Unstructured Text to Business Decisions with Clocked.ai and n8n

Description and use

The workflow automates extraction of key fields from unstructured tickets, validates them, and routes them according to business rules, reducing manual effort.

Problem

Organizations generate large amounts of unstructured data such as support tickets, but conventional workflows store it as plain text, making it hard to extract actionable information and apply business rules.

Outcome

The workflow automates extraction of key ticket fields, validates them, and routes tickets according to business rules, reducing manual effort and improving consistency.

Workflow

• 1. Trigger workflow (manual or webhook) and assign correlation ID
• 2. Preserve original ticket content and metadata
• 3. Validate payload and redact sensitive data
• 4. Call Clocked.ai with a defined extraction schema
• 5. Receive structured JSON output
• 6. Validate extracted fields with Python or another layer
• 7. Evaluate routing decisions in System One
• 8. Execute approved actions in business systems
• 9. Log results and maintain traceability

Evidence

Organizations generate enormous amounts of unstructured data every day: IT support tickets, customer emails, invoices, incident reports, meeting notes, insurance documents, and operational logs.

Source

Our pipeline separates orchestration, extraction, decision-making, and execution.

Source

Clocked is the extraction stage: instead of asking a model to summarize the ticket freely, define the fields the application needs.

Source