Workflow automation
Zapper Edge builds governed workflows that connect AI steps to the systems you already run. Every integration is discovered, ruled, mock tested and generated as code your team can read.
We chain AI steps and your existing systems into automated workflows, discovered, tested against mock data and generated as reviewable code.
- REST APIs
- Databases
- CSV and file exports
- JSON
- SQL rules
- Relational warehouse
- Field mappings
- Staging and error schemas
- Python
- Containers
- Git repositories
The challenge
Most enterprise workflows span many systems: SaaS platforms with their own APIs, databases, and files dropped into folders. Connecting them by hand means reading vendor documentation, guessing at endpoints, and writing one-off scripts. Each script is slow to build and hard for anyone else to review or maintain.
Adding AI to these flows raises the stakes. A model that reads, transforms or routes data can do real harm if the surrounding steps are unchecked. Without clear rules before and after each step, and tests before go-live, teams either move slowly or ship workflows nobody can fully explain.
What it does
Source discovery
A discovery step finds and describes your source systems and their endpoints, parameters and authentication, so the integration starts from a reviewed inventory instead of guesswork.
Pre-processing rules
Rules run on incoming data before it moves on. Records that break a rule are routed aside for review, so bad inputs never reach later steps.
Post-processing rules
Rules apply after fields are mapped, filtering and shaping the output before it lands in the target system. They are written in plain language and checked before they are saved.
Mock testing
Workflows are exercised against realistic simulated responses first, so the full chain can be proven end to end without touching live systems or real data.
Code generation
Approved endpoints, rules and field mappings are turned into a self-contained, reviewable code package, rather than a black-box flow that only the vendor can inspect.
Field mapping
Source fields are mapped to target columns with explicit transform expressions, so every value that moves between systems has a documented path and a clear owner.
How it works
Discover and approve
The discovery step finds source systems and their endpoints. Your analysts review each one, test it, then approve or reject it before anything is built.
Set rules and mappings
Pre-processing rules, post-processing rules and field mappings are defined and validated, with sample data used to show how many records each rule would affect.
Mock test the flow
The whole workflow runs against simulated responses. Failures are fixed here, in a safe place, before any real credentials or live data are involved.
Generate and release
The workflow is generated as code, run in a controlled test, reviewed by your team and then pushed to your repository for deployment under your own process.
Under the hood
- Interfaces
- REST APIs
- Databases
- CSV and file exports
- JSON
- Rules and data
- SQL rules
- Relational warehouse
- Field mappings
- Staging and error schemas
- Code and delivery
- Python
- Containers
- Git repositories
- AI layer
- Any chosen model
- Template based generation
- Automated fix loop
Business outcome
Faster path to a working flow. Discovery, rules, testing and code generation sit in one guided path, so teams spend less time on repeated manual integration work and more on checking results.
Proven before it goes live. Mock testing and controlled test runs catch broken mappings and bad rules early, so workflows reach production already exercised rather than tried for the first time live.
Code you can own. The output is readable code in your repository, not a hosted black box. Your engineers can review it, extend it and keep running it without us.
Governed AI inside the flow. AI steps work within approved rules and recorded runs, so automation can grow without losing sight of what each step does with your data.
Where it is used
- Pulling records from several business platforms into one warehouse, with bad rows held for review.
- Connecting a new SaaS tool to existing reporting by discovering its endpoints and generating the integration.
- Loading files exported from a legacy system into a clean target schema with documented field mappings.
- Adding an AI classification or extraction step between two systems, with rules before and after it.
Frequently asked questions
Do we need to use a specific AI model?
No. The AI steps are model agnostic. You choose the provider or model that fits your policy, and it can be changed without redesigning the workflow around it.
Is the generated workflow code ours?
The output is a readable code package that goes to your own repository. Your team can review, change and run it under your own deployment process.
Can we test without touching live systems?
Yes. Mock testing runs the whole chain against simulated responses first. A controlled test against real sources comes only after the flow has passed, and uses credentials stored securely on the server.
Does a person approve the work?
Yes. Endpoints, rules and mappings are reviewed and approved by your analysts, and generated code is inspected before release. AI proposes, people confirm, and every step is recorded.
See it on your own documents and data
Tell us what you want to automate and we will show you how it runs inside the harness.