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D2 Automation Systems · Public Work

Systems are judged by the decisions, failure paths and evidence behind them.

These case studies document how D2 approaches workflow orchestration, API integration, data systems, AI-assisted operations and automation infrastructure. Architecture knowledge, workflow evidence and production claims are kept explicitly separate.

Decision before node

The case starts from the operating constraint and the design decision, not from a list of n8n nodes.

Source of truth before automation

State, ownership and evidence boundaries are made explicit before workflow execution is layered on top.

Failure paths stay visible

Retry, idempotency, recovery and unverified claims remain visible instead of being hidden behind a happy path.

11 public case studies

Automation work across operations, data, AI and infrastructure.

Each case states what is supported by workflow or source evidence and what is deliberately not claimed. The goal is to show how D2 thinks about systems, not to manufacture vanity metrics.

D2 Automation Systems

Have a process that should become the next system?

Share the current process, systems involved, source of truth and the output that matters. D2 will map the problem before proposing implementation scope.

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