Building Gen AI with MongoDB & AI Partners | July 2024

Gregory Maxson
August 7, 2024 | Updated: August 9, 2024
#Partners#genAI

My colleague Richmond Alake recently published an article about the evolution of the AI stack that breaks down the “comprehensive collection of integrated tools, solutions, and components designed to streamline the development and management of AI applications.”

It’s a good read, and Richmond—who’s an AI/ML expert and developer advocate—explains clearly how the modern AI stack evolved from a set of disparate tools to the (beautifully) interdependent ecosystem on which AI development relies today. “The modern AI stack represents an evolution from the fragmented tooling landscape of traditional machine learning to a more cohesive and specialized ecosystem optimized for the era of LLMs and gen AI,” Richmond writes.

In other words, this cohesive ecosystem is aimed at ensuring end-to-end interoperability and seamless developer experiences, both of which are of utmost importance when it comes to AI innovation (and software innovation overall).

Empowering developer innovation is exactly what MongoDB is all about—from streamlining how developers build modern applications, to the blog post you’re reading now, to the news that the MongoDB AI Applications Program (MAAP) is now generally available. In particular, the MAAP ecosystem represents leaders from every part of the AI stack who will provide customer service and support, and who will work with them to ensure smooth integrations—with the ultimate aim of helping them build gen AI applications with confidence. As the saying goes, it takes a village.

Welcoming new AI partners

Because the AI ecosystem is constantly evolving, we're always working to ensure that customers can seamlessly integrate with the latest cohort of industry-leading companies. In July we welcomed nine new AI partners that offer product integrations with MongoDB. Read on to learn more about each great new partner!

Enkrypt AI

Enkrypt AI secures enterprises against generative AI risks with its comprehensive security platform that detects threats, removes vulnerabilities, and monitors performance for continuous insights. The solution enables organizations to accelerate AI adoption while managing risk and minimizing brand damage.

Sahil Agarwal, CEO of Enkrypt AI said, “We are thrilled to announce our strategic partnership with MongoDB, to help companies secure their RAG workflows for faster production deployment. Together, Enkrypt AI and MongoDB are dedicated to delivering unparalleled safety and performance, ensuring that companies can leverage AI technologies with confidence and improved trust.”

FriendliAI

FriendliAI’s mission is to empower organizations to harness the full potential of their generative AI models with ease and cost efficiency. By eliminating the complexities of generative AI serving, FriendliAI aims to empower more companies to achieve innovation with generative AI.

“We’re excited to partner with MongoDB to empower companies in testing and optimizing their RAG features for faster production deployment,” said Byung-Gon Chon, CEO and co-founder of FriendliAI. “MongoDB simplifies the launch of a scalable vector database with operational data. Our collaboration streamlines the entire RAG development lifecycle, accelerating time to market and enabling companies to deliver real value to their customers more swiftly.”

HoneyHive

HoneyHive helps organizations continuously debug, evaluate, and monitor AI applications, and ship new AI features faster and with confidence.

"We’re thrilled to announce our partnership with MongoDB, which addresses a critical challenge in GenAI deployment—the gap between prototyping and production-ready RAG systems,” said Mohak Sharma, CEO of HoneyHive. “By integrating HoneyHive's evaluation and monitoring capabilities with MongoDB's robust vector database, we're enabling developers to build, test, and deploy RAG applications with greater confidence. This collaboration provides the necessary tools for continuous quality assurance, from development through to production. For companies aiming to leverage gen AI responsibly and at scale, our combined solution offers a pragmatic path to faster, more reliable deployment."

Iguazio

The Iguazio AI platform operationalizes and de-risks ML & gen AI applications at scale so organizations can implement AI effectively and responsibly in live business environments.

“We're delighted to expand our partnership with MongoDB into the gen AI domain, jointly helping enterprises build, deploy and manage gen AI applications in live business environments with our gen AI Factory,” said Asaf Somekh, co-founder and CEO of Iguazio (acquired by McKinsey). “Together, we mitigate the challenges of scaling gen AI and minimizing risk with built-in guardrails. Our seamlessly integrated technologies enable enterprises to realize the potential of gen AI and turn their AI strategy into real business impact."

Netlify

Netlify is the essential platform for the delivery of exceptional and dynamic web experiences, without limitations. The Netlify Composable Web Platform simplifies content orchestration, streamlines and unifies developer workflow, and enables website speed and agility for enterprise teams.

"Netlify is excited to join forces with MongoDB to help companies test and optimize their RAG features for faster production deployment,” said Dana Lawson, Chief Technical Officer at Netlify. “MongoDB has made it easy to launch a scalable vector database with operational data, while Netlify enhances the deployment process and speed to production. Our collaboration streamlines the development lifecycle of RAG applications, decreasing time to market and helping companies deliver real value to customers faster."

Render

Render helps software teams ship products fast and at any scale. The company hosts applications for customers that range from solopreneurs, small agencies, and early stage startups, to mature, scaling businesses with services deployed around the world, all with a relentless commitment to reliability and uptime.

Jess Lin, Developer Advocate at Render, said, “We’re thrilled to join forces with MongoDB to help companies effortlessly deploy and scale their applications—from their first user to their billionth. Render and MongoDB Atlas both empower engineers to focus on developing their products, not their infrastructure. Together, we're streamlining how engineers build full-stack apps, which notably include new AI applications that use RAG.”

Superlinked

Superlinked is a compute framework that helps MongoDB Atlas Vector Search work at the level of documents, rather than individual properties, enabling MongoDB customers to build high-quality RAG, Search, and Recommender systems with ease.

“We're thrilled to join forces with MongoDB to help companies build vector search solutions for complex datasets,” said Daniel Svonava, CEO of Superlinked. “MongoDB makes it simple to manage operational data and a scalable vector index in one place. Our collaboration brings the operational data into the vector embeddings themselves, making the joint system able to answer multi-faceted queries like “largest clients with exposure to manufacturing risk” and operate the full vector search development cycle, speeding up time to market and helping companies get real value to customers faster."

Twelve Labs

Twelve Labs builds AI that perceives the world the way humans do. The company models the world by shipping next-generation multimodal foundation models that push the boundaries in video understanding.

"We are excited to partner with MongoDB to enable developers and enterprises to build advanced multimodal video understanding applications,” said Jae Lee, CEO of Twelve Labs. “Developers can store Twelve Labs' state-of-the-art video embeddings in MongoDB Atlas Vector Search for efficient semantic video retrieval—which enables video recommendations, data curation, RAG workflows, and more. Our collaboration supports native video processing and ensures high-performance & low latency for large-scale video datasets."

Upstage

Upstage specializes in delivering above-human-grade performance AI solutions for enterprises, focusing on superior usability, customizability, and data privacy.

“We are thrilled to partner with MongoDB to provide our enterprise customers with a powerful full-stack LLM solution featuring RAG capabilities,” said Sung Kim, CEO and co-founder of Upstage. “By combining Upstage AI's Document AI, Solar LLM, and embedding models with the robust vector database MongoDB Atlas, developers can create a powerful end-to-end RAG application that's grounded with the enterprise's unstructured data. This application achieves a fast time to value with productivity gains while minimizing the risk of hallucination.”

But wait, there's more!

To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub, and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.

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How MongoDB Scales CoPilot AI’s Humanized Sales Interactions

In a world where sales and marketing are the engines behind many tech companies’ growth in a highly competitive landscape, it’s more important than ever that those functions find better and fresher ways to implement personalization into campaigns, sales pitches, and everything in between to reach more customers. CoPilot AI has been at the helm of helping businesses do just that through their AI-powered sales enablement tool, automating personalized interactions to achieve revenue growth, all with the help of MongoDB . CoPilot AI is a software company that helps businesses leverage AI to personalize and automate sales outreach. “We’re looking to humanize digital interactions in a scalable way. That’s our mission, our ethos behind our entire business, which can sometimes seem counterintuitive to people when you think of ‘AI’ and ‘humanize’,” said Scott Morgan, Head of Product Marketing at CoPilot AI. They integrate with platforms that have a high-quality lead base or verified accounts to identify qualified leads and facilitate communication through features like smart replies and sentiment analysis. Today, they predominantly work with LinkedIn as a channel, tapping into the 1B+ professionals globally who interact and conduct business online. “We envision ourselves as having an AI suite of assisting tools that allow business professionals and companies to support their entire sales journey with AI tooling,” said Morgan. CoPilot AI uses five AI pieces (sentiment analysis, reply prediction, smart reply, Meetings Booked AI, and personalized insights) to qualify leads within its lead management platform. Reply prediction gives predictions on which leads are most likely to book meetings with you before you connect with them, while sentiment analysis analyzes replies from leads to determine if they’re interested in continuing the conversation. These features prioritize high-quality leads, boosting success rates. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. The challenge: Scaling AI-powered sales interactions CoPilot AI leverages Random Forest and Chat GPT-3.5 turbo for production and Chat GPT-4 for producing labels and creating models. Their tech stack also includes AWS SageMaker and Azure. However, efficiently managing the data behind these interactions was crucial for scaling their platform. They needed a powerful database to hold and manage their massive system records. Also, they needed a scalable and cost-effective platform that could accommodate their data needs for their growing user bases. Enter, MongoDB Atlas. For most of its 10-year journey, CoPilot AI has been using MongoDB. When evaluating alternative cloud database solutions, CoPilot AI explored Microsoft’s Azure Cosmos DB. While Azure Cosmos DB offered a compelling feature set, its pricing structure didn’t align with CoPilot AI’s specific data access patterns, resulting in high costs. This led them to MongoDB for optimal cost-efficiency for their workload. Building a scalable foundation with MongoDB Atlas CoPilot AI has been using MongoDB since 2013 and started using MongoDB Atlas in 2020. “Everything! System of record, campaigns, message sequences, sending messages, all of that is in MongoDB,” says Takahiro Kato, Principal Engineer at CoPilot AI. CoPilot AI also uses Atlas Data Federation to access its customer information, leads, and campaign conversations. They set up data lake pipelines that go into Data Federation, where their ML engineers pull the data from. They also use Online Archive quite extensively. As a fast-growing startup, CoPilot AI was also able to take advantage of the MongoDB for Startups program , giving them access to free credits and expert technical advice to optimize their usage. “Access to the consultant was quite useful as well. We received advice on how to improve query efficiency, something we’ve been struggling with for a while. In the past, the cost was quite high, our queries were inefficient. As we were going through and fixing those issues, the advice helped,” says Kato. MongoDB empowered CoPilot AI with streamlined development through an intuitive driver and data flexibility via its schema-less design, enabling developers to focus on core functionalities while effortlessly adapting the data model for business growth. CoPilot AI continues to use MongoDB Atlas for multiple reasons, some of which include: Speed and Performance: MongoDB's fast read/write capabilities ensure smooth operation for CoPilot AI's data-intensive operations. Developer Productivity: The C# driver with LINQ support simplifies data access for CoPilot AI's .NET backend, boosting development efficiency. Scalability: MongoDB's flexible schema easily accommodates CoPilot AI's evolving data needs as its user base grows. Cost Optimization: Compared to alternatives, MongoDB offered a more cost-effective option for CoPilot AI's data storage needs. Plus, the MongoDB for Atlas Startups Program provided valuable credits and expert guidance to optimize queries and reduce costs. Key takeaways for developers and businesses MongoDB Atlas securely unifies operational, unstructured, and AI-related data to streamline building AI-enriched applications. Considering leveraging AI in your business? Look no further than MongoDB as your database management solution. If you want to learn more about how you can get started with your next AI project or take your AI capabilities to the next level, you can check out our MongoDB for Artificial Intelligence resources page for the latest best practices that get you started in turning your idea into an AI-driven reality. Also, stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem. Head over to our quick-start guide to get started with Atlas Vector Search today. Add vector search to your arsenal for more accurate and cost-efficient RAG applications by enrolling in the MongoDB and DeepLearning.AI course " Prompt Compression and Query Optimization " for free today.

August 7, 2024
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Boosting Customer Lifetime Value with Agmeta and MongoDB

Nobody likes calling customer service. The phone trees, the wait times, the janky music, and how often your issue just isn’t resolved can make the whole process one most people would rather avoid. For business owners, the customer contact center can also be a source of frustration, simultaneously creating customer churn and unhappiness, while also acting as a black hole of information as to why that churn occurred. It doesn’t have to be this way. What if instead, customer service centers offered valuable ways to increase the Customer Lifetime Value (CLTV) of customers, pipelines of upsell opportunities, and valuable sources of information? That’s the goal of Agmeta.AI , a startup dedicated to giving businesses actionable insights to fight churn, identify key customers primed for upsell, and improve customer service overall. Lost in translation “We started with a very simple thesis±people call into contact centers because they have a problem. That is a real make-or-break moment. The opportunity for churn is very high… or that customer can be a great target for upselling,” said Samir Agarwal, CEO and co-founder of Agmeta. “All of this data sits in a contact center, and businesses don't ever get to see it,” he added. According to Samir, even the businesses that think they are collecting useful information on customer service interactions are instead collecting incorrect or incomplete information. Or worse, they’re analyzing the information they do record incorrectly. Every business today talks about the importance of customer experience (CX), but the challenge businesses face is how they quantify that CX. Many contact centers substitute call sentiment for CX, or use keywords to determine canned responses. For example, imagine if a customer calls into a service center and they have what appears to be a positive conversation with an agent. They use words and phrases like “thank you,” and “yes, I understand,” and reply “no, I do not have anything else to ask” at the end of a call in which their complaint is not resolved. After putting the phone down, the customer goes on to cancel the service, or worse, initiate a chargeback request with their credit card provider. In some businesses the customer service agent may manually mark such a call as positive’ The agent, after all, ‘answered all the customers' concerns.’ As this example illustrates, the sentiment of a call should not be confused with the measure of customer experience. Another common way businesses try to gather feedback is by sending a post-call survey. However, a problem with this approach is that industry response rates for surveys are close to 3%. This implies that decisions get made on that small sample, and may not take into account the other 97% of the customers who didn’t respond to the survey. Survey results are also frequently skewed, as those most likely to respond are also the ones who were most unhappy with the contact center interaction and want their voices heard. The MongoDB advantage Using machine learning and generative AI, backed by MongoDB Atlas , Agmeta’s software understands not only the content of the call, but the context too. Taking our example above, Agmeta’s software would detect that the customer is unhappy, despite their polite and ‘positive’ sounding conversation with the agent, and flag the customer as a potential churn or chargeback candidate in need of immediate attention. “We will give you a CSAT (customer satisfaction) score and a reason for that CSAT score within seconds of the call ending±for 100% of the interactions,” said Samir. For Agmeta to work, Samir and his team had to have a database ready to accept all kinds of data, including voice recordings, unstructured text, and constantly evolving schema. “We didn’t have a fixed schema, we needed a database that was as flexible as Agmeta needed to be. I’ve known of MongoDB forever, so when I started to look at databases it seemed an obvious choice to me,” he said. The ability to quickly and easily work with vectorized data for gen AI was also crucial. “MongoDB provides vector search capabilities in an operational database. Rather than having to add a bolt on a vector database and figure out the ETL, MongoDB solved this issue for me in a single product. The way I look at it, if you do a good job on Vector search, then my life as an entrepreneur and software builder becomes much easier,” Samir said. After assessing database options and multiple LLMs, Samir and his team chose to pair MongoDB Atlas with Google Cloud, taking advantage of Gemini on Google’s generative AI platform. “With Atlas on Google Cloud, there are zero worries about database administration, maintenance, and availability. This frees us up to focus on creating business value,” Samir said. “Another benefit of using MongoDB is the flexibility to use the customer’s MongoDB setup which gives the customer the peace of mind from the perspective of security and privacy of their data.” Customer service first With the power of generative AI and MongoDB, Agmeta can deliver a CSAT score that measures the customers’ true takeaway from the call. The CSAT score is a multi-dimensional score that takes into account areas including resolution (as the customer sees it), politeness, the onus on the customer, and many other attributes. In the short term, the primary use for this technology is to detect and flag those customers at risk of churn, filing a charge dispute with their card provider, or potentially upselling, giving businesses an opportunity to “see” what they could never find out before. “When we talk to customers, the number one thing they are concerned about is customer churn. Right now they operate completely blind with no idea why people are leaving them,” said Samir. “One large telecoms customer Agmeta is in talks with had no idea where their churn was happening. But when we described being able to assign every customer a CSAT score, they were very excited,” he added. And it’s not just about preventing churn. Businesses can identify happy customers too, targeting them for upsell opportunities. “One of the things we do is spot patterns of unanswered questions from product support interactions,” Samir added. “When we see ‘Oh look, suddenly there are a lot more calls because of a release,’ then we can flag this to product teams as a must-fix issue.” The future of customer service Agmeta aims to amalgamate customer information with current and past experiences to provide businesses a more holistic±and nuanced—picture of their customers, and more precise next steps they can take. “What we want to do is look back in time and see what else happened with this customer,” Samir said. “The goal is to provide businesses with targeted directives to minimize churn and grow customer lifetime value.” Retrieval-augmented generation plays a key role in Agmeta’s vision. This also means an expanded role for both MongoDB’s vector database as the source of information against which semantic searches can be run, as well as Gemini for both analysis and presentation of the directives for the business. You can learn more about how innovators across the world are using MongoDB by reviewing our Building AI case studies . If your team is building AI apps, sign up for the AI Innovators Program . Successful companies get access to free Atlas credits and technical enablement, as well as connections into the broader AI ecosystem. Additionally, if your company is interested in being featured in a story like this, we'd love to hear from you! Reach out to us at ai_adopters@mongodb.com .

September 10, 2024