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Stack AI wants to make it easy to build AI-powered workflows

TechBrunchBy TechBrunchMay 21, 20243 Mins Read
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Stack AI co-founders Antoni Rosinol and Bernardo Aceituno were MIT PhD students completing their degrees in 2022, just as large language models were becoming mainstream. ChatGPT is scheduled to be released globally at the end of the year, but even before that, we recognized the problem within enterprises of combining data and models without much expertise or knowledge, and we wanted to change that. Ta.

After graduating, they moved to San Francisco and joined Y Combinator's Winter 23 group, where they launched Stack and refined their idea. Now, the company has built low-code workflow automation tools designed to help businesses build AI-driven workflows like chatbots and AI assistants. The company has raised $3 million so far.

“Our platform allows you to build workflows that require different tools to connect and work together. We focus on connecting data sources and LLM, which allows you to build powerful workflow automations. It also offers many other tools and features to automate complex business processes,” Aceituno told TechCrunch. They've only been developing a working product for six months, but already he reports over 200 customers using it.

Basically, this involves dragging components onto the workflow canvas. This typically includes a data source such as Google Drive or LLM, and other workflow components such as trigger and action components to build the workflow, allowing customers to generate AI programs without extensive coding. can be created. Although the coding itself is not AI-driven, the tasks within the workflow are often AI-driven and may require manual coding to make the workflow work smoothly.

Some of our early customers were in the healthcare industry, where doctors and patients rely on internal data sources, especially when they are not always reliable or may contain contradictory or outdated information. Aceituno acknowledges that the applications involved require caution.

In such cases, he says, it's important to rely on human experts – doctors – to judge the quality of the answer. As another level of protection, all answers include source citations so medical professionals can verify the source before accepting the answer.

“That being said, it is true that if you put in garbage, the citations will also be garbage, which is why we want to ensure that these assistants do not take over the process completely,” he said. Ta.

Rossinol, an MIT graduate and startup founder, says going to YC really helped him understand the business side of things and how to work with customers to hone a startup idea.

“We started with an early version of this API, which was more developer-focused. Then we had the idea of ​​using AI to automate RFP responses and sales. We started with a few clients, and as we worked with them we realized that the real challenge was not in training the model, but in effectively querying the data sources and connecting to these language models. It became very clear.”

The company currently has six employees and is hiring engineers and sales and marketing professionals.

The $3 million investment was completed about a year ago. Investors include Gradient Ventures, Beat Ventures, and True Capital, as well as LambdaLabs, Y Combinator, Soma Capital, and Epakon Capital.



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