A Different Approach to Automation
Visual workflow builders ask you to think spatially. Drag a module here. Connect it to that module. Configure the data mapping. Add a router for conditional logic. Draw the branches. It's powerful, but it requires translating what you want into a flowchart.
NimbleBrain asks a different question: what if you could just say what you need?
"When a customer submits a support request, create a ticket in Zendesk, and if it's marked urgent, ping the on-call team in Slack with the details."
That's it. You describe the outcome. The system figures out the modules, connections, conditions, and data mappings. You refine through conversation, not canvas manipulation.
This isn't about simplifying a visual interface. It's a fundamentally different paradigm, designed for people who think in outcomes rather than flowcharts.
How NimbleBrain Works
Here's what building automation looks like in NimbleBrain:
Describe What You Want
Start with plain language. You might say: "Sync new Shopify orders to our inventory spreadsheet and alert the warehouse team." Or: "When someone fills out our contact form, add them to HubSpot and send a personalized welcome email."
Refine Through Conversation
The system might ask clarifying questions: "Should the welcome email include their company name if they provided it?" You answer naturally. The workflow adapts.
Modify by Explaining
Need to change something? Just say it: "Also create a follow-up task for sales if the deal size is over $10k." No finding the right module, no reconfiguring the canvas.
Handle Complexity Without Branches
Visual builders require explicit routers and branches for every variation. NimbleBrain interprets your intent. Describe the conditions in plain language; the system handles the logic.
Self-Host If You Need To
For organizations with data sovereignty requirements, NimbleBrain offers self-hosting through the NimbleTools open source runtime. Run automations on your own infrastructure while keeping the conversational interface.
Where Make Fits
Make is a powerful tool for technical users who think visually. The canvas interface lets you see your entire workflow at once, with color-coded modules showing data flow. If you enjoy building diagrams and want to see exactly how data moves between systems, Make delivers that. Their data transformation functions are extensive for parsing, formatting, and restructuring data.
Make also offers more actions per app than many competitors and competitive pricing for complex workflows. For teams with technical users who enjoy the puzzle of visual building and need granular control over every data transformation, Make is genuinely good at what it does.
Side-by-Side Comparison
| Capability | Make | NimbleBrain |
|---|---|---|
| How you build | Drag modules, draw connections | Describe what you want in plain language |
| How you modify | Find the module, edit on canvas | Say what you want to change |
| Handling variations | Build explicit routers and filters | Describe the conditions naturally |
| Learning curve | Moderate (understand modules, data mapping) | Minimal (just explain what you need) |
| Data transformation | Extensive built-in functions | Describe the transformation you need |
| Integrations | 2,400+ apps | Growing library + webhooks |
| Self-hosting | No | Yes (NimbleTools runtime) |
| Debugging | Visual execution history | Ask what happened in plain language |
On Pricing
Make uses an operations model where each module execution counts. Complex scenarios with many modules add up. NimbleBrain uses tiered usage-based pricing where both complexity and volume affect costs. Check our pricing page for current rates.
When NimbleBrain is Right for You
- You think in outcomes, not flowcharts. Describe what you want without translating it to visual logic.
- Your team isn't technical. Operators can create automations without learning visual programming.
- You want faster iteration. Change workflows by explaining what's different, not editing a canvas.
- Complex logic shouldn't mean complex diagrams. Describe conditions naturally; skip the routers.
- You need self-hosting. Run on your own infrastructure with NimbleTools.
When Make Might Be Better
- You enjoy visual building and want to see your data flow as a diagram
- You need extensive, granular data transformation functions
- Your team has technical users who prefer canvas-based interfaces
Frequently Asked Questions
What about complex data transformations?
Describe them. "Format the date as MM/DD/YYYY." "Extract the domain from the email address." "Combine first and last name into a full name field." NimbleBrain handles common transformations through natural language. For highly specialized manipulation, Make's extensive function library offers more granular control.
Can I see what the automation is actually doing?
Yes. NimbleBrain shows you the workflow it built and the execution history. You can ask "what happened with the last run?" or "why did this fail?" Transparency through conversation, not through reading flowcharts.
I like seeing my workflow visually. Is that possible?
NimbleBrain can show you what it built, but the primary interface is conversational. If you genuinely enjoy the process of visual building and find it satisfying to see your logic as a diagram, Make's canvas might be a better fit for how you think.
What if I need an integration you don't have?
Our integration library is growing. If you need something we don't have, let us know. We can often add integrations quickly. You can also use webhooks for custom connections.
Can I migrate my Make scenarios?
There's no automatic migration. The process is straightforward though: describe what each scenario does in NimbleBrain and it will recreate the workflow. For complex scenarios with many branches, you may want to simplify during migration. See our tutorials for examples of building common workflows.
Can I self-host NimbleBrain?
Yes. The NimbleTools open source runtime lets you run automations on your own infrastructure. This gives you data sovereignty and control while keeping the conversational interface.