MongoDB Atlas AWS CloudFormation and CDK Integration Expansion
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.
MongoDB is Changing the Way We Hire Veterans: Learn How
One of our core company values at MongoDB is “Embrace the Power of Differences” which means that we commit to creating a culture of inclusivity with employees from different backgrounds and experiences. We value a diverse workforce as a way to broaden our perspectives, foster innovation, and enable competitive advantages. Our employee resource groups (ERGs) support our larger commitment to a diverse and inclusive community and empower our employees to create an internal network that they are passionate about. The co-leads for MongoDB’s Veterans ERG harnessed that passion, forming a collaborative partnership with our Talent Acquisition team to launch a new initiative. This initiative enables our recruiters to engage in more meaningful conversations with military veterans by providing insight into how different military ranks and disciplines build skills and experience that can translate into corporate roles. In addition, recruiters participate in a live discussion around resumes and how military experience can often lead to non-traditional career paths. The goal of this training is to empower recruiters to become advocates for veterans by better understanding how their military experience generates skills in areas that aren’t often highlighted through words on a resume. “Military veterans are an integral part of our employee population,” says Em Blankenberger, diversity recruiter at MongoDB. “Veterans offer a variety of skills, from technical to interpersonal, and are uniquely adaptable to many different environments. One goal of this initiative is to empower recruiters to have initial calls and offer advice to our veteran applicants regarding their resumes, interview skills, and verbiage used to help set them up for success throughout their career, whether that be with MongoDB or elsewhere.” A Veteran's Perspective Nathan Leniz, Senior Software Engineer & Veteran ERG Co-Lead I joined MongoDB in 2017 as an Education Engineer after 15 years in the United States Army. I was an Explosive Ordnance Disposal technician and gained the skills for deep technical research, structured experimentation, fast learning, leadership, project management, extensive planning, and handling adversity. Very little of my military career translates directly to the typical job descriptions I see at most tech companies. However, my experiences have taught me this: how to learn from failure, that the worth of a person can't be derived from arbitrary labels and categorizations, success as a team and organization is more profound than success as an individual, and that the ideas of even someone new to the field are worth listening to. These aren’t skills you’ll normally see listed on a job description or even written on a resume, but they are skills that recruiters can identify during conversations with candidates. In my post-military career, I've struggled with PTSD, imposter syndrome, and the normal vicissitudes of life. At MongoDB, I've found an organization with people who are beyond supportive, and I'm passionate about ensuring everyone has that chance. This is why I continue to advocate for other veterans, and it’s this passion that sparked the idea for a MongoDB recruiter training that would better enable our team to recruit veterans. I want to ensure veterans are better equipped to enter technical fields. MongoDB's Military Appreciation Program, along with our upcoming mentorship initiative and collaboration with Operation Code, aims to raise awareness among veterans and those transitioning to civilian life about the resources and opportunities available to them. Are you a veteran? Our Military Appreciation Program offers four dedicated learning paths to assist those who have served with education in data and tech. Enroll today! Ashley Heaps, Senior Manager, Global Billing & Veteran ERG Co-Lead After four years of active duty in the Army, I transitioned into corporate America and joined MongoDB as a Billing Supervisor in 2019. My role in the Army was 74D (also known as a Chemical, Nuclear, and Biological Specialist), which did not correlate to my corporate career. It wasn’t easy after being discharged from the Army. Many companies thought I didn’t have the experience for the roles I applied to, which made it difficult for me to be hired in a corporate setting. What I learned from my job search was that I did have experience, I just didn’t know how to translate it on my resume and align it to the roles I was applying for. Once someone finally took a chance on me, I knew I wanted to find a way to give back to other veterans. When this project presented itself I jumped at the opportunity. There’s still a long way to go, but this initiative is the start of improving the post-military job search for veterans everywhere. Conclusion At MongoDB, our core values are deep-rooted to our success. They are central to who we are as an organization, and we strive to ensure our employees connect with them on a personal level. Embodying the values “Build Together” and “Embrace the Power of Differences,” our Talent Acquisition team and Veterans ERG are achieving amazing things by connecting and leveraging their diversity of perspectives, skills, experiences, and backgrounds. Transform your career at MongoDB. View open roles on our careers site.
Building AI with MongoDB: Giving Your Apps a Voice
In previous posts in this series, we covered how generative AI and MongoDB are being used to unlock value from data of any modality and in supercharging communications . Put those topics together, and we can start to harness the most powerful communications medium (arguably!) of them all: Voice . Voice brings context, depth, and emotion in ways that text, images, and video alone simply cannot. Or as the ancient Chinese Proverb tells us, “The tongue can paint what the eyes can’t see.” The rise of voice technology has been a transformative journey that spans over a century, from the earliest days of radio and telephone communication to the cutting-edge realm of generative AI. It began with the invention of the telephone in the late 19th century, enabling voice conversations across distances. The evolution continued with the advent of radio broadcasting, allowing mass communication through spoken word and music. As technology advanced, mobile communications emerged, making voice calls accessible anytime, anywhere. Today, generative AI, powered by sophisticated machine learning (ML) models, has taken voice technology to unprecedented levels. The generation of human-like voices and text-to-speech capabilities are one example. Another is the ability to detect sentiment and create summaries from voice communications. These advances are revolutionizing how we interact with technology and information in the age of intelligent software. In this post, we feature three companies that are harnessing the power of voice with generative AI to build completely new classes of user experiences: Xoltar uses voice along with vision to improve engagement and outcomes for patients through clinical treatment and recovery. Cognigy puts voice at the heart of its conversational AI platform, integrating with back-office CRM, ERP, and ticketing systems for some of the world’s largest manufacturing, travel, utility, and ecommerce companies. Artificial Nerds enables any company to enrich its customer service with voice bots and autonomous agents. Let's learn more about the role voice plays in each of these very different applications. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. GenAI companion for patient engagement and better clinical outcomes XOLTAR is the first conversational AI platform designed for long-lasting patient engagement. XOLTAR’s hyper-personalized digital therapeutic app is led by Heather, XOLTAR’s live AI agent. Heather is able to conduct omni-channel interactions, including live video chats. The platform is able to use its multimodal architecture to better understand patients, get more data, increase engagement, create long-lasting relationships, and ultimately achieve real behavioral changes. Figure 1: About 50% of patients fail to stick to prescribed treatments. Through its app and platform, XOLTAR is working to change this, improving outcomes for both patients and practitioners. It provides physical and emotional well-being support through a course of treatment, adherence to medication regimes, monitoring post-treatment recovery, and collection of patient data from wearables for remote analysis and timely interventions. Powering XOLTAR is a sophisticated array of state-of-the-art machine learning models working across multiple modalities — voice and text, as well as vision for visual perception of micro-expressions and non-verbal communication. Fine-tuned LLMs coupled with custom multilingual models for real-time automatic speech recognition and various transformers are trained and deployed to create a truthful, grounded, and aligned free-guided conversation. XOLTAR’s models personalize each patient’s experience by retrieving data stored in MongoDB Atlas . Taking advantage of the flexible document model, XOLTAR developers store both structured data, such as patient details and sensor measurements from wearables, alongside unstructured data, such as video transcripts. This data provides both long-term memory for each patient as well as input for ongoing model training and tuning. MongoDB also powers XOLTAR’S event-driven data pipelines. Follow-on actions generated from patient interactions are persisted in MongoDB, with Atlas Triggers notifying downstream consuming applications so they can react in real-time to new treatment recommendations and regimes. Through its participation in the MongoDB AI Innovators program , XOLTAR’s development team receives access to free Atlas credits and expert technical support, helping them de-risk new feature development. How Cognigy built a leading conversational AI solution Cognigy delivers AI solutions that empower businesses to provide exceptional customer service that is instant, personalized, in any language, and on any channel. Its main product, Cognigy.AI, allows companies to create AI Agents, improving experiences through smart automation and natural language processing. This powerful solution is at the core of Cognigy's offerings, making it easy for businesses to develop and deploy intelligent voice and chatbots. Developing a conversational AI system poses challenges for any company. These solutions must effectively interact with diverse systems like CRMs, ERPs, and ticketing systems. This is where Cognigy introduces the concept of a centralized platform. This platform allows you to construct and deploy agents through an intuitive low-code user interface. Cognigy took a deliberate approach when constructing the platform, employing a composable architecture model, as depicted in Figure 1 below. To achieve this, it designed over 30 specialized microservices, adeptly orchestrated through Kubernetes. These microservices were strategically fortified with MongoDB's replica sets, spanning across three availability zones. In addition, sophisticated indexing and caching strategies were integrated to enhance query performance and expedite response times. Figure 2: Congnigy's composable architecture model platform MongoDB has been a driving force behind Cognigy's unprecedented flexibility and scalability and has been instrumental in bringing groundbreaking products like Cognigy.AI to life. Check out the Cognigy case study to learn more about their architecture and how they use MongoDB. The power of custom voice bots without the complexity of fine-tuning Founded in 2017, Artificial Nerds assembled a group of creative, passionate, and "nerdy" technologists focused on unlocking the benefits of AI for all businesses. Its aim was to liberate teams from repetitive work, freeing them up to spend more time building closer relationships with their clients. The result is a suite of AI-powered products that improve customer sales and service. These include multimodal bots for conversational AI via voice and chat along with intelligent hand-offs to human operators for live chat. These are all backed by no-code functions to integrate customer service actions with backend business processes and campaigns. Originally the company’s ML engineers fine-tuned GPT and BERT language models to customize its products for each one of its clients. This was a time-consuming and complex process. The maturation of vector search and tooling to enable Retrieval-Augmented Generation (RAG) has radically simplified the workflow, allowing Artificial Nerds to grow its business faster. Artificial Nerds started using MongoDB in 2019, taking advantage of its flexible schema to provide long-term memory and storage for richly structured conversation history, messages, and user data. When dealing with customers, it was important for users to be able to quickly browse and search this history. Adopting Atlas Search helped the company meet this need. With Atlas Search, developers were able to spin up a powerful full-text index right on top of their database collections to provide relevance-based search across their entire corpus of data. The integrated approach offered by MongoDB Atlas avoided the overhead of bolting on a separate search engine and creating an ETL mechanism to sync with the database. This eliminated the cognitive overhead of developing against, and operating, separate systems. The release of Atlas Vector Search unlocks those same benefits for vector embeddings. The company has replaced its previously separate standalone vector database with the integrated MongoDB Atlas solution. Not only has this improved the productivity of its developers, but it has also improved the customer experience by reducing latency 4x . Artificial Nerds is growing fast, with revenues expanding 8% every month. The company continues to push the boundaries of customer service by experimenting with new models including the Llama 2 LLM and multilingual sentence transformers hosted in Hugging Face. Being part of the MongoDB AI Innovators program helps Artificial Nerds stay abreast of all of the latest MongoDB product enhancements and provides the company with free Atlas credits to build new features. Getting started Check out our MongoDB for AI page to get access to all of the latest resources to help you build. We see developers increasingly adopting state-of-the-art multimodal models and MongoDB Atlas Vector Search to work with data formats that have previously been accessible only to those organizations with access to the very deepest data science resources. Check out some examples from our previous Building AI with MongoDB blog post series here: Building AI with MongoDB: first qualifiers includes AI at the network edge for computer vision and augmented reality, risk modeling for public safety, and predictive maintenance paired with Question-Answering generation for maritime operators. Building AI with MongoDB: compliance to copilots features AI in healthcare along with intelligent assistants that help product managers specify better products and sales teams compose emails that convert 2x higher. Building AI with MongoDB: unlocking value from multimodal data showcases open source libraries that transform unstructured data into a usable JSON format, entity extraction for contracts management, and making sense of “dark data” to build customer service apps. Building AI with MongoDB: Cultivating Trust with Data covers three key customer use cases of improving model explainability, securing generative AI outputs, and transforming cyber intelligence with the power of MongoDB. Building AI with MongoDB: Supercharging Three Communication Paradigms features developer tools that bring AI to existing enterprise data, conversational AI, and monetization of video streams and the metaverse. There is no better time to release your own inner voice and get building!