About Us

Reflecting on our inception, our founder, Ryan Schuetz, shares his inspiring story:

"I remember sitting at the Civic Center in downtown San Francisco in 2018 when Twilio unveiled a new product called Autopilot. As a rather average software developer, I was captivated by this technology, convinced it was going to change the world. It was the first time I saw AI in a format that made sense to me, that I could interact with easily via code and API's. Despite its simplicity compared to today's standards, Autopilot’s primary function—creating Omni-Channel AI chatbots—seemed tremendously useful, not only for our contact center but for many others as well.

Eager to explore its potential, we brainstormed ideas, designed interfaces, tested products, and built MVPs. We developed some impressive bots, yet the elusive magic that would transform these into viable commercial projects remained out of reach. Nonetheless, this journey sparked a deep curiosity about other AI technologies.

Over the following years, we delved into various AI innovations to enhance our contact centers. Early IBM Watson NLP proved particularly fruitful, especially for real-time transcription and processing of voice calls—tools we still use today. Despite building helpful internal tools with other technologies, we again fell short of creating viable commercial products.
 

The introduction of the current generation of Generative AI, most notably exemplified by the rise of OpenAI’s ChatGPT, caught our attention. This seemed to be a missing piece of the puzzle. We spent the next year developing a wide range of tools and applications. These tools started to make a significant impact on our business, yet they felt like a collection of cool prompts, “party tricks,” and automations without a cohesive product.

As we dove deeper into modern generative LLMs, we realized the ease with which these models could be prompted to solve or automate various problems and processes. The ability to create interesting and useful prompts quickly surpassed our ability to collect, analyze, and make meaningful decisions with the data. The greater challenge quickly became aggregating and analyzing all the resulting data. Shifting our focus from AI to BI (Business Intelligence) was crucial.

This realization led us to build a platform to accomplish a few things. First and foremost manage and make sense of the data we were collecting. Second, creating an easy way to generate and manage the data sets needed to train our models, and allow us to train models on an individual org/user level. Third, provide the structural components to work well with the various AI systems of today as well as tomorrow. Automatdo was the result."