AI Lab

The core of the product is a canvas where you place blocks and connect them. Data loading, cleaning, model choice, and training are separate steps you can rearrange and rerun, which means a pipeline you build once can be adjusted for the next dataset without starting over from scratch. It's aimed at people who understand their data but not the code that usually surrounds it.

AI Lab ships with forecasting as a first-class workflow. You feed in historical data, and the pipeline produces forward-looking predictions for sales, demand, or patient volume. The templated approach cuts setup time compared to building the same thing in a notebook. It just works, most of the time.

Rather than starting from an empty canvas, you pick a scenario such as churn prediction or risk assessment. Each template carries a sensible default pipeline you can adjust without touching the underlying settings. For a first project, that's the difference between finishing in an hour and giving up.

Data scientists aren't locked into the visual interface. You can prototype on the canvas and export the workflow as code for deployment. That keeps the tool useful for teams that need the model to live somewhere other than a dashboard, because the same pipeline that you tested visually can be handed to engineering and run in your own infrastructure without a rewrite.

The vendor positions AI Lab for shared workflows across departments, so different people can work on the same project without passing notebooks around. Consistency across an organization's models is the stated goal. The exact collaboration features aren't confirmed yet.

Beyond prediction, the platform aims to surface insight from your models, like which factors drive churn or which periods spike demand, and it packages that output in a form a manager can read without opening a notebook. That's what a non-technical stakeholder actually reads. It's the payoff, not the model itself.