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.

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.