, customer experience is enhanced. Dynamic pricing and promotions Pricing in retail is often a moving target, and AI-driven insights help us optimize our approach. We’ve used Together AI and MongoDB Atlas to analyze market trends, competitor pricing, and customer demand to keep our pricing competitive and adjust promotions in real-time. It’s incredible how much more strategic we can be with AI’s help. Inventory management and forecasting This might be one of the most impactful use cases I’ve worked on—using AI to predict demand and optimize stock levels. With Together AI and MongoDB Atlas, it’s easier to balance inventory, reduce waste, and ensure the products customers want are always in stock. This leads to better efficiency and fewer out-of-stock scenarios. Implementing retail use cases with Together AI and MongoDB Atlas Let me share a concrete example that really brings these concepts to life. Case study: Building a multilingual product-description-generation system We recently worked on a solution to create a product-description-generation system for an e-commerce platform. The goal was to provide highly descriptive product information based on the images of the products from the product catalog. This use case really demonstrated the value of storing the data in MongoDB and using the multilanguage capabilities of Together AI’s inference engine. Embeddings and inference with Together AI: Together AI generated product descriptions based on images retrieved from the product catalog using Llama 3.2 vision models. This way, each product’s unique characteristics were considered, then generated in multiple languages. These descriptions could then be embedded into the MongoDB Atlas Vector Search database via a simple API. Indexed embeddings with MongoDB Atlas Vector Search: Using MongoDB Atlas Vector Search capabilities, we created embeddings, and then indexed them to be used to retrieve relevant data based on other matched product queries. This step made sure the product descriptions were not just accurate but also relevant to the images. Real-time data processing: By connecting this setup to a real-time product dataset, we ensured that product descriptions in multiple languages were always updated automatically. So when a marketplace vendor or retailer uploads new images with distinct characteristics, they get up-to-date product descriptions in the catalog. This project showcased how Together AI and MongoDB Atlas could work together to deliver a solution that was reliable, highly efficient, and scalable. The feedback from users was overwhelmingly positive. They especially appreciated how intuitive and helpful the product descriptions were and how simple the whole product onboarding process could become for multilingual businesses spread across multiple geographical regions. Figure 2. An example of a query and response flow for a RAG architecture using MongoDB and Together AI. Looking at the business impacts For a retail organization, implementing Together AI and MongoDB Atlas can streamline the approach to gen AI, creating an effective and immediate positive impact to business in several ways: Reduced product onboarding time and costs: Retailers can onboard products faster and quickly make them available on their sales channels because of the ready-to-use tools and prebuilt integrations. This cuts down on the need for custom code and significantly lowers development costs. Increased flexibility and customization: MongoDB’s flexible document model and Together AI’s inference engine enables retailers to mold their applications to fit specific needs, such as back-office data processing, demand forecasting, and pricing as well as customer-facing conversational AI. Seamless integration with existing systems: MongoDB Atlas, in particular, integrates seamlessly with other frameworks we’re already using, like LangChain and LlamaIndex. This has made it easier to bring AI capabilities to adopt across various business units. Added support and expertise: The MongoDB AI Applications Program (MAAP) is especially helpful in beginning the journey into AI adoption across enterprises. It offers not just architectural guidance but also hands-on support, so enterprises can implement AI projects with confidence and a well-defined road map. Combining Together AI and MongoDB Atlas for a powerful approach to retail Together AI and MongoDB Atlas are a powerful combination for anyone in the retail industry looking to make the most of gen AI. It is evident how they help unlock valuable use cases, from personalized customer experiences to real-time operational improvements. By adopting MongoDB Atlas with Together AI, retailers can innovate, create richer customer interactions, and ultimately gain a competitive edge. If you’re exploring gen AI for retail, you’ll find that this combination has a quick, measurable, and transformative impact. Learn more about Together AI by visiting www.together.ai . For additional information, check out Together AI: Advancing the Frontier of AI With Open Source Embeddings, Inference, and MongoDB Atlas . Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads ? ” presented by MongoDB Field CTO, Rick Houlihan.

January 13, 2025

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. Check out our AI Learning Hub to learn more about building AI-powered apps with MongoDB. Enter retrieval-augmented generation (RAG) : a powerful approach that leverages generative AI and advanced search capabilities to deliver precise insights on demand. For IT decision-makers, the key challenge lies in integrating operational data with unstructured information—which can span object stores (like Amazon S3 and SharePoint), internal wikis, PDFs, Microsoft Word documents, and more. Enterprises must unlock value from curated, reliable internal data sources that often hold critical yet hard-to-access information. By combining RAG’s capabilities with these data assets, retailers can find contextually accurate information. For example, they can seamlessly surface needed information like return policies, refund processes, shipment details, and product recalls, driving operational efficiency and enhancing customer experiences. To provide the most relevant context to a large language model (LLM) , traditional RAG (which has typically relied on vector search) needs to be combined with real-time data in an operational database, the last conversation captured in a customer relationship management API call to a REST endpoint, or both. RAG has evolved to become agentic—that is, it’s capable of understanding a user inquiry and translating it to determine which path to use and which repositories to access to answer the question. MongoDB Atlas and Dataworkz provide an agentic RAG as a service solution that enables retailers to combine operational data with relevant unstructured data to create transformational experiences for their customers. MongoDB Atlas stores and unifies diverse data formats—such as customer purchases, inventory levels, and product descriptions—making them easily accessible. Dataworkz then transforms this data into vector embeddings, enabling a multistep agentic RAG pipeline to retrieve and create personalized, context-aware responses in real time. This is especially powerful in the context of customer support, product recommendations, and inventory management. When customers interact with retailers, Dataworkz dynamically retrieves real-time data from MongoDB Atlas, and, where needed, combines it with unstructured information to generate personalized AI responses, enhancing the customer experience. This architecture improves engagement, optimizes inventory, and provides scalable, adaptable AI capabilities, ultimately driving a more efficient and competitive retail operation. Reasons for using MongoDB Atlas and Dataworkz MongoDB Atlas and Dataworkz work together to deliver agentic RAG as a service for a smarter, more responsive customer experience. Here’s a quick breakdown of how: Vector embeddings and smart search: The Dataworkz RAG builder enables anyone to build sophisticated retrieval mechanisms that turn words, phrases, or even customer behaviors into vector embeddings—essentially, numbers that capture their meaning in a way that’s easy for AI to understand—and store them in MongoDB Atlas. This makes it possible to search for content based on meaning rather than exact wording, so search results are more accurate and relevant. Scalable, reliable performance: MongoDB Atlas’s cloud-based, distributed setup is built to handle high-traffic retail environments, minimizing disruptions during peak shopping times. Deep context with Dataworkz’s agentic RAG as a service: Retailers can build agentic workflows powered by RAG pipelines that combine lexical and semantic search with knowledge graphs to fetch the most relevant data from unstructured operational and analytical data sources before generating AI responses. This combination gives ecommerce brands the power to personalize experiences at a vastly larger scale. Figure 1: Reference architecture for customer support chatbots with Dataworkz and MongoDB Atlas Retail e-commerce use cases So how does this all work in practice? Here are some real-world examples of how MongoDB Atlas and Dataworkz are helping ecommerce brands create standout experiences. Building smarter customer-support chatbots Today’s shoppers want quick, accurate answers, and RAG makes this possible. When a customer asks a chatbot, “Where’s my order?” RAG enables the bot to pull the latest order and shipping details stored in MongoDB Atlas. Even if the question is phrased differently—say, “I need my order status”—the RAG-powered vector search can interpret the intent and fetch the correct response. As a result, the customer gets the help they need without waiting on hold or navigating complex menus. Personalizing product recommendations Imagine a customer who’s shown interest in eco-friendly products. With MongoDB Atlas’s vector embeddings, a RAG-powered system can identify this preference and adjust recommendations accordingly. So when the customer returns, they see suggestions that match their style—like organic cotton clothing or sustainably sourced kitchenware. This kind of recommendation feels relevant and thoughtful, making the shopping experience more enjoyable and increasing the chances of a purchase. Creating dynamic marketing content Marketing thrives on fresh, relevant content. With MongoDB Atlas managing product data and Dataworkz generating personalized messages, brands can send out dynamic promotions that truly resonate. For example, a customer who browsed outdoor gear might receive a curated email with top-rated hiking boots or seasonal discounts on camping equipment. This kind of targeted messaging feels personal, not pushy, building stronger customer loyalty. Enhancing site search experiences Traditional e-commerce searches often rely on exact keyword matches, which can lead to frustrating dead ends. But with MongoDB Atlas Vector Search and Dataworkz’s agentic RAG, search can be much smarter. For example, if a customer searches for “lightweight travel shoes,” the system understands that they’re looking for comfortable, portable footwear for travel, even if none of the product listings contain those exact words. This makes shopping smoother and more intuitive and less of a guessing game. Understanding trends in customer sentiment For e-commerce brands, understanding how customers feel can drive meaningful improvements. With RAG, brands can analyze reviews, social media comments, and support interactions to capture sentiment trends in MongoDB Atlas. Imagine a brand noticing a spike in mentions of “too small” in product reviews for a new shoe release—this insight lets them quickly adjust sizing info on the product page or update their stock. It’s a proactive approach that shows customers they’re being heard. Interactions that meet customers where they are In essence, MongoDB Atlas and Dataworkz’s RAG models enable retailers to make e-commerce personalization and responsiveness smarter, more efficient, and easier to scale. Together, they help retailers deliver exactly what customers are looking for—whether it’s a personalized recommendation, a quick answer from a chatbot, or just a better search experience. In the end, it’s about meeting customers where they are, with the information and recommendations they need. With MongoDB and Dataworkz, e-commerce brands can create that kind of connection—making shopping easier, more enjoyable, and ultimately more memorable. Learn more about Dataworkz on MongoDB by visiting dataworkz.com . The Dataworkz free tier is powered by MongoDB Atlas Vector Search . Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads ? ” presented by MongoDB Field CTO, Rick Houlihan.

December 23, 2024

Building Gen AI with MongoDB & AI Partners | November 2024

Unless you’ve been living under a rock, you know it’s that time of year again—re:Invent season! Last week, I was in Las Vegas for AWS re:Invent, one of our industry’s most important annual conferences. re:Invent 2024 was a whirlwind of keynote speeches, inspirational panels and talks, and myriad ways to spend time with colleagues and partners alike. And this year, MongoDB had its biggest re:Invent presence ever, alongside some of the most innovative players in AI. The headline? The MongoDB AI Application Program (MAAP) . Capgemini, Confluent, IBM, QuantumBlack AI by McKinsey, and Unstructured joined MAAP, boosting the value customers receive from the program and cementing MongoDB’s position as a leader in driving AI innovation. We also announced that MongoDB is collaborating with Meta to support developers with Meta models and the end-to-end MAAP technology stack. Figure 1: The MongoDB booth at re:Invent 2024 MongoDB’s re:Invent AI Showcase was another showstopper. As part of the AI Hub in the re:Invent expo hall, MongoDB and partners Arcee, Arize, Fireworks AI, and Together AI collaborated on engaging demos and presentations. Meanwhile, the “ Building Your AI Stack ” panel—which included leaders from MongoDB and MAAP partners Anyscale, Cohere, and Fireworks AI—featured an insightful discussion on building AI technologies, challenges with taking applications to production, and what’s next in AI. As at every re:Invent, networking opportunities abounded; I had so many interesting and fruitful conversations with partners, customers, and developers during the week’s many events, including those MongoDB sponsored—like the Cabaret of Innovation with Accenture, Anthropic, and AWS; the Galactic Gala with Cohere; and Tuesday’s fun AI Game Night with Arize, Fireworks AI, and Hasura. Figure 2: Networking at the Galactic Gala Whether building solutions or building relationships, MongoDB’s activities at re:Invent 2024 showcased the importance of collaboration to the future of AI. As we close out the year, I’d like to thank our amazing partners for their support—we look forward to more opportunities to collaborate in 2025! And if you want to learn more about MongoDB’s announcements at re:Invent 2024, please read this blog post by my colleague Oliver Tree. Welcoming new AI and tech partners In November, we also welcomed two new AI and tech partners that offer product integrations with MongoDB. Read on to learn more about each great new partner! Braintrust Braintrust is an end-to-end platform for building and evaluating world-class AI apps. “ We're excited to partner with MongoDB to share how you can build reliable and scalable AI applications with vector databases,” said Ankur Goyal, CEO of Braintrust. “By combining Braintrust’s simple evaluation workflows with MongoDB Atlas, developers can build an end-to-end RAG application and iterate on prompts and models without redeploying their code.” Langtrace Langtrace is an open-source observability tool that collects and analyzes traces in order to help you improve your LLM apps. “ We're thrilled to join forces with MongoDB to help companies trace, debug, and optimize their RAG features for faster production deployment and better accuracy,” said Karthik Kalyanaraman, Co-founder and CTO at Langtrace AI. “MongoDB has made it dead simple to launch a scalable vector database with operational data. Our collaboration streamlines the RAG development process by empowering teams with database observability, speeding up time to market and helping companies get real value to customers faster.” But wait, there's more! To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.

December 12, 2024

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. A satisfied customer is a loyal customer. They are 80% more likely to renew their policies, directly contributing to sustainable growth. However, one major challenge faced by many insurance companies is the inefficiency of their call centers. Agents often struggle to quickly locate and deliver accurate information to customers, leading to frustration and dissatisfaction. This article explores how Dataworkz and MongoDB can transform call center operations. By converting call recordings into searchable vectors (numerical representations of data points in a multi-dimensional space), businesses can quickly access relevant information and improve customer service. We'll dig into how the integration of Amazon Transcribe, Cohere, and MongoDB Atlas Vector Search—as well as Dataworkz's RAG-as-a-service platform— is achieving this transformation. Check out our AI Learning Hub to learn more about building AI-powered apps with MongoDB. From call recordings to vectors: A data-driven approach Customer service interactions are goldmines of valuable insights. By analyzing call recordings, we can identify successful resolution strategies and uncover frequently asked questions. In turn, by making this information—which is often buried in audio files— accessible to agents, they can give customers faster and more accurate assistance. However, the vast volume and unstructured nature of these audio files make it challenging to extract actionable information efficiently. To address this challenge, we propose a pipeline that leverages AI and analytics to transform raw audio recordings into vectors as shown in Figure 1: Storage of raw audio files: Past call recordings are stored in their original audio format Processing of the audio files with AI and analytics services (such as Amazon Transcribe Call Analytics ): speech-to-text conversion, summarization of content, and vectorization Storage of vectors and metadata: The generated vectors and associated metadata (e.g., call timestamps, agent information) are stored in an operational data store Figure 1: Customer service call insight extraction and vectorization flow Once the data is stored in vector format within the operational data store, it becomes accessible for real-time applications. This data can be consumed directly through vector search or integrated into a retrieval-augmented generation (RAG) architecture, a technique that combines the capabilities of large language models (LLMs) with external knowledge sources to generate more accurate and informative outputs. Introducing Dataworkz: Simplifying RAG implementation Building RAG pipelines can be cumbersome and time-consuming for developers who must learn yet another stack of technologies. Especially in this initial phase, where companies want to experiment and move fast, it is essential to leverage tools that allow us to abstract complexity and don’t require deep knowledge of each component in order to experiment with and realize the benefits of RAG quickly. Dataworkz offers a powerful and composable RAG-as-a-service platform that streamlines the process of building RAG applications for enterprises. To operationalize RAG effectively, organizations need to master five key capabilities: ETL for LLMs: Dataworkz connects with diverse data sources and formats, transforming the data to make it ready for consumption by generative AI applications. Indexing: The platform breaks down data into smaller chunks and creates embeddings that capture semantics, storing them in a vector database. Retrieval: Dataworkz ensures the retrieval of accurate information in response to user queries, a critical part of the RAG process. Synthesis: The retrieved information is then used to build the context for a foundational model, generating responses grounded in reality. Monitoring: With many moving parts in the RAG system, Dataworkz provides robust monitoring capabilities essential for production use cases. Dataworkz's intuitive point-and-click interface (as seen in Video 1) simplifies RAG implementation, allowing enterprises to quickly operationalize AI applications. The platform offers flexibility and choice in data connectors, embedding models, vector stores, and language models. Additionally, tools like A/B testing ensure the quality and reliability of generated responses. This combination of ease of use, optionality, and quality assurance is a key tenet of Dataworkz's "RAG as a Service" offering. Diving deeper: System architecture and functionalities Now that we’ve looked at the components of the pre-processing pipeline, let’s explore the proposed real-time system architecture in detail. It comprises the following modules and functions (see Figure 2): Amazon Transcribe , which receives the audio coming from the customer’s phone and converts it into text. Cohere ’s embedding model, served through Amazon Bedrock , vectorizes the text coming from Transcribe. MongoDB Atlas Vector Search receives the query vector and returns a document that contains the most semantically similar FAQ in the database. Figure 2: System architecture and modules Here are a couple of FAQs we used for the demo: Q: “Can you explain the different types of coverage available for my home insurance?” A: “Home insurance typically includes coverage for the structure of your home, your personal belongings, liability protection, and additional living expenses in case you need to temporarily relocate. I can provide more detailed information on each type if you'd like.” Q: “What is the process for adding a new driver to my auto insurance policy?" A: “To add a new driver to your auto insurance policy, I'll need some details about the driver, such as their name, date of birth, and driver's license number. We can add them to your policy over the phone, or you can do it through our online portal.” Note that the question is reported just for reference, and it’s not used for retrieval. The actual question is provided by the user through the voice interface and then matched in real-time with the answers in the database using Vector Search. This information is finally presented to the customer service operator in text form (see Fig. 3). The proposed architecture is simple but very powerful, easy to implement, and effective. Moreover, it can serve as a foundation for more advanced use cases that require complex interactions, such as agentic workflows , and iterative and multi-step processes that combine LLMs and hybrid search to complete sophisticated tasks. Figure 3: App interface, displaying what has been asked by the customer (left) and how the information is presented to the customer service operator (right) This solution not only impacts human operator workflows but can also underpin chatbots and voicebots, enabling them to provide more relevant and contextual customer responses. Building a better future for customer service By seamlessly integrating analytical and operational data streams, insurance companies can significantly enhance both operational efficiency and customer satisfaction. Our system empowers businesses to optimize staffing, accelerate inquiry resolution, and deliver superior customer service through data-driven, real-time insights. To embark on your own customer service transformation, explore our GitHub repository and take advantage of the Dataworkz free tier . Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads ? ” presented by MongoDB Field CTO, Rick Houlihan.

November 27, 2024

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. According to Accenture’s 2023 consumer banking study , 44% of consumers aged 18-44 reported difficulty accessing human support when needed, underscoring the demand for more responsive digital solutions that help bridge this gap between customers and financial services. Generative AI technologies like chatbots and virtual assistants can fill this need by instantly addressing inquiries, providing tailored financial advice, and anticipating future needs. This shift has tremendous growth potential; the global chatbot market is expected to grow at a CAGR of 23.3% from 2023 to 2030 , with the financial sector experiencing the fastest growth rate of 24.0%. This shift is more than just a convenience; it aims to create a smarter, more engaging, and intuitive banking journey for every user. Check out our AI Learning Hub to learn more about building AI-powered apps with MongoDB. Simplifying self-service banking with AI Navigating daily banking activities like transfers, payments, and withdrawals can often raise immediate questions for customers: “Can I overdraft my account?” “What will the penalties be?” or “How can I avoid these fees?” While the answers usually lie within the bank’s terms and conditions, these documents are often dense, complex, and overwhelming for the average user. At the same time, customers value their independence and want to handle their banking needs through self-service channels, but wading through extensive fine print isn't what they signed up for. By integrating AI-driven advisors into the digital banking experience, banks can provide a seamless, in-app solution that delivers instant, relevant answers. This removes the need for customers to leave the app to sift through pages of bank documentation in search of answers, or worse, endure the inconvenience of calling customer service. The result is a smoother and user-friendly interaction, where customers feel supported in their self-service journey, free from the frustration of navigating traditional, cumbersome information sources. The entire experience remains within the application, enhancing convenience and efficiency. Solution overview This AI-driven solution enhances the self-service experience in digital banking by applying Retrieval-Augmented Generation (RAG) principles, which combine the power of generative AI with reliable information retrieval, ensuring that the chatbot provides accurate, contextually relevant responses. The approach begins by processing dense, text-heavy documents, like terms and conditions, often the source of customer inquiries. These documents are divided into smaller, manageable chunks vectorized to create searchable data representations. Storing these vectorized chunks in MongoDB Atlas allows for efficient querying using MongoDB Atlas Vector Search , making it possible to instantly retrieve relevant information based on the customer’s question. Figure 1: Detailed solution architecture When a customer inputs a question in the banking app, the system quickly identifies and retrieves the most relevant chunks using semantic search. The AI then uses this information to generate clear, contextually relevant answers within the app, enabling a smooth, frustration-free experience without requiring customers to sift through dense documents or contact support. Figure 2: Leafy Bank mock-up chatbot in action How MongoDB supports AI-driven banking solutions MongoDB offers unique capabilities that empower financial institutions to build and scale AI-driven applications. Unified data model for flexibility: MongoDB’s flexible document model unifies structured and unstructured data, creating a consistent dataset that enhances the AI’s ability to understand and respond to complex queries. This model enables financial institutions to store and manage customer data, transaction history, and document content within a single system, streamlining interactions and making AI responses more contextually relevant. Vector search for enhanced querying: MongoDB Atlas Vector Search makes it easy to perform semantic searches on vectorized document chunks, quickly retrieving the most relevant information to answer user questions. This capability allows the AI to find precise answers within dense documents, enhancing the self-service experience for customers. Scalable integration with AI models: MongoDB is designed to work seamlessly with leading AI frameworks, allowing banks to integrate and scale AI applications quickly and efficiently. By aligning MongoDB Atlas with cloud-based LLM providers, banks can use the best tools available to interpret and respond to customer queries accurately, meeting demand with responsive, real-time answers. High performance and cost efficiency: MongoDB’s multi-cloud, developer-friendly platform allows financial institutions to innovate without costly infrastructure changes. It’s built to scale as data and AI needs to grow, ensuring banks can continually improve the customer experience with minimal disruptions. MongoDB’s built-in scalability allows banks to expand their AI capabilities effortlessly, offering a future-proof foundation for digital banking. Building future-proof applications Implementing generative AI presents several advantages, not only for end-users of the interactive banking applications but also for financial institutions: Enhanced user experience encourages customer satisfaction, ensures retention, boosts reputation, and reduces customer turnover while unlocking new opportunities for cross-selling and up-selling to increase revenue, drive growth and elevate customer value. Moreover, adopting AI-driven initiatives prepares the groundwork for businesses to develop innovative, creative, and future-proof applications to address customer needs and upgrade business applications with features that are shaping the industry and will continue to do so, here are some examples: Summarize and categorize transactional information by powering applications with MongoDB’s Real-Time Analytics . Understand and find trends based on customer behavior that could positively impact and leverage fraud prevention , anti-money laundering (AML) , and credit card application (just to mention a few). Offering investing, budgeting, and loan assessments through AI-powered conversational banking experience. In today’s data-driven world, companies face increasing pressure to stay ahead of rapid technological advancements and ever-evolving customer demands. Now more than ever, businesses must deliver intuitive, robust, and high-performing services through their applications to remain competitive and meet user expectations. Luckily, MongoDB provides businesses with comprehensive reference architectures for building generative AI applications, an end-to-end technology stack that includes integrations with leading technology providers, professional services, and a coordinated support system through the MongoDB AI Applications Program (MAAP) . By building AI-enriched applications with the leading multi-cloud developer data platform, companies can leverage low-cost, efficient solutions through MongoDB’s flexible and scalable document model which empowers businesses to unify real-time, operational, unstructured, and AI-related data, extending and customizing their applications to seize upcoming technological opportunities. Check out these additional resources to get started on your AI journey with MongoDB: How Leading Industries are Transforming with AI and MongoDB Atlas - E-book Our Solutions Library is where you can learn about different use cases for gen AI and other interesting topics that are applied to financial services and many other industries. Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads ? ” presented by MongoDB Field CTO, Rick Houlihan.

November 26, 2024

AI-Driven Noise Analysis for Automotive Diagnostics

Aftersales service is a crucial revenue stream for the automotive industry, with leading manufacturers executing repairs through their dealer networks. One global automotive giant recently embarked on an ambitious project to revolutionize their diagnostic process. Their project—which aimed to increase efficiency, customer satisfaction, and revenue throughput—involved the development of an AI-powered solution that could quickly analyze engine sounds and compare them to a database of known problems, significantly reducing diagnostic times for complex engine issues. Traditional diagnostic methods can be time-consuming, expensive, and imprecise, especially for complex engine issues. MongoDB’s client in automotive manufacturing envisioned an AI-powered solution that could quickly analyze engine sounds and compare them to a database of known problems, significantly reducing diagnostic times. Check out our AI Learning Hub to learn more about building AI-powered apps with MongoDB. Initial setbacks, then a fresh perspective Despite the client team's best efforts, the project faced significant challenges and setbacks during the nine-month prototype phase. Though the team struggled to produce reliable results, they were determined to make the project a success. At this point, MongoDB introduced its client to Pureinsights , a specialized gen AI implementation and MongoDB AI Application Program partner , to rethink the solution and to salvage the project. As new members of the project team, and as Pureinsights’s CTO and Lead Architect, respectively, we brought a fresh perspective to the challenge. Figure 1: Before and after the AI-powered noise diagnostic solution A pragmatic approach: Text before sound Upon review, we discovered that the project had initially started with a text-based approach before being persuaded to switch to sound analysis. The Pureinsights team recommended reverting to text analysis as a foundational step before tackling the more complex audio problem. This strategy involved: Collecting text descriptions of car problems from technicians and customers. Comparing these descriptions against a vast database of known issues already stored in MongoDB. Utilizing advanced natural language processing, semantic / vector search, and Retrieval Augmented Generation techniques to identify similar cases and potential solutions. Our team tested six different models for cross-lingual semantic similarity, ultimately settling on Google's Gecko model for its superior performance across 11 languages. Pushing boundaries: Integrating audio analysis With the text-based foundation in place, we turned to audio analysis. Pureinsights developed an innovative approach to the project by combining our AI expertise with insights from advanced sound analysis research. We drew inspiration from groundbreaking models that had gained renown for their ability to identify cities solely from background noise in audio files. This blend of AI knowledge and specialized audio analysis techniques resulted in a robust, scalable system capable of isolating and analyzing engine sounds from various recordings. We adapted these sophisticated audio analysis models, originally designed for urban sound identification, to the specific challenges of automotive diagnostics. These learnings and adaptations are also applicable to future use cases for AI-driven audio analysis across various industries. This expertise was crucial in developing a sophisticated audio analysis model capable of: Isolating engine and car noises from customer or technician recordings. Converting these isolated sounds into vectors. Using these vectors to search the manufacturer's existing database of known car problem sounds. At the heart of this solution is MongoDB’s powerful database technology. The system leverages MongoDB’s vector and document stores to manage over 200,000 case files. Each "document" is more akin to a folder or case file containing: Structured data about the vehicle and reported issue Sound samples of the problem Unstructured text describing the symptoms and context This unified approach allows for seamless comparison of text and audio descriptions of customer engine problems using MongoDB's native vector search technology. Encouraging progress and phased implementation The solution's text component has already been rolled out to several dealers, and the audio similarity feature will be integrated in late 2024. This phased approach allows for real-world testing and refinement before a full-scale deployment across the entire repair network. The client is taking a pragmatic, step-by-step approach to implementation. If the initial partial rollout with audio diagnostics proves successful, the plan is to expand the solution more broadly across the dealer network. This cautious (yet forward-thinking) strategy aligns with the automotive industry's move towards more data-driven maintenance practices. As the solution continues to evolve, the team remains focused on enhancing its core capabilities in text and audio analysis for current diagnostic needs. The manufacturer is committed to evaluating the real-world impact of these innovations before considering potential future enhancements. This measured approach ensures that each phase of the rollout delivers tangible benefits in efficiency, accuracy, and customer satisfaction. By prioritizing current diagnostic capabilities and adopting a phased implementation strategy, the automotive giant is paving the way for a new era of efficiency and customer service in their aftersales operations. The success of this initial rollout will inform future directions and potential expansions of the AI-powered diagnostic system. A new era in automotive diagnostics The automotive giant brought industry expertise and a clear vision for improving their aftersales service. MongoDB provided the robust, flexible data platform essential for managing and analyzing diverse, multi-modal data types at scale. We, at Pureinsights, served as the AI application specialist partner, contributing critical AI and machine learning expertise, and bringing fresh perspectives and innovative approaches. We believe our role was pivotal in rethinking the solution and salvaging the project at a crucial juncture. This synergy of strengths allowed the entire project team to overcome initial setbacks and develop a groundbreaking solution that combines cutting-edge AI technologies with MongoDB's powerful data management capabilities. The result is a diagnostic tool leveraging text and audio analysis to significantly reduce diagnostic times, increase customer satisfaction, and boost revenue through the dealer network. The project's success underscores several key lessons: The value of persistence and flexibility in tackling complex challenges The importance of choosing the right technology partners The power of combining domain expertise with technological innovation The benefits of a phased, iterative approach to implementation As industries continue to evolve in the age of AI and big data, this collaborative model—bringing together industry leaders, technology providers, and specialized AI partners—sets a new standard for innovation. It demonstrates how companies can leverage partnerships to turn ambitious visions into reality, creating solutions that drive business value while enhancing customer experiences. The future of automotive diagnostics—and AI-driven solutions across industries—looks brighter thanks to the combined efforts of forward-thinking enterprises, cutting-edge database technologies like MongoDB, and specialized AI partners like Pureinsights. As this solution continues to evolve and deploy across the global dealer network, it paves the way for a new era of efficiency, accuracy, and customer satisfaction in the automotive industry. This solution has the potential to not only revolutionize automotive diagnostics but also set a new standard for AI-driven solutions in other industries, demonstrating the power of collaboration and innovation. To deliver more solutions like this—and to accelerate gen AI application development for organizations at every stage of their AI journey—Pureinsights has joined the MongoDB AI Application Program (MAAP). Check out the MAAP page to learn more about the program and how MAAP ecosystem members like Pureinsights can help your organization accelerate time-to-market, minimize risks, and maximize the value of your AI investments. Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads ? ” presented by MongoDB Field CTO, Rick Houlihan.

September 27, 2024

Collaborating to Build AI Apps: MongoDB and Partners at Google Cloud Next '24

From April 9 to April 11, Las Vegas became the center of the tech world, as Google Cloud Next '24 took over the Mandalay Bay Convention Center—and the convention’s spotlight shined brightest on gen AI. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Between MongoDB’s big announcements with Google Cloud (which included an expanded collaboration to enhance building, scaling, and deploying GenAI applications using MongoDB Atlas Vector Search and Vertex AI ), industry sessions, and customer meetings, we offered in-booth lightning talks with leaders from four MongoDB partners—LangChain, LlamaIndex, Patronus AI, and Unstructured—who shared valuable insights and best practices with developers who want to embed AI into their existing applications or build new-generation apps powered by AI. Developing next-generation AI applications involves several challenges, including handling complex data sources, incorporating structured and unstructured data, and mitigating scalability and performance issues in processing and analyzing them. The lightning talks at Google Cloud Next ‘24 addressed some of these critical topics and presented practical solutions. One of the most popular sessions was from Harrison Chase , co-founder and CEO at LangChain , an open-source framework for building applications based on large language models (LLMs). Harrison provided tips on fixing your retrieval-augmented generation (RAG) pipeline when it fails, addressing the most common pitfalls of fact retrieval, non-semantic components, conflicting information, and other failure modes. Harrison recommended developers use LangChain templates for MongoDB Atlas to deploy RAG applications quickly. Meanwhile, LlamaIndex —an orchestration framework that integrates private and public data for building applications using LLMs—was represented by Simon Suo , co-founder and CTO, who discussed the complexities of advanced document RAG and the importance of using good data to perform better retrieval and parsing. He also highlighted MongoDB’s partnership with LlamaIndex, allowing for ingesting data into the MongoDB Atlas Vector database and retrieving the index from MongoDB Atlas via LlamaParse and LlamaCloud . Guillaume Nozière - Patronus AI Andrew Zane - Unstructured Amidst so many booths, activities, and competing programming, a range of developers from across industries showed up to these insightful sessions, where they could engage with experts, ask questions, and network in a casual setting. They also learned how our AI partners and MongoDB work together to offer complementary solutions to create a seamless gen AI development experience. We are grateful for LangChain, LlamaIndex, Patronus AI, and Unstructured's ongoing partnership. We look forward to expanding our collaboration to help our joint customers build the next generation of AI applications. To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub and stop by our Partner Ecosystem Catalog to read about our integrations with these and other AI partners.

April 23, 2024