Levity's End-to-End AI Workflows
Thilo Huellmann, CTO of Levity, on using no-code AI for workflow automation
This choice says Levity is trying to own the full workflow around messy business data, not just one technical step. Instead of selling only labeling, model training, or app integration, it bundles data import, cleanup, model creation, deployment, and downstream actions into one product. That matters because the real buyer is usually an ops owner with a manual process, not an ML team assembling five separate tools.
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Levity is built for teams already using tools like Zapier, but getting stuck on emails, PDFs, images, and other unstructured inputs. The product starts with historical examples, trains a custom classifier, then routes the prediction back into the workflow, which removes the handoff between labeling tool, model tool, and automation tool.
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The tradeoff versus Zapier, Make, or n8n is depth in a narrower class of jobs. Those platforms are broad orchestration layers across many apps. Levity goes deeper on cases where the hard part is understanding the content itself, like classifying text or extracting fields from documents, then ships native integrations so common workflows feel first party.
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The strategic bet is that the biggest market is the white space between vertical software categories. Levity explicitly points to custom problems that are too small to support a standalone vertical AI company, but painful enough that companies otherwise hire consultants, agencies, or internal developers to stitch together one off solutions.
Going forward, the winners in this layer are likely to be platforms that hide the plumbing and make custom automation feel as simple as configuring a business app. That pushes Levity toward deeper native integrations, more built in data preparation, and more reusable AI workflows, so customers can automate niche processes without ever assembling a separate ML stack.