Adam Hughes

3 results

Built with MongoDB: Leadgence

Leetal Gruper and Sergey Bahchissaraitsev worked together previously, but in 2019, they sat down to brainstorm a new direction. Leadgence was born out of their passion for data and business expertise and quickly grew, with Leetal as CEO and Sergey as CTO. With customers ranging from startups to Fortune 500 companies, they tripled their client base three times over in the last quarter. “The Leadgence platform delivers banks, financial services, fintech and insurance companies smart actionable data about SMBs,” said David Citron, Partner at Global Founders Capital, which invested in Leadgence and partners with MongoDB for Startups to support their portfolio companies. “With Leadgence, customers see through the cluttered SMB landscape using industry-specific tags and change and event-based triggers to tailor their outreach knowing who to call, when, and most importantly why they are calling! SMBs are in turn getting offers for services they need, when they need them, saving them time and money that is always scarce when growing a small or midsize business." In this edition of #BuiltWithMongoDB, we chat with Sergey about the evolution of Leadgence, his favorite MongoDB features, and constantly learning new lessons as a company co-founder. MongoDB: In your own words, what does Leadgence do, and how have your products evolved since you launched the company? Sergey: Leadgence grows revenue for enterprise companies that target small and mid-size businesses. We started out offering pure smart data to support sales and marketing for financial services. Initially, there was no user-facing platform. We had paying customers from day one which were getting smart actionable data. Then we launched our first application DataSeeker in December 2020 delivering event and change-based triggered actionable data intelligence. Somewhere in-between, we mapped the company’s future road map to include applications offering services to support the different needs of each of marketing, sales, growth and risk-assessment teams. MongoDB is probably going to back all of our future released applications. MongoDB: How did you decide to build with MongoDB? Sergey: It was pretty straightforward. First of all, MongoDB is the go-to database when you're talking about the back end for APIs. I was also already a bit familiar with it from working on a previous product. I guess the key point is that MongoDB Atlas makes things easy, so we started using the infrastructure as a code approach, and spinned it up with Terraform. Atlas was the key feature that drew us to MongoDB. Its document-native approach makes sense with the Node.js that we use. MongoDB: What has your experience been scaling with MongoDB? Sergey: We started pretty small, so at first we were just running trials, but we eventually had millions of documents in the database. As we’ve scaled and started building our applications, we also use it like an analytics database. We basically run online inquiries on it, where our users can explore our data and get instant results. The MongoDB for Startups program also has been really helpful. They’ve supported us a lot and have been a real partner in our growth. The consulting sessions they offered helped us finalize our analytic database approach. MongoDB: What is your favorite technical article or podcast? Sergey: Startups for the Rest of Us podcast! MongoDB: What are you currently learning? Sergey: I keep evolving in the entrepreneurial side of things. From a technical side, we keep facing challenges and solving them. Sometimes I’m specifically learning about new things from a data science, machine learning, or data processing perspective. Other times I’m learning about scaling the company and bringing people on board. I guess I’m going to keep learning about these things for a while! One big lesson I’ve learned is that when working on a problem, you should try to solve it in the simplest way possible. Complex solutions usually don’t work out in the end. So, if you solve something in a very simple way, it usually means that you understand what you’ve solved. You can make the greatest impact this way. MongoDB: How do you upskill and continue educating yourself? Sergey: I try to communicate with other professionals in entrepreneurship and technical spaces. Networking with individuals pursuing similar work helps me share perspectives and advice. It’s helpful to keep up these connections to understand what’s happening in the market, and what should be done or not done. Hearing others’ opinions about the market helps me understand the kind of direction Leadgence should be going, and what we need to pursue more deeply and analyze further. MongoDB: What’s been the most challenging thing about building Leadgence? Sergey: Building a business is in two words not-simple. We at Leadgence work with cutting-edge technology that is evolving rapidly, requiring us to always be on top of the latest developments. Add to that on-boarding new customers and the constant addition of new features and data requests, well I think you get the picture. Interested in learning more about MongoDB for Startups? Learn more about us here .

September 29, 2021

Built With MongoDB: Vectorly

When Sam Bhattacharyya spent time in the Peace Corps as a teacher in Mexico, he learned how much of a barrier the lack of internet bandwidth was for his classes. The students simply did not have the resources needed to take advantage of online learning, which was a problem Sam soon became fixated on. Years later, Sam founded his company Vectorly with a goal to fix that bandwidth issue via an AI-based video compression solution that streams low-resolution videos and turns them into a high-definition viewing experience. Vectorly is a software development kit (SDK) that companies can integrate into their video applications. Vectorly released its minimal viable product (MVP) for use by early customers in February 2021 and has a total of 20 companies that are actively using the product. In this edition of #BuiltWithMongoDB, we talked with Sam about how Vectorly’s software works, how he got started with MongoDB for Startups, and the future of this fast-growing industry. MongoDB: What's Vectoryl's mission? Sam: We’re building a technology that uses artificial intelligence (AI) to upscale and enhance video in real time on users’ devices, as they watch it. So, what that lets a user do is stream low-resolution video content and watch it in high definition. We have about 100 AI models on our server. Most of them are for AI upscaling, for different kinds of content and different quality levels. Based on feedback from customers, we've also been building AI filters for, say, virtual background replacement. All that data is loaded in real time from the server every time you load the library. With our SDK, you specify that you want to use this AI filter on that library, and you have an API token that calls our API and that returns the AI model in real time to your device so you can watch the upscaled video. AI takes some computing power, which can be a concern especially on low-end devices, and we’re conscious of that, so we pay close attention to performance and frame rate to make sure our AI models do not overload the devices users are working on. MongoDB: What are some of the use cases for Victory? Sam: The first is to think of a user that is watching Netflix with a slow internet connection. Because the network is so slow, that user’s going to end up with a low-resolution version of the video. But we have AI filters that can pop in and start to upscale and enhance the video and make it look as if it’s high definition. The other use case is around video conferencing, where all kinds of things can affect call quality or user experience, from background noise to blurry video. You can use AI to correct any of those issues that come up. MongoDB: What does your tech stack consist of? Sam: Our product is a software library, which is for the web, and it’s all built in JavaScript. The main JavaScript functionality we’re using is called WebGL, which is a graphics pipeline that lets you access the GPU on devices. We have a bunch of AI models on our server, which are just numbers stored in JSON files. Our SDKs load the AI models in real time, and we use MongoDB to track and store event data, as well as basic metadata. MongoDB: How did you choose MongoDB? Sam: I've been using MongoDB since I started programming in 2012. Although the first programming course I took used the LAMP stack (Linux, Apache, MySQL, and PHP), SQL seemed unintuitive, and the LAMP stack in general just felt bulky. When I started my first personal programming project, I looked for alternatives, and I found this new thing called the MEAN stack (MongoDB, Express.js, AngularJS, and Node.js). I thought it was the greatest thing in the world that you could use JavaScript in the front end and the back end, and that you could even use JSON like notation for the database. Having a full JavaScript stack made so much sense. Every web development project I've started since has used the MEAN stack. When it came time to hack together the first version of Vectorly, MongoDB was our first choice for the database. MongoDB: How has the experience been working with MongoDB? Sam: It’s been fantastic. We had to come up with this model of tracking users and usage of our platform in a very short amount of time, because the first version we released had no tracking whatsoever. One of the things that saved us a lot of time was the MongoDB Charts function, because it really allows us to track what we’re doing. It was super quick to set up. Looking to build something cool? Get started with the MongoDB for Startups program.

June 9, 2021

Built With MongoDB: Milky Way AI

Sagar Setu received his PhD in helicopter flight dynamics and has a fascination with deep learning and integration within the field of aerospace. However, helicopter flight dynamics is not what Sagar is involved in today. Through Entrepreneur First , an international program that helps entrepreneurs launch companies, Sagar met Eunice Wong , a fellow aspiring entrepreneur, who introduced Sagar to the world of retail, which he calls a “fantastic playground” for any engineer to be involved in. The pair founded Milky Way AI, with Wong as CEO and Sagar as CTO. Milky Way AI is designed to empower the largest retailers and brands globally with real-time visibility into how their products and their competitors’ products are being merchandised across thousands of stores. In this edition of #BuiltWithMongoDB, we chat with Sagar about the ways Milky Way AI creates opportunities for retailers, his favorite MongoDB features, and how the competitive AI industry keeps him motivated. MongoDB: What does the product look like now, and how does it work? Sagar: Our flagship product is called InstaShelf. It’s a mobile app that we put in the hands of distributors and merchandisers. When there is a person stocking the shelves and we put the app in the hands of that person, they are able to snap a photo, which then goes through our proprietary computer vision engine. This generates a variety of insights that are valuable for both the distributor and the brands. We are looking into how we can share this same data with and make it equally valuable to the retailers. We developed this product over the last year and launched our beta three months ago. Since then we have gotten quite good traction in terms of users in a number of countries that are deploying the product. MongoDB: Let's talk about that traction. How far along are you? Sagar: We started in January with a 15-store pilot for Kelloggs in Singapore. We have deployed across 150 stores now, and we are set to deploy across a few hundred more in Malaysia and the Philippines. By the end of next year, we hope to be in three more countries — just with Kelloggs. The typical number of users for each of our pilots is around 15 to 20 merchandisers visiting between 50 and 60 stores. In a typical audit, the user takes 10 to 15 photographs and our AI identifies what's on the shelf from these pictures, reporting on key metrics such as a brand’s share of shelf compared with a competitor brand, products that are out of stock, product placement compliance, and so forth. MongoDB: What does your tech stack consist of? Sagar: The web and mobile components of our solution are built using MongoDB, React Native, React, Node.js, and Python Flask. The computer vision pipeline is built on both TensorFlow and Pytorch. We use MongoDB for all our database requirements — transactional and analytical. Our top criteria for choosing the tech stack were proven scalability and stability, and a wide developer pool. It was important in the early stages to keep the team lean and the product flexible, and the choice of MongoDB Atlas turned out to be a great one. The support for being schemaless was crucial in allowing us to stay nimble as we learned the nitty-gritty of the domain. With features such as triggers and BI Connector, we could orchestrate various components of the solution right from the Atlas GUI, saving us hundreds of working hours. MongoDB: What are some of your favorite features in MongoDB? Sagar: My favorite feature is the support with autoscaling, which is the primary concern if you’re building anything into production. I’ve never had to worry about that. I don’t even think about it; I have just turned the features on, and it allows so much creativity. With MongoDB Atlas, I have peace of mind. MongoDB: What is something that you are learning right now? Sagar: Learning is a constant, working in the field of AI. A wonderful plus point is you always have so much competition: there might be a paper tomorrow that basically undoes everything you’ve done — something comes out that is far superior to the method you just took two months to deploy. So I’m always reading, learning, and trying to improve our solutions. MongoDB: What’s one of your favorite books? Sagar: The Selfish Gene . It’s not exactly technical, but more on the scientific side. That’s more of my kind of read. I really like the thought process the book instills in you. It gives you an understanding of the world — the good, the bad, and learning not to take things personally. Looking to build something cool? Get started with the MongoDB for Startups program.

May 26, 2021