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

Stack AI co-founders Antoni Rosinol and Bernardo Aceituno were PhD students at MIT, completing their studies in 2022, just as large language models were becoming more popular. ChatGPT would be released to the world at the end of the year, but even before that they recognized a problem within companies that combine data with models without much expertise and knowledge – and they wanted to change that.

After graduating, they moved to San Francisco and joined Y Combinator’s Winter 23 group, where they launched Battery and refined his idea. Today, the company has built a low-code workflow automation tool designed to help companies create AI-driven workflows, including chatbots and AI assistants, for example. The company has raised $3 million so far.

“Our platform allows people to create workflows that require connecting different tools to work together. We focus on connecting data sources and LLMs as this allows you to create powerful workflow automations. We also offer many other tools and functions to automate complex business processes,” Aceituno told TechCrunch. They’ve only had the product working for six months, but they already report over 200 customers using the product.

Essentially, this involves dragging components onto a workflow canvas. This typically includes a data source like Google Drive and an LLM, along with other workflow components like a trigger component or an action component to build the workflow, allowing the customer to create generative AI programs without lots of coding. The coding itself is not AI-driven, but the tasks in the workflow often are, and may require some manual coding to make the workflow run smoothly.

Some of its first customers are in the healthcare sector, and Aceituno recognizes that care must be taken with applications involving doctors and patients, especially when internal data sources are not always reliable or may contain contradictory or obsolete information.

In these cases, he says, it is important to rely on the human expert, the doctor, to decide on the quality of the response. As another level of protection, include source citations in all responses so that the healthcare professional can verify the source before accepting the response.

“That being said, it is true that you can put out garbage and then the citations will also be garbage and so it is necessary that these assistants do not completely take over the process,” he said.

Coming straight from MIT and launching a startup, Rosinol says that going to YC really helped them understand the business side of things and how to refine their startup idea by working with customers.

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“We started with an early version of this API, which was much more developer-focused. And we started with some customers with the idea that we wanted to use AI to automate RFP responses or automate sales. And in working with clients, it became very evident that the real challenge was not in training a model, but rather in effectively querying and connecting data sources to these language models.”

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

The US$3 million investment was closed about a year ago. Investors include Gradient Ventures, Beat Ventures and True Capital, along with LambdaLabs, Y Combinator, Soma Capital and Epakon Capital.