Bukola Ayeni

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.