Support ticket triage — simple ones close, hard ones go to an engineer

The scenario classifies the ticket, searches the knowledge base, answers the client itself or calls an engineer with diagnostics, and feeds the found solution back into the base.

  • Tickets are sorted and resolved automatically
  • Known issues close with an answer in seconds
  • Hard cases go to an engineer with diagnostics
  • The knowledge base grows with every solution
WebhookMCP Client ToolPostgres PGVector StoreEmbeddings OpenAI
Connect a knowledge base and launchScriptera

How it works

To connect Pgvector and Slack, you don't need a developer: a ready-made scenario links them in minutes.

  1. Starts when: Incoming Ticket Webhook
  2. Then: Workflow Configuration
  3. Then: Ticket Classifier Agent
  4. Check: Check If Solution Found
  5. If yes: Format Auto-Resolution
  6. Then: Update Ticket Status
  7. Then: Prepare KB Entry
  8. Then: Add to Knowledge Base
  9. If no: AI Solution Generator
  10. Then: Create Diagnostic Logs
  11. Check: Check If Engineer Needed
  12. If yes: Notify Engineer
  13. Then: OpenAI Chat Model - Classifier
  14. Then: Classification Output Parser
  15. Then: MCP Server Tools
  16. Then: Knowledge Base Search
  17. Then: Embeddings OpenAI - Search
  18. Then: OpenAI Chat Model - Solution
  19. Then: Solution Output Parser
  20. Then: Embeddings OpenAI - Insert
  21. Then: Document Loader
  22. Then: MCP Server Tools1

You can launch this Pgvector + Slack integration in Scriptera: describe the task in plain words — the scenario is built, launched and monitored for you.

Similar scenarios