Show HN: Workflow86 - An AI business analyst and automation engineer (workflow86.com)
We built Workflow86 to help teams build and automate their internal business processes and workflows using drag and drop components like forms, tasks, tables and nodes for business logic, API requests, running custom code etc. It works as a standalone process/workflow automation tool, or as a workflow customization layer on top of existing apps and systems like HRIS, CRM and ERP.
One common problem we hear from users is that no-code still has a significant learning curve, and it can take some time to understand how to properly build something. Users also needed help with knowing what to build in the first place, or what a process might or should look like.
To solve this, we've integrated an AI that acts as a business analyst/consultant and workflow automation engineer. This AI is powered by a combination of Large Language Models and lots of prompt engineering, RAG and prompt chaining techniques we developed along the way.
See a demo of it in action here: https://www.loom.com/share/fdbd5ad64c8f4071a062ecaa6a6d01f1?...
In business analyst/consultant mode, the AI helps users brainstorm ideas, identify and discover processes and draft what a process should look like. Like a business analyst/consultant, the AI works to pull and extract information and details from the user by asking the right questions rather than rely on the user's instructions alone.
Once the required information has been gathered, the AI goes into engineer mode: it will plan and then build the entire workflow by selecting the right nodes, connecting them together and then fully configuring every single node individually as well. This includes writing custom code and API requests using stored credentials when required.
Once a workflow is built, edits can be done manually or by asking the AI to adjust the workflow at any time (e.g., “Add a compensation band check before final approval”). The AI has full context of the current state of the workflow, so it can “patch” in any changes like adding new nodes, rewriting existing nodes and so on.
Some use cases we’ve seen from customers include building: - automated compliance checks for new CRM leads - custom international contractor onboarding workflows on top of a HRIS - automated vendor risk assessment before ERP updates
Try it out and let us know how the AI performs and any other feedback you have!
Full docs can be found at https://docs.workflow86.com
47 comments
[ 2.3 ms ] story [ 114 ms ] threadThat said, maybe this would be really valuable to someone doing a greenfield project without needing to back into existing workflows. Either way, cool project.
We actually integrate with Zapier i.e. you can trigger a workflow from Zapier, and we can trigger a zap from within a workflow.
While Zapier has also done some great work in the AI space, I'd also say our Ai builder goes a lot further in being able to fully set up a workflow and then continue to help users edit, change and refine them at any point. We're able to do this because a lot more of the moving parts are internal to Workflow86 (forms, tables, tasks etc), so the AI has more context and control over what it can do.
Not because it isn’t useful (to be honest it looks decidedly useful as an API aggregator service) but the fact that one of its main selling points is for “AI”.
Yet, if we had AI, it would be able to build these integrations itself
These credentials can then be use in the API node (where the AI can write a custom curl) or the run code component (where the AI can write custom python or js code to make an HTTP request).
The fact is “AI” isn’t building these integrations because they aren’t capable. They’re capable of generating some sandbox code, if anything, but full scale integration, that updates itself as APIs change etc?
It’s simply not happening, because they don’t actually reason, because they lack actual intelligence
I moved on to other things.
I think ChatGPT really kicked that off, but maybe it was something else that inspired it?
Less normie/friendly and more technical sounding. So far, I'm a fan!
Make's copilot is pretty limited to generating an outline of the flow by selecting the right nodes but does not actually configure them. You still need to manually click into each one and set it up.
Zapier goes a bit further than Make, but it still leaves the workflow with a lot of configuration work that needs to be picked up by the user.
In both Make and Zapier, you really need to prompt the AI copilot in a very specific way to get good results. In our case, the AI is designed to use its business analyst/consultant mode to extract information so it can work from very general, unclear and ambiguous instructions to a clearly defined workflow/process to build.
The ability for our AI to edit the workflow at any time (including on top of your own manual changes) also means you can have a continuous iterative dialog/interaction with our AI copilot vs a once off interaction at the start. Both Make and Zapier's AI Copilots lack this or are very weak in being able to edit existing workflows reliably.
I'm sorry to be rough, but from your description it just sounds like your AI somehow does a better job with prompts compared to Zapier & Make, which is highly subjective.
Besides that, this looks very cool and IMO is the future of interfacing AI automations in work environments
One example of what we had to do to achieve this was to develop an "intermediary language" defines how the current state of the workflow is represented to the AI and how the AI responds back - this needed to capture enough detail about the workflow without overwhelming it with too much context. We also developed techniques for structuring the prompting, with the process of building a workflow actually split into 3 stages: a pre-build planning stage, a build stage where the overall structure of the workflow is set, and then a build node stage where each individual node its configured. There is a bunch of other techniques we developed to get LLMs to be able to do what they current do, but these are just some examples of how it's a bit more than just a "You're a business consultant" prompt.
One thing I'd encourage people to do is test these co-pilots head-to-head on the same prompt. If you were to ask Zapier or Make to "build me a process for triaging customer complaints", I'd expect them to not get very far, perhaps an outline of some apps you could connect together to achieve it. If you asked our AI this same request, it would be able to deliver a complete workflow with fully configured forms, tables, branching logic, tasks etc
Congrats on shipping this.
Off topic question out of curiosity:
If we are builde a simpler visual "workflow" tool that allows users to drag and drop components on to a canvas, connect them, create brancing logic etc and configure properties of each components in a properties panel .... is there a good open source library/framework that we can add to say a React front end that has a lot of this UI already available? Thanks
Here's the workflow that was generated: https://app.workflow86.com/template/2f8c5a73-af76-47bc-8757-...
I am in a similar space and our product creates workflows from prompts. Internally it is a JSON schema generator and response parser (from AI). We focus on creating assistants (personal or team) that reside on local computers, can crawl, read from any data source and build a knowledge graph for search, daily tasks, etc.
In our UI efforts (we are really behind in this) the graph editor is starting to look like how I see in your product.