AutoML Stops at the Model. We Don’t.

AutoML tools build models, and then leave you hanging.

causaLens digital workers go further: automating full workflows, adapting to change, and producing business-ready outputs your team can use today.

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The Overarching Problem:
You’re Still Doing the Hard Part Yourself

AutoML promises to remove the data science bottleneck, but only for model training. Once that model is built, you’re still on the hook for visualizing the results, crafting stakeholder-ready outputs, and rebuilding everything when the data changes. That’s not automation. That’s a handoff.

Pain Point 1:
Models without action waste time and budget

You get a trained model, but then spend weeks turning it into something stakeholders can understand or use.

Pain Point 2:
Static SaaS workflows

Pain Point 3:
You’re paying for a tool that only solves one problem

causaLens Digital Workers replace AutoML tools, saving you money, time, and resources.

We recently replaced an industry-leading AutoML tool with Digital Workers for a global Pharmaceutical organization, saving them $millions in subscriptions and labor costs, whilst offering more personalized and intuitive interfaces for their business teams.

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The Solution:
Digital Workers That Don’t Stop at the Model

causaLens digital workers are AI agents built for the full data science lifecycle, not just one slice. They automate data prep, modeling, visualization, and delivery of business-facing outputs. And because LLMs and causal AI power them, they adapt in real time as your data and priorities shift.

Solution:
Deliver insights, not just models

causaLens digital workers create business-ready outputs, charts, presentations, and reports, so insights move from notebook to boardroom without delay.

Solution:
Adapt to new data without breaking workflows

Solution:
Consolidate tools and use cases into one platform

How it Works

AutoML tools are incredibly powerful for narrowly-scoped machine learning tasks. But you know that the workflow to get value from data involves so much more than just machine learning.

Because of their flexibility, causaLens digital workers can take the place of not just one point in a data science pipeline, but the entire pipeline.