Platform · Actions & Workflows
Business actions, not raw API calls.
Give AI the actions your business runs on, like “mark an invoice paid” or “onboard a new customer,” built without code in hours.
Shape
Give AI fewer, better tools.
An AI with three hundred raw API calls guesses. An AI with ten well-described actions has far less to guess, and nothing else it can do through Bazinga.
Named for the business.
“Mark invoice paid” and “onboard new customer,” not endpoints and table names.
Described for the model.
Every data set and action carries a plain description, so the AI can tell which one fits the request.
Off until you switch it on.
Nothing is exposed by default: each data set, each operation, and each workflow is opted in.
Data
Look things up. Change only what you allow.
Expose a data set to AI, and choose what may happen to it. The same few actions work across every data set, so the toolkit stays small however large your systems are.
Invoices
Description for the AI
Customer invoices from the finance system. One per order, with status, amount, and due date.
What AI may do
- Look upon
- Createoff
- Updateon
- Deleteoff
Hidden from AI
Bank account
Look up and filter.
Search, sort, and page through records with structured filters. AI never writes its own SQL.
Change only what you allow.
Create, update, and delete are switched on one at a time, for each data set.
Hide what AI shouldn’t see.
Keep a field like a bank account or a tax ID out of what AI gets from a data set, while the rest of the record stays available.
Workflows
One request. Every system it takes.
The work that matters rarely lives in one system. Expose a workflow, and AI can run the whole process from one sentence, the way your business designed it.
Onboard new customer
Inputs
- Company
- Plan
- Start date
- Mark the deal wonCRM
- Send the contract for signatureE-signature
- Create the customer accountERP
- Open the onboarding projectProjects
Typed inputs.
Each workflow names its inputs, with types and descriptions, so AI can gather the right information before it runs.
Every system it takes.
One action can update a record, call an API, post a message, and send an email, in order.
Your rules inside.
Validations, approvals, and error paths run the same way whether a person or an AI starts the work.
Only for the right roles.
Decide which roles may run each workflow through their AI.
Engine
A real workflow engine behind every tool.
An MCP tool is only as good as what runs behind it. In Bazinga, that’s the workflow engine of Plant an App: the same one that runs processes for governments and enterprises.
Drawn in standard BPMN.
What you draw is what runs: tasks, decisions, and events, in the notation process owners already know.
Decisions and parallel paths.
Branch on any condition, or run steps side by side and continue when all of them finish.
Tested before AI uses it.
Run the workflow with real inputs and see every output, before any assistant can call it.
Failures take a planned path.
You decide what happens when a step fails and what the workflow reports, so AI and people know what needs attention.
- 450+ ready-made actions
- Data
- Documents
- Email and messaging
- Integrations
- AI steps
- C#, JavaScript, or PowerShell
Build visually. Add code only where you need it.AI sees the same simple tool either way.
Confirm
Look-ups run. Changes are flagged.
Looking things up is one thing. Changing them is another, and the person whose name it runs under should approve it.
Read-only by default.
A data set starts with look-ups only. Every change is a separate decision.
Changes flagged.
Actions that change data are marked as such, so assistants like Claude and ChatGPT can ask the person before running them.
You decide what counts.
Flag any workflow as a change, even when it only sends a message.
Who asks. Each assistant decides how it asks before a flagged change, in the place the person works. When a sign-off must happen whichever assistant is used, build it into the work: the AI’s action opens a request, and the change runs only when someone in the right role approves it. An agent running on a key has no one to ask, so give it only the actions it may run on its own.
Build
A toolkit in hours, not a project.
No code, and no MCP server to write and maintain. Actions are built in a visual builder, on the same platform that runs the systems behind them.
Connect
Pick the systems the toolkit works with.
Choose
Switch on the data sets, operations, and workflows AI may use.
Test
Run each workflow with real inputs, and describe each tool in the words your business uses.
Try it
Connect Claude or ChatGPT and ask for the work, as your users will.
Your first tools in an afternoon.The workflows behind them change with the business, with their history in your own Git.
Related capabilities
Name the work you want AI to do.We’ll show you the actions.
Pick one process your teams repeat every week, and see it become an action anyone can ask their AI for, safely.