How Canara HSBC Life Insurance Optimized Costs and Claims Processing with MongoDB
Since 2008, Canara HSBC Life Insurance has focused relentlessly on bringing a fresh perspective to an industry known more for stability and conservatism rather than innovation. Since its inception in 2008 as a joint venture between Canara Bank and HSBC Insurance, Canara HSBC Life Insurance has strived to differentiate itself from the competition through enhanced customer interactions, launching cutting-edge digital products, and integrating digital services that cater to the evolving needs of customers. For the past six years Chief Operating Officer, Mr. Sachin Dutta, has been on a mission to bring this customer-first mindset to the digital products and touchpoints his team creates. Speaking at MongoDB’s annual .local developer conference in Delhi, Dutta outlined Canara HSBC Life Insurance’s ongoing digital transformation journey, and how his team's focus on customer success and business efficiency led them to work with MongoDB for improved efficiencies and results. “I truly value the partnership we have with MongoDB. We are building a future-ready organization, and this partnership clearly helps us achieve our aim of reaching the last mile possible in customer servicing. Mr. Sachin Dutta, Chief Operating Officer, Canara HSBC Modernizing the architecture and driving developer efficiency Canara HSBC’s digital transformation was centered on three technical pillars: the cloud, analytics, and mobility. The company focused on creating a more integrated organization and automating manual processes within the system. “We try to remove human intervention with a life insurance policy delivered in seconds and claims that are settled virtually in seconds,” Dutta says. To get there, Canara HSBC Life Insurance had to move on from its existing architecture, which required multifaceted changes and several new implementations: Monolithic applications made alterations a time-consuming process A reliance on rigid relational databases prolonged development timelines, forcing developers to spend time wrangling data when they could be building better products for customers. The fully on-premises system had supported the organization in the past but required future-proofing to support growth and deliver a better customer experience. Because of this valuable development time and money were spent managing, patching, and scaling databases, rather than getting new products into the hands of customers. These technical issues impacted the speed of business, particularly during month-end and year-end data processing, when the volumes were high. In addition, batch processing stood in the way of creating the real-time availability of information customers wanted. Dutta and his senior team also realized that their existing infrastructure would make it more challenging to find the right talent in the market, as the existing infrastructure was increasingly becoming outdated. Dutta realized early on that, in order for Canara HSBC to attract and retain the best and brightest developers, the insurer had to offer the chance to work with the latest technologies. Platforms like MongoDB would be integral to this effort. “I want to create an organization that is attracting talent and where people start to enjoy their work, and that benefit then gets passed on to the customers, ” Mr. Dutta says. Looking to overhaul its existing infrastructure, Canara HSBC Life Insurance wanted to move fast and hire the talent required to best serve its end customers. Dutta summarized the situation succinctly: "We found that some of those relational structures that had worked for us would not take us through the next 10 years.” Migrating to a secure, fully managed database platform After evaluating the solutions on the market, the team decided to transition from their existing on-premises relational databases, like IBM DB2, MySQL, and Postgres, to MongoDB Atlas . In the last six years of my work, I’m pleased to say that MongoDB has seamlessly integrated all the processes in the backend. We migrated from a completely legacy-based setup to the new fully managed MongoDB service to enhance IT productivity Mr. Sachin Dutta The first stage of the journey was moving from monolithic applications and relational databases to a microservices architecture. With its flexible schema and capabilities for redundancy, automation, and scalability, MongoDB served as the best partner to help facilitate the transition. Next, the team moved to modernize key parts of the business, such as underwriting, freeing their data to power more automation in straight-through processing (STP) of policies and faster claims processing. The adoption of a hybrid cloud model shifted Canara HSBC Life Insurance away from on-premises databases to MongoDB Atlas. As a fully managed cloud database, MongoDB Atlas solves issues related to scalability, database management, and overall reliability. MongoDB Atlas is also cloud agnostic, giving the insurance company an option to work with Azure, AWS, and Google Cloud. Mongo Atlas’ BI Connector bridged the gap between MongoDB and traditional BI tools. This seamless integration allowed Canara HSBC Life Insurance to deploy its preferred reporting tools and, when coupled with MongoDB Atlas’ real-time analytics capability, made batch processing a thing of the past. Halving delivery times and driving business efficiencies Moving to MongoDB Atlas has had a profound impact on the breadth of digital experiences Canara HSBC Life Insurance can offer customers and the speed at which new products can be developed. Something that used to take months, with the implementation of our new tools could be completed in a couple of weeks or days Mr. Sachin Dutta And it’s not only the customer experience and product delivery that has benefited from the partnership. Canara HSBC Life Insurance has also realized substantial efficiency gains and savings as a result of working with MongoDB. We are leveraging artificial intelligence as a core capability to predict human behavior and auto-underwrite policies wherein around half of the policies issued today are issued by the system Mr. Sachin Dutta Highlighted results include: Straight-through processing (STP) surged from 37% to an impressive 60%. This is set to increase further with AI/ML integrations and rule suggestions. Policy issuance turnaround time improved by 60%. Efficiency in operations led to a 20% cost-saving per policy issuance. Canara HSBC experienced 2x top-line growth due to seamless integration with analytical tools. Looking ahead, Canara HSBC Life Insurance has already outlined three key areas where the MongoDB partnership will grow. First, Dutta wants to take advantage of MongoDB Atlas’ flexible document data model to collect and organize data on customers from across the business, making MongoDB Atlas the sole database at Canara HSBC Life Insurance and creating a true customer 360 data layer to power sophisticated data analytics. In financial services, this capability is referred to as know your customer (KYC). “We want to build a data layer that provides a unique experience to the customer after getting to know them,” he says. “That’ll help the company generate better NPS scores and retain customers.” Second, the adoption and integration of AI and machine learning tools also factor heavily into future plans. MongoDB Atlas, with its flexible schema, compatibility with various machine learning platforms, and AI-specific features — such as Vector Search and storage — is a good fit for the company. In Dutta's words, "We are going to scale up and capture the GenAI space.” Last, Dutta wants to take advantage of the MongoDB Atlas SQL interface, connectors, and drivers to augment business intelligence for reporting and precise SQL-based report conversions. Learn More about how MongoDB Works with global Insurers
A Year of Thrill: Celebrating the New MongoDB University
Staying ahead in the ever-evolving tech world is like being on a rollercoaster - it’s exciting but it can also make your head spin! When we set out to revamp MongoDB University , we wanted to provide developers with frictionless access to the learning content they needed to conquer their challenges. It’s been one year since the launch and we are over the moon about how far the new MongoDB University has come - a one-stop hub with fresh certifications and new content, all available online. But none of this would have been possible without the incredible support of our engaged learners who have embarked on this ride with us. Our commitment to delivering top-notch educational resources has been nothing short of award-winning, earning us the prestigious Silver Excellence Award from the Brandon Hall Group in the category of ‘Best Advance in Creating an Extended Enterprise Learning Program’. The success of the new University has also been featured at industry events, including Cognition and the Customer Education Management Association Conference. So, let’s buckle up and take a tour through the revamped MongoDB University! New content With over 1,000 learning assets, including videos, hands-on labs, code recaps, quizzes, and courses, there’s something for everyone. Plus, now we have you covered with language subtitles in Chinese (Traditional and Simplified), Korean, Spanish, Deutsch, Japanese, Italian, French, and Portuguese. The best part? All of the content is free, online, and you can take your time and learn at your own pace. Let’s explore three of our newest courses: Data Modeling for MongoDB: This course guides you through the foundational steps of creating an effective data model in MongoDB, including identifying entities and workloads, mapping and modeling relationships between entities, and using key schema design patterns. Atlas Essentials: In this course, you’ll gain the foundational knowledge and skills needed to use MongoDB Atlas, the multi-cloud developer data platform. MongoDB for SQL Professionals: This course will help you leverage your SQL skills to get started with MongoDB quickly. You can practice what you learn and gain valuable real-world skills with labs hosted in our in-browser development environment. The new experience allows you to explore hands-on exercises as part of our courses, or you can dive directly into a standalone lab . The labs include step-by-step instructions that guide you through each scenario and even provide hints along the way. And for those looking for nuggets of MongoDB wisdom, explore the catalog of over 30 Learning Bytes . These short videos cover a wide variety of topics and are designed to help you get the knowledge you need quickly. New certifications Our freshly revamped certifications are recognized by professional institutions and are your ticket to having your knowledge and skills formally validated and recognized by MongoDB. They are a great way to elevate yourself in your current role and increase your marketability for future roles. Certifications come with bragging rights, inclusion in the Credly Talent Directory, and a shiny Credly badge that makes it easy for you to share your achievement. So, let’s explore the two new certifications: MongoDB Associate Developer: Certify that you possess the essential skills to create beginner-level applications utilizing MongoDB as a backing database for Java, Python, C#, PHP, or Java applications. MongoDB Associate Database Administrator: Validate your MongoDB database administration skills by certifying your knowledge of building, supporting, and securing MongoDB infrastructure. And if you need a boost, once you complete one of the certification learning paths you will automatically unlock a 50% discount on a certification exam. Educators and students can check out the Academia program to learn how to receive a free exam. All aboard! This is just the beginning of the adventure and we are excited for what is yet to come. So, fasten your seatbelt, and let’s keep learning together! With over 1,000 learning assets, MongoDB University has what you need to pick up new skills and advance your career. Explore free courses, practice with hands-on labs, and earn MongoDB certifications.
Building AI with MongoDB: Retrieval-Augmented Generation (RAG) Puts Power in Developers’ Hands
As recently as 12 months ago, any mention of retrieval-augmented generation (RAG) would have left most of us confused. However, with the explosion of generative AI, the RAG architectural pattern has now firmly established itself in the enterprise landscape. RAG presents developers with a potent combination. They can take the reasoning capabilities of pre-trained, general-purpose LLMs and feed them with real-time, company-specific data. As a result, developers can build AI-powered apps that generate outputs grounded in enterprise data and knowledge that is accurate, up-to-date, and relevant. They can do this without having to turn to specialized data science teams to either retrain or fine-tune models — a complex, time-consuming, and expensive process. Over this series of Building AI with MongoDB blog posts, we’ve featured developers using tools like MongoDB Atlas Vector Search for RAG in a whole range of applications. Take a look at our AI case studies page and you’ll find examples spanning conversational AI with chatbots and voice bots, co-pilots, threat intelligence and cybersecurity, contract management, question-answering, healthcare compliance and treatment assistants, content discovery and monetization, and more. Further reflecting its growing adoption, Retool’s State of AI survey from a couple of weeks ago shows Atlas Vector Search earning the highest net promoter score (NPS) among developers . Check out our AI resource page to learn more about building AI-powered apps with MongoDB. In this blog post, I’ll highlight three more interesting and novel use cases: Unlocking geological data for better decision-making and accelerating the path to net zero at Eni Video and audio personalization at Potion Unlocking insights from enterprise knowledge bases at Kovai Eni makes terabytes of subsurface unstructured data actionable with MongoDB Atlas Based in Italy, Eni is a leading integrated energy company with more than 30,000 employees across 69 countries. In 2020, the company launched a strategy to reach net zero emissions by 2050 and develop more environmentally and financially sustainable products. Sabato Severino, Senior AI Solution Architect for Geoscience at Eni, explains the role of his team: “We’re responsible for finding the best solutions in the market for our cloud infrastructure and adapting them to meet specific business needs.” Projects include using AI for drilling and exploration, leveraging cloud APIs to accelerate innovation, and building a smart platform to promote knowledge sharing across the company. Eni’s document management platform for geosciences offers an ecosystem of services and applications for creating and sharing content. It leverages embedded AI models to extract information from documents and stores unstructured data in MongoDB. The challenges for Severino’s team were to maintain the platform as it ingested a growing volume of data — hundreds of thousands of documents and terabytes of data — and to enable different user groups to extract relevant insights from comprehensive records quickly and easily. With MongoDB Atlas , Eni users can quickly find data spanning multiple years and geographies to identify trends and analyze models that support decision-making within their fields. The platform uses MongoDB Atlas Search to filter out irrelevant documents while also integrating AI and machine learning models, such as vector search, to make it even easier to identify patterns. “The generative AI we’ve introduced currently creates vector embeddings from documents, so when a user asks a question, it retrieves the most relevant document and uses LLMs to build the answer,” explains Severino. “We’re looking at migrating vector embeddings into MongoDB Atlas to create a fully integrated, functional system. We’ll then be able to use Atlas Vector Search to build AI-powered experiences without leaving the Atlas platform — a much better experience for developers.” Read the full case study to learn more about Eni and how it is making unstructured data actionable. Video personalization at scale with Potion and MongoDB Potion enables salespeople to personalize prospecting videos at scale. Already over 7,500 sales professionals at companies including SAP, AppsFlyer, CaptivateIQ, and Opensense are using SendPotion to increase response rates, book more meetings, and build customer trust. All a sales representative needs to do is record a video template, select which words need to be personalized, and let Potion’s audio and vision AI models do the rest. Kanad Bahalkar, co-founder and CEO at Potion explains: “The sales rep tells us what elements need to be personalized in the video — that is typically provided as a list of contacts with their name, company, desired call-to-action, and so on. Our vision and audio models then inspect each frame and reanimate the video and audio with personalized messages lip-synced into the stream. Reanimation is done in bulk in minutes. For example, one video template can be transformed into over 1,000 unique video messages, personalized to each contact.” Potion’s custom generative AI models are built with PyTorch and TensorFlow, and run on Amazon Sagemaker. Describing their models, Kanad says “Our vision model is trained on thousands of different faces, so we can synthesize the video without individualized AI training. The audio models are tuned on-demand for each voice.” And where does the data for the AI lifecycle live? “This is where we use MongoDB Atlas ,” says Kanad. “We use the MongoDB database to store metadata for all the videos, including the source content for personalization, such as the contact list and calls to action. For every new contact entry created in MongoDB, a video is generated for it using our AI models, and a link to that video is stored back in the database. MongoDB also powers all of our application analytics and intelligence . With the insights we generate from MongoDB, we can see how users interact with the service, capturing feedback loops, response rates, video watchtimes, and more. This data is used to continuously train and tune our models in Sagemaker." On selecting MongoDB Kanad says, “I had prior experience of MongoDB and knew how easy and fast it was to get started for both modeling and querying the data. Atlas provides the best-managed database experience out there, meaning we can safely offload running the database to MongoDB. This ease-of-use, speed, and efficiency are all critical as we build and scale the business." To further enrich the SendPotion service, Kanad is planning to use more of the developer features within MongoDB Atlas. This includes Atlas Vector Search to power AI-driven semantic search and RAG for users who are exploring recommendations across video libraries. The engineering team is also planning on using Atlas Triggers to enable event-driven processing of new video content. Potion is a member of the MongoDB AI Innovators program. Asked about the value of the program, Kanad responds, “Access to free credits helped support rapid build and experimentation on top of MongoDB, coupled with access to technical guidance and support." Bringing the power of Vector Search to enterprise knowledge bases Founded in 2011, Kovai is an enterprise software company that offers multiple products in both the enterprise and B2B SaaS arena. Since its founding, the company has grown to nearly 300 employees serving over 2,500 customers. One of Kovai’s key products is Document360, a knowledge base platform for SaaS companies looking for a self-service software documentation solution. Seeing the rise of GenAI, Kovai began developing its AI assistant, “Eddy.” The assistant provides answers to customers' questions utilizing LLMs augmented by retrieving information in a Document360 knowledge base. During the development phase Kovai’s engineering and data science teams explored multiple vector databases to power the RAG portion of the application. They found the need to sync data between its system-of-record MongoDB database and a separate vector database introduced inaccuracies in answers from the assistant. The release of MongoDB Atlas Vector Search provided a solution with three key advantages for the engineers: Architectural simplicity: MongoDB Vector Search's architectural simplicity helps Kovai optimize the technical architecture needed to implement Eddy. Operational efficiency: Atlas Vector Search allows Kovai to store both knowledge base articles and their embeddings together in MongoDB collections, eliminating “data syncing” issues that come with other vendors. Performance: Kovai gets faster query response from MongoDB Vector Search at scale to ensure a positive user experience. Atlas Vector Search is robust, cost-effective, and blazingly fast! Said Saravana Kumar, CEO, Kovai, when speaking about his team's experience Specifically, the team has seen the average time taken to return three, five, and 10 chunks between two and four milliseconds, and if the question is a closed loop, the average time reduces to less than two milliseconds. You can learn more about Kovai’s journey into the world of RAG in the full case study . Getting started As the case studies in our Building AI with MongoDB series demonstrate, retrieval-augmented generation is a key design pattern developers can use as they build AI-powered applications for the business. Take a look at our Embedding Generative AI whitepaper to explore RAG in more detail.
Building AI with MongoDB: Improving Productivity with WINN.AI’s Virtual Sales Assistant
Better serving customers is a primary driver for the huge wave of AI innovations we see across enterprises. WINN.AI is a great example. Founded in November 2021 by sales tech entrepreneur Eldad Postan Koren and cybersecurity expert Bar Haleva, their innovations are enabling sales teams to improve productivity by increasing the time they focus on customers. WINN.AI orchestrates a multimodal suite of state-of-the-art models for speech recognition, entity extraction, and meeting summarization, relying on MongoDB Atlas as the underlying data layer. I had the opportunity to sit down with Orr Mendelson, Ph.D., Head of R&D at WINN.AI, to learn more. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Tell us a little bit about what WINN.AI is working to accomplish Today’s salespeople spend over 25% of their time on administrative busywork - costing organizations time, money, and opportunity. We are working to change that so that sales teams can spend more time solving their customer’s problems and less on administrative tasks. At the heart of WINN.AI is an AI-powered real-time sales assistant that joins your virtual meetings. It detects and interprets customer questions, and immediately surfaces relevant information for the salesperson. Think about retrieving relevant customer references or competitive information. It can provide prompts from a sales playbook, and also make sure meetings stay on track and on time. After concluding, WINN.AI extracts relevant information from the meeting and updates the CRM system. WINN.AI integrates with the leading tools used by sales teams, including Zoom, Hubspot, Salesforce, and more. Can you describe what role AI plays in your application? Our technology allows the system to understand not only what people are saying on a sales call, but also to specifically comprehend the context of a sales conversation, thus optimizing meeting summaries and follow-on actions. This includes identifying the most important talking points discussed in the meeting, knowing how to break down the captured data into different sales methodology fields (MEDDICC, BANT, etc.), and automatically pushing updates to the CRM. What specific AI/ML techniques, algorithms, or models are utilized in the application? We started out building and training our own custom Natural Language Processing (NLP) algorithms and later switched to GPT 3.5 and 4 for entity extraction and summarization. Our selection of models is based on specific requirements of the application feature – balancing things like latency with context length and data modality. We orchestrate all of the models with massive automation, reporting, and monitoring mechanisms. This is developed by our engineering teams and assures high-quality AI products across our services and users. We have a dedicated team of AI Engineers and Prompts Engineers that develop and monitor each prompt and response so we are continuously tuning and optimizing app capabilities. How do you use MongoDB in your application stack? MongoDB stores everything in the WINN.AI platform. Organizations and users, sessions, their history, and more. The primary driver for selecting MongoDB was its flexibility in being able to store, index, and query data of any shape or structure. The database fluidly adapts to our application schema, which gives us a more agile approach than traditional relational databases. My developers love the ecosystem that has built up around MongoDB. MongoDB Atlas provides the managed services we need to run, scale, secure, and backup our data. How do you see the broader benefits of MongoDB in your business? In the ever-changing AI tech market, MongoDB is our stable anchor. MongoDB provides the freedom to work with structured and unstructured data while using any of our preferred tools, and we leave database management to the Atlas service. This means my developers are free to create with AI while being able to sleep at night! MongoDB is familiar to our developers so we don’t need any DBA or external experts to maintain and run it safely. We can invest those savings back into building great AI-powered products. What are your future plans for new applications and how does MongoDB fit into them? We’re always looking for opportunities to offer new functionality to our users. Capabilities like Atlas Search for faceted full-text navigation over data coupled with MongoDB’s application-driven intelligence for more real-time analytics and insights are all incredibly valuable. Streaming is one area that I’m really excited about. Our application is composed of multiple microservices that are soon to be connected with Kafka for an event-driven architecture. Building on Kafka based messaging, Atlas Stream Processing is another direction we will explore. It will give our services a way of continuously querying, analyzing and reacting to streaming data without having to first land it in the database. This will give our customers even lower latency AI outputs. Everybody WINNs! Wrapping up Orr, thank you for sharing WINN.AI’s story with the community! WINN.AI is part of the MongoDB AI Innovators program , benefiting from access to free Atlas credits and technical expertise. If you are getting started with AI, sign-up for the program and build with MongoDB.
Why Leading Insurer Manulife Ditched SQL For MongoDB
Manulife, one of the largest life insurance companies in the world, is in the midst of a digital transformation. Earlier this year, Harry Cheung, Chief Architect of Manulife Asia, spoke to industry experts and developers at MongoDB.local in Hong Kong, outlining the transformation journey so far and what’s next for Manulife. Better experiences, happier customers Manulife, like many large enterprises, is under pressure to get new digital products to market, fast. In addition, the insurer is constantly looking for ways to better connect with and serve customers, in real time, by broadening their digital capabilities and further personalizing the interactions customers have with Manulife. Manulife’s existing data infrastructure, however, was becoming a drag on innovation. Traditional relational databases limited how fast the Manulife team could bring new digital products to market. In particular, Manulife’s developers, the architects of these new digital products and services, faced issues working with the existing data infrastructure, including the need to constantly optimize the database, deal with data normalization issues, and work with slow querying of data. From Relational to NoSQL to MongoDB From the outset, Manulife knew that they would build their new digital experience on a NoSQL database. NoSQL is core to our strategy of building our digital experience. The flexible data model [for NoSQL] means you’re not limited by the schema. Harry Cheung, Chief Architect, Manulife Asia After deciding to go the NoSQL, Manulife was won over to MongoDB for several reasons, including: The document data model: MongoDB's document data model means no rigid schemas to slow down development. This allows for faster iterations when building new digital products. From on-premises to the cloud: Moving from a MongoDB on-premises deployment to MongoDB Atlas in the cloud was easy for the Manulife team. Scalability: MongoDB can easily scale horizontally to meet spikes in demand. Enterprise-ready & mature: MongoDB is used by the world’s largest insurers, offering greater flexibility alongside the sorts of core requirements you would expect from an RDBMS, such as ACID transactions. MongoDB Support: Assistance with projects like data migration from on-premises to cloud services on MongoDB Atlas made the transition smoother. A pay-as-you-go model: MongoDB’s elastic scaling capabilities and flexible pricing model keep costs down. On and offline functionality: MongoDB Atlas has built-in mobile device synchronization capabilities, speeding up the development of offline-first insurance applications. Built with MongoDB: Four Use Cases for Manulife MOVE, a Health-Focused App: MOVE is a digital app that encourages users to meet fitness goals, with daily steps linked to insurance premium discounts. MongoDB's JSON-based document model simplified app development and data management. Secondly, Manulife started running the MOVE app on-premises. When they wanted to migrate the app to a public cloud of their choice (from MongoDB to MongoDB Atlas) the process was seamless. Sales Assistance App: Used by 90% of agents, this app helps Manulife agents in the field service customers and complete applications. One area where MongoDB Atlas was particularly helpful was mitigating issues with mobile connectivity and data synchronization. Agents in the field often suffer from internet service interruptions, such as a dropped mobile signal. When the agent’s sales app reconnects, the data from the app has to be synchronized with the backend MongoDB database. Building apps that can handle such offline/online data synchronization, also known as offline-first apps, can significantly eat into development time, slowing time to value for organizations developing robust offline-first apps. MongoDB Atlas Device Sync solves this issue with native offline to online synchronization capabilities to enable uninterrupted client interactions, even in low connectivity areas. Using Atlas Device Sync, the sales app can store customer, proposal, application, and document metadata on the local device (using MongoDB’s dedicated mobile device database), and then synchronize that data and the customer application to the main MongoDB database when connected to the internet. Manulife launched their sales app's offline mode in just 2 months with MongoDB Atlas Device Sync Policy Life Cycle Management: Traditional relational databases spread policy data across multiple tables. With MongoDB, a single document can encapsulate an entire policy, streamlining querying access and enhancing performance. MongoDB is now the system of record for policy servicing and life cycle management. This new system was met with overwhelming approval from Manulife’s developers. In the past, we were using a traditional relational database, with more than 500 core tables. With MongoDB, when I asked developers who had previously used our traditional [RDBMS] database, ‘You have a choice, do you want to use MongoDB or go back to the traditional [database]?’ all our developers said MongoDB. Harry Cheung, Chief Architect, Manulife Asia Claims Processing: MongoDB's capability to handle structured and unstructured data simplified integration with partners, especially in Optical Character Recognition (OCR) for claim processes. Looking ahead Manulife is set on expanding its use of NoSQL databases, with MongoDB identified as the go-to solution for such projects. MongoDB is our internal standard. MongoDB is our strategic partner for NoSQL development. Harry Cheung, Chief Architect, Manulife Asia About Manulife Manulife Financial Corporation is one of the largest life insurance companies in the world. The company provides insurance and financial services to millions of customers in Asia, Canada, and the United States. Manulife operates under different brand names: Manulife in North America and Asia, and John Hancock in the U.S. It's recognized for its long-standing presence in Hong Kong, with a focus on life insurance, mutual funds, and other financial products. In addition to life insurance, Manulife offers a wide range of financial services including wealth and asset management, group benefits, and retirement services. Learn more about our work with the world's leading insurers on our MongoDB for Insurance page.
You Asked, We Listened. It's Here - Dark Mode for Atlas is Now Available in Public Preview
We are thrilled to announce a much-anticipated feature for MongoDB Atlas. Dark mode is now available in Public Preview for users worldwide. Dark mode has been the number one requested feature in MongoDB's feedback forum , and we've taken note. Users have tried browser plugins and other makeshift fixes, but now the wait is over. Our development team diligently worked to introduce a dark mode option, improving user experience with a new and refreshing perspective to the familiar interface of Atlas. This update—which includes 300 converted pages—is not just for our community. It also benefits us as developers, promoting a seamless dark mode experience across different tools in the developer workflow. Dark mode is sleek and sophisticated, aligning with the preferred working styles of many of our developers. Remember that this is an ongoing project, and there may be areas within Atlas that need refining. Rest assured, we will be monitoring our feedback channels closely. Not just a sleek interface We took a thoughtful approach to the overall dark mode user experience, particularly with respect to accessibility considerations. We ensured that our dark mode theme met accessibility standards by checking and adjusting all text, illustrations, and UI elements for color and contrast to help reduce eye strain and address those with light sensitivities while making sure it was still easy to read. We also focused on accommodating the overall light-to-dark background contrast while staying mindful of how they may layer or interact with other elements. Beyond aesthetics, dark mode is a proven method for extending battery life. For our users with OLED or AMOLED screens dark mode ensures the device’s battery life stretches even further by illuminating fewer pixels and encouraging lower brightness levels. Health benefits A typical engineer spends no fewer than eight hours a day in front of a computer, exposing their eyes to multiple digital screens, according to data from Medium . This screen usage can lead to dry eyes, insomnia, and headaches. While dark text on a light background is best for legibility purposes, light text on a dark background helps reduce eye strain in low-light conditions. Enable dark mode preview today To update the theme at any time, navigate to the User Menu in the top right corner, then select User Preferences . Under Appearance , there will be three options. Light Mode: This is the default color scheme. Dark Mode: Our new dark theme. Auto (Sync with OS): This setting will match the operating system's setting. A few things to keep in mind This is a user setting and does not impact other users within a project or organization. Dark mode is not currently available for Charts, Documentation, University, or Cloud Manager. Since we are releasing this in Public Preview , there might be some minor visual bugs. The goal of Public Preview releases is to generate interest and gather feedback from early adopters. It is not necessarily feature-complete and does not typically include consulting, SLAs, or technical support obligations. We have conducted comprehensive internal testing, and we did not find anything that prevents users from using Atlas. While we are still making a few finishing touches feel free to share any feedback using this form . Thank you to all our users who provided valuable feedback and waited patiently for this feature! Keep the feedback coming . We hope you enjoy dark mode, designed to improve accessibility, reduce eye strain and fatigue, and enhance readability. We invite you to experience the difference. Try dark mode today through your MongoDB Atlas portal .
Perfect Your CI/CD Pipelines with MongoDB's New GitHub Action and Docker Image for the Atlas CLI
Do you use GitHub Actions for your CI/CD workflows? Or build using Docker containers? If so, you’ll probably be excited to hear that MongoDB has released: 1. An official GitHub Action and 2. A dedicated Docker image for the Atlas CLI. Together, these two releases make it easier than ever to develop applications with MongoDB Atlas. Since MongoDB announced the Atlas CLI at MongoDB World in 2022, it has become one of our most popular tools for building with the Atlas developer data platform. One of the great things about the Atlas CLI is that it not only caters to the individual developer wanting a mouseless terminal experience—it also makes it easy to programmatically manage Atlas resources throughout the entire development lifecycle. With the new releases for the Atlas CLI with GitHub Actions and Docker, you can easily use the Atlas CLI to build with Atlas while still working natively within your preferred CI/CD platforms. Within GitHub Actions, you now have access to a dedicated Action that allows you to seamlessly manage Atlas resources using your favorite Atlas CLI commands. You can use the predefined workflows available or create custom workflows leveraging native Atlas CLI commands. For example, with one of the predefined workflows you can: create a project, set up the Atlas CLI with an Atlas deployment, retrieve your connection string, and tear down your project and deployment. If you use a platform other than GitHub Actions to manage your CI/CD pipelines, or simply use Docker in your toolchain, you can now also use the Atlas CLI by pulling the Docker image with just one command: docker pull mongodb/atlas From there, you can enter an interactive shell to run Atlas CLI commands as you normally would: docker run --rm -it mongodb/atlas bash atlas --help You can also find detailed information in the MongoDB Documentation on how to run Docker in interactive mode or as a daemon (detached mode) for working with the Atlas CLI. Ready to get started? You can find the Atlas CLI GitHub Action in the GitHub Marketplace and the Atlas CLI Docker image on Docker Hub . If you have any feedback on either experience, share your thoughts with us in the Atlas CLI section of the MongoDB Feedback Engine .
Kathreftis Launches World-Class Identity Access Management with Cymmetri
Security breaches and cyberattacks are more prevalent than ever. These attacks are often targeted at an organization's identity access management systems, with over 60% of cyber threats stemming from identity-based vulnerabilities. To address this critical issue, Kathreftis, an Indian startup, emerged in 2022 with a mission to create a world-class identity access management platform. At the heart of the venture lies the company's flagship product, Cymmetri, a comprehensive solution for identity access management and governance. The critical role of identity access management Cybersecurity threats are increasingly centered around exploiting weaknesses in identity access management, particularly attacking multi-factor authentication (MFA) systems. These attacks often involve compromised usernames and passwords, and they are on the rise. In response to this growing concern, Kathreftis' Founder & CEO, Vikas Jha, set out to address four key challenges when developing Cymmetri: Centralized identity management: The first challenge was to create a unified solution capable of managing all identities with access to various systems, including partners, outsourced services, and all privileged users, through a centralized administrative console. 360-degree visibility: The second challenge involved providing a 360-degree view of all access permissions. For any user with access privileges, Cymmetri shows which applications they can access, their assigned roles, and the level of permissions granted. Scalability and data management: The third challenge was handling the increasing data volume as an organization expands. As more data is generated and access privileges are granted, system performance may slow down. Cymmetri aimed to address these issues while ensuring optimal performance. High availability and scalability: The fourth challenge was to ensure that the identity access management platform remained highly available and horizontally scalable to meet the demands of a growing user base. Choosing the right database solution Selecting the appropriate database was critical to meet these challenges. Jha and his team decided to opt for a document database due to its ability to simplify data storage. Unlike relational databases, which involve complex tables, rows, and columns, document databases offered a more flexible and streamlined solution. MongoDB was the choice because of its versatility, supporting both on-premises and cloud deployment. Additionally, many of Kathreftis' developers were already familiar with MongoDB, facilitating rapid development and a quicker go-to-market strategy. This agility provided by MongoDB was a significant advantage for the company. Global compliance and accessibility To expand its reach, Kathreftis needed a database that would adhere to Indian data privacy laws while remaining adaptable to international markets. Jha emphasized that Cymmetri needed to accommodate varying regulatory environments. "We are located in India and we needed a database that would support Indian laws. But we also knew that, as we started to grow into markets like the Middle East, the U.K., and the U.S., we wanted something that wouldn't require major code changes," Jha explained. "Today, if you want to use Cymmetri in Australia, you just need to use the Australia cloud on AWS and Azure, and the system is ready to launch." Unlocking success with MongoDB for startups Cymmetri's journey to success was further aided by the MongoDB for Startups program, which offers valuable resources such as free MongoDB Atlas credits, technical guidance, co-marketing opportunities, and access to a network of partners with exclusive perks. The company used the free credits for proof of concept (POC) during its early stages, and MongoDB experts reviewed their architecture to ensure it met their requirements. Today, Cymmetri is predominantly used by large enterprises throughout India and the Middle East, including prominent financial services firms, public sector banks, manufacturing companies, cybersecurity organizations, and data resilience managed services providers. With Cymmetri, Kathreftis aims to simplify identity access management implementation, emphasizing ease of use and automation. The company strives to reduce the total cost of ownership for identity access management solutions, making them accessible to businesses of all sizes. In a digital world where security is paramount, Kathreftis and Cymmetri are at the forefront, reshaping how organizations manage and secure their identities. With their innovative solutions and global ambitions, they are poised to make a lasting impact on the world of identity access management.
Unleashing the Power of MongoDB Atlas and Amazon Web Services (AWS) for Innovative Applications
When you use MongoDB Atlas on AWS, you can focus on driving innovation and business value, instead of managing infrastructure. The combination of MongoDB Atlas, the premier developer data platform, and AWS, the largest global public cloud provider empowers organizations to create scalable and intelligent applications while streamlining their data infrastructure management. With MongoDB Atlas and AWS, building GenAI-powered applications is far simpler. MongoDB Vector Search enables developers to build intelligent applications powered by semantic search and generative AI over any type of data. Organizations can use their proprietary application data and vector embeddings to enhance foundation models like large language models (LLMs) via retrieval-augmented generation (RAG). This approach reduces hallucinations and delivers personalized user experiences while scaling applications seamlessly to meet evolving demands and maintaining top-tier security standards. MongoDB real-world use cases MongoDB helped Forbes accelerate provisioning, maintenance, and disaster-recovery times. Plus, the flexible data structures of MongoDB's document data model allows for faster development and innovation. In another example , a popular convenience store chain reported 99.995% uptime, freeing up its engineers and allowing them to focus on building innovative solutions thanks to Atlas Device Sync . Working with MongoDB helped functional food company MuscleChef transition from a food and beverage business with a website to a data-driven company that leverages customer insights to continuously improve and scale user experience, new product development, operations and logistics, marketing, and communications. Since working with MongoDB, repeat customer orders have surged 49%, purchase frequency saw a double-digit increase, and average order value is 50% higher than its largest competitors. Thousands of customers have been successful running MongoDB Atlas on the robust infrastructure offered by AWS. No-code enterprise application development platform Unqork helps businesses build apps rapidly without writing a line of code. Using MongoDB Atlas, the platform ingests data from multiple sources at scale and pushes it to applications and third-party services. Volvo Connect enables drivers and fleet managers to track trucks, activities, and even insights using a single administrative portal. The versatility and performance of Atlas combined with the AWS global cloud infrastructure helps the business connect critical aspects of their business in completely new ways. Verizon also opted to run Atlas on AWS to unlock the full power of its 5G mobile technology by moving compute elements to the network edge, making the user experience faster. A unified approach to data handling The Atlas developer data platform integrates all of the data services you need to build modern applications that are highly available, performant at global scale, and compliant with the most demanding security and privacy standards within a unified developer experience. With MongoDB Atlas running on AWS Global Cloud Infrastructure, organizations can leverage a single platform to store, manage, and process data at scale, allowing them to concentrate on building intelligent applications and driving business value. Atlas handles transactional data, app-driven analytics, full-text search, generative AI and vector search workloads, stream data processing, and more, all while reducing data infrastructure sprawl and complexity. MongoDB Atlas is available in 27 AWS regions. This allows organizations to deliver fast and consistent user experiences in any region and replicate data across multiple regions to reach end-users globally with high performance and low latency. Additionally, the ability to store data in specific zones ensures compliance with data sovereignty requirements. Security is paramount for both MongoDB Atlas and AWS. MongoDB Atlas is secure by default. It leverages built-in security features across your entire deployment. Atlas helps organizations comply with FedRAMP certification and regulations such as HIPAA, GDPR, PCI DSS, and more. It offers robust security measures , like our groundbreaking queryable encryption, which enables developers to run expressive queries on the encrypted data. MongoDB Atlas also enhances developer productivity with its fully managed developer data platform on AWS. It offers a unified interface/API for all data and application services, seamlessly integrating into development and deployment workflows. MongoDB Atlas also integrates with Amazon CodeWhisperer . This powerful combination accelerates developer innovation for a seamless coding experience, improved efficiency, and exceptional business growth. Conclusion MongoDB Atlas and AWS have worked together for almost a decade to offer a powerful solution for organizations looking to innovate and build intelligent applications. By simplifying data management, enhancing security, and providing a unified developer experience, they ensure that organizations can focus on what truly matters: driving innovation and delivering exceptional user experiences. If you're ready to get started, MongoDB Atlas is available in the AWS Marketplace, and you have the option to start with a free tier. Get started with MongoDB Atlas on AWS today .
Why We’re Celebrating Developers (And Why We Always Will)
No matter the future, developers will build it. This is the fundamental message for MongoDB’s Love Your Developers campaign, which we created to celebrate, recognize, and support all that developers have done and continue to do. When the campaign launched earlier this year, it took a more historical view on how developers have shaped the history of computing from its earliest days — even before computers were invented. We wanted to show that — even in the eras when hardware was the primary focus — programming language pioneers, like Grace Hopper, and innovative engineers, like Jerry Lawson, fundamentally shaped the future through software. (To read more about the campaign, and why we launched it, check out our announcement blog article .) Introducing the MongoDB Developer Spotlight Series Just like in the past, today’s technologies aren’t developed in a vacuum. It takes entire teams of developers — experimenting, innovating, testing — to build and deploy applications that transform how we interact with the world. That is why we’re introducing our MongoDB Developer Spotlight Series. Our aim is to celebrate the diversity of experiences and approaches that exist among and throughout the global developer community. To do this, we will highlight stories from today’s community champions, developer advocates, newbies, senior engineers — and more. It’s our hope that these stories will help inspire the next generation of developers who will further shape our future. In this new series, we’ll introduce you to developers like Trina Yau. Trina went from being a pharmacist to a software engineer at Cisco. While initially concerned about her nontraditional background, she was later told by recruiters that her unique journey was a strength, rather than a weakness. This revelation showed her that, in order to become a developer, one must only possess a curious mind. In other words, there is no singular path to working in software. In a forthcoming post, Trina (who is also a MongoDB Community Creator) will share with us why developing isn’t just a way to earn a living, but a way to connect with people and share experiences through technology. This series will also spotlight developers like Justin Jenkins, who is a Software Development Engineer at Pushpay and a MongoDB Community Creator. He got his start at an early age, building his own computers and coding in BASIC - before he even knew that developing was a thing. In a future post, Justin will share tips based on his own experiences. For example, in his view, newbie developers know more than they think when it comes to getting started. Through MongoDB’s social channels, we’ll share all of these developers’ insights, tips, advice, and origin stories. So stay tuned. And let us know what you think. #LoveYourDevelopers Because no matter what the future holds, it’s developers who will build it. If you’d like to learn how you can get involved in MongoDB’s own Developer Community, head on over to our Community Forum . There, you’ll find updates, announcements, and ways to interact with other developers.
MongoDB Atlas for Telecommunications Launches in Dallas, Alongside AT&T and Cisco
MongoDB has officially launched MongoDB Atlas for Telecommunications, a new program for telecommunications companies to accelerate innovation and maximize the use of their data to better serve customers. The program includes expert-led innovation workshops, tailored technology partnerships, and industry-specific knowledge accelerators that provide customized training paths designed for modernization and innovative digital services leveraging the potential of modern 5G networks. Visit the MongoDB Atlas for Industries page to learn more. MongoDB Atlas for Telecommunications launched against a backdrop of hundreds of developers and industry IT leaders at our MongoDB.local gathering in Dallas. Throughout the event, speakers and product announcements emphasized how the telecommunications industry is currently utilizing MongoDB Atlas to gain a competitive advantage. Atlas for the Edge 5G has opened up new revenue streams for Communication Service Providers (CSPs), with whole new industries racing to take advantage of the up to 100x improvements in speed and throughput that these new networks offer. However, while these networks offer significant speed enhancements, the underlying cloud computing environments, without any optimization, remain the primary bottleneck to delivering the sorts of data processing capabilities that these new, ultra-low latency applications demand. Atlas for the Edge addresses this issue by providing reliable data connectivity across the cloud, data centers, and devices, catering to critical real-time use cases like machine learning, disaster recovery, and autonomous vehicles. MongoDB's Atlas developer data platform ensures consistent data management, irrespective of the data's origin, storage location, or destination. This solution not only offers the ability to deploy MongoDB at any edge location, thereby enhancing performance and cost efficiency but also unifies data across various sources, ensuring a singular, dependable data source. Furthermore, with Atlas Stream Processing , real-time data processing from numerous devices, including sensors and mobile phones, is made possible. This allows for functions like anomaly detection and predictive maintenance on data sets. In terms of security, data encryption is ensured at all stages, and users can also benefit from advanced access controls or integrate with external identity management solutions. You can learn more about how MongoDB and Verizon are building the next generation of mobile 5G networks on our Mobile Edge Computing page. Atlas Stream Processing Atlas Stream Processing is especially beneficial for CSPs as it aids in real-time network performance analysis, ensuring quicker responses and enhancing customer experiences. With the ability to quickly detect anomalies, CSPs can maintain consistent network performance. Moreover, with data security as a priority in today's digital landscape, Atlas Stream Processing encrypts data both in transit and at rest, ensuring the safe processing of CSP data streams. For CSPs, being able to adapt and react in real time is essential. By leveraging Atlas Stream Processing, they can optimize operations, offer improved services, and make data-driven decisions promptly, ensuring they remain competitive in the ever-evolving industry. Vector Search Atlas Vector Search , a new addition to the MongoDB Atlas product line, enables CSPs to build intelligent applications powered by semantic search and generative AI over unstructured data, giving customers results that go beyond keyword matching and that infer meaning and intent from a user’s search term. By employing Atlas Vector Search, CSPs can quickly sift through vast datasets, including customer profiles, service logs, or network patterns, to find relevant insights. Such capabilities can enhance customer service, as CSPs can more readily understand user behavior and preferences, helping to uncover new opportunities to address customer needs. Additionally, as CSPs diversify their offerings and integrate more with digital services, the ability to conduct nuanced searches becomes essential for product innovation and market differentiation. Learn more about Atlas Vector Search on our product page . Attendees of MongoDB.local Dallas were also treated to talks by Luke Rice, Director of Technology at AT&T, and Shaun Roberts, Principal Engineer at Cisco, both of whom outlined how they were utilizing MongoDB Atlas to transform how they do business. Going on a modernization journey with AT&T Rice presented an overview of AT&T's platform for address management, validation, and qualification. This platform is crucial for the accurate deployment of products and services to customers, determining service eligibility based on location, and handling address inconsistencies from various sources. It supports both geocoding and reverse geocoding, translating addresses to geospatial coordinates and vice versa. It interacts with third-party address data providers and has the ability to match and merge inconsistent address data. The system's importance extends beyond mere validation and service qualification, heavily influencing much of AT&T's product and service lifecycle. From planning where to build out infrastructure, to working with construction and engineering during builds, and setting up services in areas where addresses are yet to be finalized, the system plays a pivotal role. Additionally, it aids in sales, order provisioning, billing, and dispatching for service management, handling around 380 million unique addresses and managing around 14 million daily transactions. Currently, AT&T’s address management system consists of several aging and sometimes overlapping systems, creating issues with maintenance and efficiency. AT&T is on a modernization journey to integrate approximately 12 of these systems into a singular solution, the Intelligent Network Location Application Platform (IN-LAP). At the center of IN-LAP is MongoDB Atlas. The primary benefit of MongoDB Atlas to AT&T is its simplified and flexible data structure, which helps them create their single source of truth. And with MongoDB’s flexible schema, AT&T is ready to continuously adapt digital products to new data demands and technologies, like edge computing and AI, without extensive and time-consuming database redesign. MongoDB Atlas also offers AT&T benefits from reduced data duplication, multiple data ingestion options, and built-in geospatial functions, allowing them to perform calculations like point-to-point distances without third-party tools. We do require our Solutions like [IN-LAP] to be multi-region, and so that [multi-cloud] being built into the Atlas platform enables me to just focus on building value. Luke Rice, Director of Technology at AT&T Ultimately, accelerating time to market for new products is key for Rice and AT&T. MongoDB Atlas offers AT&T a level of "platformization" that allows developers to focus solely on delivering business value, relieving them of the operational intricacies and management responsibilities of running a large and mission-critical database. Lastly, MongoDB’s native multi-region, multi-cloud support gives Rice and his team reassurance that they can easily scale IN-LAP to different regions and countries in the future. How Cisco empowers its workforce to build a better customer experience Roberts shared how Cisco’s Customer Experience (CX) team leverages MongoDB in the company’s groundbreaking Ascension product. Ascension is a platform that empowers people from across the company, including those with little to no coding knowledge, to develop customer-focused innovations using a cloud-native, low-to-no-code solution. Shaun and his CX team had specific requirements for Ascension. They wanted: A scalable, production-grade system. To engage a broader spectrum of their development base, including those not traditionally involved in coding. A cutting-edge, compliant solution upholding Cisco's stringent security standards. A solution that could be managed by a compact, volunteer-based tiger team. Initially, they experimented with a range of on-prem solutions, including OpenStack, VMware, Arango, and Microsoft SQL. Unfortunately, none met their criteria, particularly when it came to scaling the usage of Ascension. This was especially true for their core application, the Cisco Virtual Tac Engineer, which manages tens of thousands of cases daily. Ascension cut development time by 40% to 60% compared with traditional methods. In the end, to meet their ambitious goals and stringent requirements, Cisco built its solution using MongoDB Atlas. MongoDB Atlas now serves as the primary database for Ascension and also operates as the de facto database-as-a-service (DBaaS) for client apps built using Ascension, with 98% of all applications built by Ascension users running on MongoDB Atlas. With MongoDB at its core, Ascension helps power some of the most exciting innovations across Cisco Customer Experience, helping the company bring amazing efficiency and service to its customers. The results speak for themselves: Ascension cut development time by 40% to 60% compared with traditional methods. Over a year, Ascension user numbers surged from 20 to over 900. Ascension processes 450,000 to 500,000 workflows daily, amounting to about 12 million monthly. Ascension uptime stands at an impressive 99.9%, with the minor downtime attributed to upgrades. On average, an engineer at Cisco dedicating time to Ascension spends 12 hours a week on it. Learn more about how to take advantage of the MongoDB Atlas for Telecommunications program on our MongoDB for Industries page.
Atlas Vector Search Commands Highest Developer NPS in Retool State of AI 2023 Survey
Retool has just published its first-ever State of AI report and it's well worth a read. Modeled on its massively popular State of Internal Tools report, the State of AI survey took the pulse of over 1,500 tech folks spanning software engineering, leadership, product managers, designers, and more drawn from a variety of industries. The survey’s purpose is to understand how these tech folk use and build with artificial intelligence (AI). As a part of the survey, Retool dug into which tools were popular, including the vector databases used most frequently with AI. The survey found MongoDB Atlas Vector Search commanded the highest Net Promoter Score (NPS) and was the second most widely used vector database - within just five months of its release. This places it ahead of competing solutions that have been around for years. In this blog post, we’ll examine the phenomenal rise of vector databases and how developers are using solutions like Atlas Vector Search to build AI-powered applications. We’ll also cover other key highlights from the Retool report. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Vector database adoption: Off the charts (well almost...) From mathematical curiosity to the superpower behind generative AI and LLMs, vector embeddings and the databases that manage them have come a long way in a very short time. Check out DB-Engines trends in database models over the past 12 months and you'll see that vector databases are head and shoulders above all others in popularity change. Just look at the pink line’s "up and to the right" trajectory in the chart below. Screenshot courtesy of DB-engines, November 8, 2023 But why have vector databases become so popular? They are a key component in a new architectural pattern called retrieval-augmented generation — otherwise known as RAG — a potent mix that combines the reasoning capabilities of pre-trained, general-purpose LLMs and feeds them real-time, company-specific data. The results are AI-powered apps that uniquely serve the business — whether that’s creating new products, reimagining customer experiences, or driving internal productivity and efficiency to unprecedented heights. Vector embeddings are one of the fundamental components required to unlock the power of RAG. Vector embedding models encode enterprise data, no matter whether it is text, code, video, images, audio streams, or tables, as vectors. Those vectors are then stored, indexed, and queried in a vector database or vector search engine, providing the relevant input data as context to the chosen LLM. The result are AI apps grounded in enterprise data and knowledge that is relevant to the business, accurate, trustworthy, and up-to-date. As the Retool survey shows, the vector database landscape is still largely greenfield. Fewer than 20% of respondents are using vector databases today, but with the growing trend towards customizing models and AI infrastructure, adoption is guaranteed to grow. Why are developers adopting Atlas Vector Search? Retool's State of AI survey features some great vector databases that have blazed a trail over the past couple of years, especially in applications requiring context-aware semantic search. Think product catalogs or content discovery. However, the challenge developers face in using those vector databases is that they have to integrate them alongside other databases in their application’s tech stack. Every additional database layer in the application tech stack adds yet another source of complexity, latency, and operational overhead. This means they have another database to procure, learn, integrate (for development, testing, and production), secure and certify, scale, monitor, and back up, And this is all while keeping data in sync across these multiple systems. MongoDB takes a different approach that avoids these challenges entirely: Developers store and search native vector embeddings in the same system they use as their operational database. Using MongoDB’s distributed architecture, they can isolate these different workloads while keeping the data fully synchronized. Search Nodes provide dedicated compute and workload isolation that is vital for memory-intensive vector search workloads, thereby enabling improved performance and higher availability With MongoDB’s flexible and dynamic document schema, developers can model and evolve relationships between vectors, metadata, and application data in ways other databases cannot. They can process and filter vector and operational data in any way the application needs with an expressive query API and drivers that support all of the most popular programming languages. Using the fully managed MongoDB Atlas developer data platform empowers developers to achieve the scale, security, and performance that their application users expect. What does this unified approach mean for developers? Faster development cycles, higher performing apps providing lower latency with fresher data, coupled with lower operational overhead and cost. Outcomes that are reflected in MongoDB’s best-in-class NPS score. Atlas Vector Search is robust, cost-effective, and blazingly fast! Saravana Kumar, CEO, Kovai discussing the development of his company’s AI assistant Check out our Building AI with MongoDB blog series (head to the Getting Started section to see the back issues). Here you'll see Atlas Vector Search used for GenAI-powered applications spanning conversational AI with chatbots and voicebots, co-pilots, threat intelligence and cybersecurity, contract management, question-answering, healthcare compliance and treatment assistants, content discovery and monetization, and more. MongoDB was already storing metadata about artifacts in our system. With the introduction of Atlas Vector Search, we now have a comprehensive vector-metadata database that’s been battle-tested over a decade and that solves our dense retrieval needs. No need to deploy a new database we'd have to manage and learn. Our vectors and artifact metadata can be stored right next to each other. Pierce Lamb, Senior Software Engineer on the Data and Machine Learning team at VISO TRUST What can you learn about the state of AI from the Retool report? Beyond uncovering the most popular vector databases, the survey covers AI from a range of perspectives. It starts by exploring respondents' perceptions of AI. (Unsurprisingly, the C-suite is more bullish than individual contributors.) It then explores investment priorities, AI’s impact on future job prospects, and how it will likely affect developers and the skills they need in the future. The survey then explores the level of AI adoption and maturity. Over 75% of survey respondents say their companies are making efforts to get started with AI, with around half saying these were still early projects, and mainly geared towards internal applications. The survey goes on to examine what those applications are, and how useful the respondents think they are to the business. It finds that almost everyone’s using AI at work, whether they are allowed to or not, and then identifies the top pain points. It's no surprise that model accuracy, security, and hallucinations top that list. The survey concludes by exploring the top models in use. Again no surprise that Open AI’s offerings are leading the way, but it also indicates growing intent to use open source models along with AI infrastructure and tools for customization in the future. You can dig into all of the survey details by reading the report . Getting started with Atlas Vector Search Eager to take a look at our Vector Search offering? Head over to our Atlas Vector Search product page . There you will find links to tutorials, documentation, and key AI ecosystem integrations so you can dive straight into building your own GenAI-powered apps . If you want to learn more about the high level possibilities of Vector Search, then download our Embedding Generative AI whitepaper.