Building Gen AI with MongoDB & AI Partners | August 2024

Gregory Maxson
September 11, 2024 | Updated: September 19, 2024
#Partners#genAI

As the AI landscape continues to evolve, companies, industries, and developers seek tailored solutions to their unique challenges. Gone are the days when general-purpose AI models could be applied universally. Now, organizations are looking for industry-specific applications, verticalized AI solutions, and specialized tools to gain a competitive edge and best serve their customers. And as gen AI use cases have diversified—from healthcare diagnostics and autonomous driving, to personalized recommendations and creative content generation—so has the technology stack supporting them.

The complexity of building and deploying AI models has led to the rise of specialized AI frameworks and platforms that streamline workflows and optimize performance for specific use cases. In this context, having the right AI stack is essential for driving innovation. AI development is no longer just about choosing the best model but also about selecting the right tools, libraries, and infrastructure to support that model across the board.

All of which makes partnerships (and combining technical strengths) increasingly important to innovating with AI. Take, for example, our most recent integration with LangChain: the MongoDB-LangChain partnership exemplifies how having the right components in an AI stack allows teams to focus on innovating instead of managing infrastructure bottlenecks. By combining LangGraph with MongoDB’s vector search capabilities, developers can create more sophisticated, high-performing AI applications. This integration allows for the seamless development of agentic AI systems capable of generating actionable insights and delivering complex tasks.

To learn more about building powerful AI agents with LangGraph.js and MongoDB, plus our recent work making vector search even more versatile with custom LangChain Retrievers, check out our tutorial.

Welcoming new AI partners

MongoDB’s partnership with LangChain highlights the importance of building adaptable solutions that can grow and change as the needs of developers and customers grow and change.

Which is why MongoDB is always on the lookout for innovative partners and solutions—in August we welcomed five new AI partners that offer product integrations with MongoDB. Read on to learn more about each great new partner!


BuildShip

BuildShip is a low-code visual backend and workflow builder to instantly create APIs, scheduled tasks, backend cloud jobs, and automation, powered by AI.

"We at BuildShip are thrilled to partner with MongoDB to introduce an innovative low-code approach for rapidly building AI workflows and backend tasks in a visual and scalable manner,” said Harini Janakiraman, CEO of BuildShip.com. “MongoDB offers a comprehensive data stack for AI developers and organizations, enabling them to efficiently build scalable databases and access vector or hybrid search options for their products. Our collaboration provides customizable low-code templates that allow for easy integration of MongoDB databases with a variety of AI models and tools. This enables teams and companies to quickly build powerful APIs, automations, vector search, and scheduled tasks, unlocking organizational efficiency and driving product innovation.”


Inductor

Inductor is a platform to prototype, evaluate, improve, and observe LLM apps and features, helping developers ship high-quality LLM-powered functionality rapidly and systematically.

“We’re excited to partner with MongoDB to enable companies to rapidly create production-grade LLM applications, by combining MongoDB's powerful vector search with Inductor’s developer platform enabling streamlined, systematic workflows for developing RAG-based applications,” said Ariel Kleiner, CEO of Inductor. “While many LLM-powered demos have been created, few have successfully evolved into production-grade applications that deliver business wins. Together, Inductor and MongoDB enable enterprises to build impactful, needle-moving LLM applications, accelerating time to market and delivering real value to customers.”


Metabase

Metabase is the easy-to-use, open source Business Intelligence tool that lets everyone work with data, with or without SQL, for internal and customer-facing, embedded analytics.

"This partnership is an important step forward for NoSQL database analytics. By integrating Metabase with MongoDB, two popular open-source tools, we are making it easier for users to quickly get valuable insights from their MongoDB data,” explained Luiz Arakaki, Product Manager at Metabase. “Our goal is to create a better integration between the tools to offer more advanced features and stability, simplifying the use of NoSQL databases for advanced analytics.”


Shakudo

Shakudo is a comprehensive development platform that lets data professionals develop, run, and deploy data pipelines and applications in an all-in-one integrated environment.

“Shakudo is thrilled to be partnering with MongoDB to streamline the entire retrieval-augmented generation (RAG) development lifecycle. Together we help companies test and optimize their RAG features for faster PoC, and production deployment,” noted Yevgeniy Vahlis, CEO of Shakudo. “MongoDB has made it dead simple to launch a scalable vector database with operational data, and Shakudo brings industry leading AI tooling to that data. Our collaboration speeds up time to market and helps companies get real value to customers faster.”


VLM Run

VLM Run is a versatile API that enables accurate JSON extraction from any visual content such as images, videos, and documents, helping users to integrate visual AI to applications.

“VLM Run is excited to partner with MongoDB to help enterprises accurately extract structured insights from visual content such as images, videos and visual documents,” said Sudeep Pillai, Co-Founder and CEO of VLM Run. “Our combined solution will enable enterprises to turn their often-untapped unstructured visual content into actionable, queryable business intelligence.”

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.

Head over to our quick-start guide to get started with Atlas Vector Search today.

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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 . Head over to our quick-start guide to get started with Atlas Vector Search today.

September 10, 2024
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Hanabi Technologies Uses MongoDB to Power AI Assistant, Hana

For all the hype surrounding generative AI, cynics tend to view the few real-world implementations as little more than “fancy chatbots.” But for Abhinav Aggarwal, CEO of Hanabi Technologies, the idea of a generative AI-powered bot that is more than just an assistant was intriguing. “I’d been using ChatGPT since it launched,” said Aggarwal. “That got me thinking: How could we make a chatbot that was like a team member?” And with that concept, Hana was born. The problem with bots “Most generative AI chatbots do not act like people; they wait for a command and give a response,” said Aggarwal. “We wanted to create a human-like chatbot that would proactively help people based on what they wanted—automating reminders, for example, or fetching time zones from your calendar to correctly schedule meetings.” Hanabi’s flagship product, Hana, is an AI assistant designed to enhance team collaboration within Google Chat, working in concert with Google Workspace and its suite of products. “Our target customers are smaller companies of between 10 and 50 people. At this size you’re not going to build your own agent from scratch,” he said. Hana integrates with Google APIs to deliver a human-like assistant that chimes in with helpful interventions, such as automatically setting reminders and making sure meetings are booked in the right time zone for each participant. “Hana is designed to bring AI to smaller companies and help them collaborate in a space where they are already working—Google Workspace,” Aggarwal explained. The MongoDB Atlas solution For Hana to act like a member of the team, Hanabi needed to process massive amounts of data to support advanced features like retrieval-augmented generation (RAG) for better information retrieval across Google Docs and many other sources. And with a rapidly growing user base of over 600 organizations and 17,000+ installs, Hanabi also required a secure, scalable, and high-performing data storage solution. MongoDB Atlas provided a flexible document model, built-in vector database, and scalable cloud-based infrastructure, freeing Hanabi engineers to build new features for Hana rather than focusing on rote tasks like data extract, transform, and load processes or manual scaling and provisioning. Now, MongoDB Atlas handles a variety of responsibilities: Scalability and security: MongoDB Atlas’s auto-scaling and automatic backup features have enabled Hanabi to seamlessly grow its user base without the need for manual database management. RAG: MongoDB Atlas plays a critical role in Hana’s RAG functionality. The platform enables Hanabi to split Google Docs into small sections, create embeddings, and store these sections in Atlas’s vector database. Development Processes: According to Aggarwal, MongoDB’s flexibility in managing changing schemas has been essential to the company’s fast-paced development cycle. Data Visualization: Using MongoDB Atlas Charts has enabled Hanabi to create comprehensive dashboards for real-time data visualization. This has helped the team track usage, set reminders, and optimize performance without needing to build a manual dashboard. Impact and results With MongoDB Atlas, Hanabi can successfully scale Hana to meet the demands of its rapidly expanding user base. The integration is also enabling Hana to offer powerful features like automatic interactions with customers, advanced information retrieval from Google Docs, and manually added memory snippets, making it an essential tool for teams around the world. Next steps Hanabi plans to continue integrating more tools into Hana while expanding its reach to personal Gmail users. The company is also rolling out a new automatic-interaction feature, further enhancing Hana’s ability to proactively assist users without direct commands. MongoDB Atlas remains a key component of Hanabi’s stack, alongside Google Kubernetes Engine, NestJS, and LangChain, enabling Hanabi to focus on innovating to improve the customer experience. Tech Stack MongoDB Atlas Google Kubernetes Engine NestJS LangChain Are you building AI apps? Join the MongoDB AI Innovators Program today! Successful participants gain access to free MongoDB Atlas credits, technical enablement, and invaluable connections within the broader AI ecosystem. If your company is interested in being featured, we’d love to hear from you. Connect with us at ai_adopters@mongodb.com.

November 21, 2024