Here’s a video walkthrough: https://youtu.be/S4L3B21vXGY.
We initially set out to build a dev tool for API integration, and while building in the integration space, we realized existing workflow automation tools have a few key limitations. They still force users to do a lot of work like: Navigate through a sea of menus; Break down their workflow into discrete steps; Manually configure data transformations.
This led us to explore how we could make workflow automation way simpler by letting users describe what they want in plain English and having AI take care of the automation setup, replacing no-code GUIs or scripts.
Under the hood, DryMerge has two key components:
- A semantic layer that uses LLMs to interpret the user's request and map it to a series of pre-defined triggers and actions (we've built hundreds of these integrations).
- A data plane that orchestrates the actual execution, complete with smart field mapping, conditional logic, and human-in-the-loop checks.
When a user describes a workflow, our semantic layer generates multiple candidate plans, scores them, and selects the best based on prior successful/failed workflows. It extracts key entities and fields needed, and auto-generates a simple form for the user to fill in any missing details. Users can then iteratively describe, tweak, and test their workflow in the same chat.
The data plane then subscribes to the relevant event streams, executes the workflow steps, and handles gnarly aspects like pagination, retries, and rollbacks invisibly. We allow the semantic layer to delegate some values for runtime dependency injection from the data plane, to handle open-ended logic like classifying an email as urgent or summarizing a Google Meet transcript.
We integrate with 14 common services — we’d love for you to try it out and share what you think. Check it out at https://drymerge.com/app.
by Andrey
Azimov