Built With MongoDB: Atlas Helps Team-GPT Launch in Two Weeks

Team-GPT enables teams large and small to collaborate on AI projects. When OpenAI released GPT-4, it turned out to be a game-changer for the startup. Founded in 2023, the company has been helping people train machine learning (ML) models, in particular natural language processing (NLP) models. But when OpenAI launched GPT-4 in March 2023, the team was blown away by how much progress had been made on large language models (LLMs).

So Team-GPT dropped everything they were doing and started experimenting with it. Many of those early ideas are still memorialized on a whiteboard in one of the office's meeting rooms:

Evolving the application

Of all the ideas they batted around, there was one issue in particular the team wanted to solve—the need for a shared workspace where they could experiment with LLMs together. What they found was that having to work with LLMs in the terminal was a major point of friction. Plus, there weren't any sharing abilities. So they set out to create a UI consisting of chat sharing, in-chat team collaboration, folders and subfolders, and a prompt library.

The whole thing came together in an incredibly short period of time. This was due, in large part, to their initial choice of MongoDB Atlas, which allowed them to build with speed and scalability.

"MongoDB made it possible for us to launch in just two weeks," said Team-GPT Founder and CTO, Ilko Kacharov. "With the MongoDB Atlas cloud platform, we were able to move rapidly, focusing our efforts on developing innovative product features rather than dealing with the complexities of infrastructure management."

Before long, the team realized there was a lot more that could be built around LLMs than simply chat, and set out to add more advanced capabilities. Today, users can integrate any LLM of their choice and add custom instructions. The platform also supports multimodality like ChatGPT Vision and DALL-E. Users use any GPT model to turn chat responses into a standalone document that can then be edited. All these improvements are meant to unify teams' AI workflows in a single, AI-powered tool.

A platform built for developers

Diving deeper into more technical aspects of the solution, Team-GPT CEO Iliya Valchanov acknowledges the virtues of the document data model, which underpins the Atlas developer data platform. "We wanted the ability to quickly update and create new collections, add more data, and expand the existing database setup without major hurdles or time consumption," he said. "That's something that relational databases often struggle with."

A developer data platform consists of integrated data infrastructure components and services for quick deployment. With transactional, analytical, search, and stream processing capabilities, it supports various use cases, reduces complexity, and accelerates development. Valchanov's team leverages a few key elements of the platform to address a range of application needs. "We benefited from Atlas Triggers, which allow automatic execution of specified database operations," he said. "This greatly simplified many of our routine tasks."

It's not easy to build truly differentiated applications without a friction-free developer experience. Valchanov cites Atlas' user-friendly UI as a key advantage for a startup where time is of the essence. And he said that Atlas Charts has been instrumental for the team, who use it every day, even their less technical people.

Of course one of the biggest reasons why developers and tech leaders choose MongoDB, and why so many are moving away from relational databases, is its ability to scale—which Valchanov said is one of the most critical requirements for supporting the company's growth. "With MongoDB handling the scaling aspect, we were able to focus our attention entirely on building the best possible features for our customers."

Accelerating AI transformation

Team-GPT is a collaborative platform that allows teams of up to 20,000 people to use AI in their work. It's designed to help teams learn, collaborate, and master AI in a shared workspace. The platform is used by over 2,000 high-performing businesses worldwide, including EY, Charles Schwab, Johns Hopkins University, Yale University, and Columbia University, all of which are also MongoDB customers. The company's goal is to empower every person who works on a computer to use AI in a productive and safe manner.

Valchanov fully appreciates the rapid change that accompanies a product's explosive growth. "We never imagined that we would eventually grow to provide our service to over 40,000 users," he said. "As a startup, our primary focus when selecting a data platform was flexibility and the speed of iteration. As we transitioned from a small-scale tool to a product used by tens of thousands, MongoDB's attributes like flexibility, agility, and scalability became necessary for us."

Another key enabler of Team-GPT's explosive growth has been the MongoDB for Startups program. It offers valuable resources such as free Atlas credits, technical guidance, co-marketing opportunities, and access to a network of partners. Valchanov makes no secret of how instrumental the program has been for his company's success.

"The startup program made it free! It offered us enough credits to build out the MVP and cater to all our needs," he said. "Beyond financial aid, the program opened doors for us to learn and network. For instance, my co-founder, Yavor Belakov, and I participated in a MongoDB hackathon in MongoDB's office in San Francisco."

Professional services engagements are an essential part of the program, especially for early-stage startups. "The program offered technical sessions and consultations with MongoDB staff, which enriched our knowledge and understanding, especially for Atlas Vector Search, aiding our growth as a startup," said Valchanov.

The roadmap ahead for the company includes the release of Team-GPT 2.0, which will introduce a brand-new user interface and new, robust functionalities. The company encourages anyone looking to learn more or join their efforts to ease adoption of AI innovations to reach out on LinkedIn.