Great product and congratulations on the launch. Who is the target user vs customer? On the surface, and I may be wrong here, this feels like a LLM layered on top of a typical AutoML structure eg: TPOT, Caret. Is that the correct mental model for a tool like this? And if so, do you see a similar problem that these tools faced in broader adoption at companies?

I think "agents layered on top of AutoML" is a reasonable simple mental model for Plexe's model building capabilities, but it also masks some important qualitative differences between Plexe and traditional AutoML tools:

1. AutoML tools work on clean data. Data preparation requires an understanding of business context, the ability to reason on the data in that context, and then produce code for the required data transformations. Given that this process could not be automated with "templated" pipelines, teams using AutoML still have to do the hardest - and arguably most important - part of the data science job themselves.

2. AutoML tools use "templated" models for regression, classification, etc, which may not result in as good a "task-data-model fit" as the sort of purpose-written ML code a data scientist or ML engineer might produce.

3. AutoML tools still require a working understanding of data science technicalities. They automate the running of ML training experiments, but not the task of deciding what to do in the first place, or the task of understanding whether what was done actually fits the task.

With this in mind, we've seen that most ML teams don't find traditional AutoML tools useful (they only automate the "easy" part), while software teams don't find them accessible (data science knowledge is still required).

Plexe addresses both of these issues: the agents' reasoning capabilities enable it to work with messy data (as long as you provide business context), and to ENTIRELY abstract the deeper technicalities of building custom models fitting the task and the data. We believe this makes Plexe both useful to ML teams and accessible to non-ML teams.

Does this line up with your experience of AutoML tools?