AI workers for workflows you cannot automate blindly.
We turn messy SOPs and operational workflows into controlled AI-worker blueprints: trust boundaries, approval gates, blocked actions, test cases, readiness scoring, and runtime recommendations.
Built for teams considering n8n, Make, Zapier, Flowable, Temporal, Docker workers, or custom automation — but unsure where AI is allowed, where humans must approve, and what must never happen.
Automation tools can execute workflows. The hard part is deciding what should be allowed.
n8n and similar tools can support approvals, waits, branches, integrations, and AI-agent tool review. That does not remove the need for controlled-worker design. It makes the design layer more important.
Messy SOPs are not executable
Most workflows live in emails, tribal knowledge, spreadsheets, screenshots, and half-written procedures.
AI needs boundaries
Before an AI worker touches business systems, it needs scoped identity, approved tools, blocked actions, and human approval gates.
Runtime choice comes later
The same workflow may be suited for n8n, Flowable, Temporal, Docker, or no deployment yet. The blueprint should decide.
From messy workflow to controlled worker blueprint.
The first product is not a runtime. It is a discovery and design layer that produces the human-facing report and the machine-facing runtime package.
1. Discover the workflow
Paste an SOP, call notes, or a rough workflow description. The system extracts the trigger, actors, systems, process steps, rules, exceptions, and missing questions.
2. Define the Trust Boundary
Identify what the worker may read, what it may write only after approval, what must be blocked, and what data is out of scope.
3. Simulate edge cases
Generate test cases for normal flows, approval failures, missing data, unknown vendors, prompt injection attempts, and tool failures.
4. Recommend the runtime
Decide whether the workflow belongs in n8n, Flowable, Temporal, a Docker worker, a customer-hosted runtime, or should remain in discovery.
AI proposes. The control plane governs.
LLMs are used where they are strongest: interpreting messy business context, extracting fields, summarizing cases, generating clarification questions, drafting test cases, and preparing recommendations. Deterministic policy, approval gates, tool permissions, and audit rules decide what is allowed.
Not another automation builder.
The product sits before and above the execution layer.
Runtime-agnostic by design.
The blueprint can guide implementation in the right execution layer instead of forcing every workflow into one runtime.
Start with one workflow.
The first commercial offer is a Controlled AI Worker Blueprint Session — not a promise of full automation.
Input
One messy SOP, workflow description, discovery call, or operational pain point.
Output
Blueprint report, JSON specification, Mermaid diagram, trust boundary, test cases, readiness score, and runtime recommendation.
Next step
Implement in n8n, simulate in Docker, map to Flowable/Temporal, or clarify missing rules before automation.
Blueprint → Runtime Package human_report.md workflow_blueprint.json workflow.runtime.yaml policy.yaml approval_rules.yaml test_cases/*.json connector_requirements.md runtime_recommendation.md
Have one workflow that is painful but too risky to automate blindly?
Bring the messy SOP, email flow, or team explanation. We will turn it into a controlled AI-worker blueprint and show the safest first pilot path.
Book a 30-minute workflow review