Describe what you need - and watch the workflow build itself.
Building an enterprise AI workflow used to mean weeks of back-and-forth: scoping sessions, technical design documents, engineering sprints, and rounds of revision before anything ran in production. The bottleneck wasn't usually the technology. It was the translation layer between what a business needed and what an engineering team could build.
Unframe's Builder changes that starting point.
In the Builder, you describe a use case in plain language - the trigger that starts the workflow, what each step should do, how approvals should route, what the agent should produce at the end. Unframe assembles that description into a structured workflow on the canvas: nodes, triggers, logic, and connections laid out and ready to run. From there, you refine. You don't start from scratch.
The canvas itself gives builders full visibility into what the workflow does at every step. Each node is editable. Triggers can be swapped without rebuilding the rest of the flow. Branches, escalations, and human approval checkpoints can be added where the use case calls for them. The output is a workflow you can test, publish, and hand off - not a prototype that needs engineering work before it's real.
What this changes for the business
The traditional path from "we need an AI workflow" to "the workflow is running in production" runs through too many handoffs. A business team describes a need. Someone translates it into a specification. Engineers build from the specification. The result often looks different from what was originally described, and the cycle starts again.
Starting from a conversation compresses that cycle. The person who understands the business problem can see the workflow take shape in the same session where they described it - and can correct it before it goes anywhere near production.
