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Resolve support escalations with product context

Support escalations require context from product docs, runbooks, known issues, release notes, and previous tickets. VectorAmp gives support teams a cited retrieval layer over that material.

What you will build​

A support knowledge assistant for escalation triage and answer drafting.

Prerequisites​

  • Product docs or help center URLs.
  • Runbook source folder.
  • Ticket exports or a simple API export from your support system.
  • Metadata plan for product area, severity, version, and customer tier.

Step 1: Connect product and runbook sources​

Use implemented sources such as web, Confluence, Google Drive, S3, GCS, Jira, or file upload.

vectoramp sources web https://docs.example.com --name public-docs \
  --config '{"max_depth":2,"allowed_domains":["docs.example.com"]}'

vectoramp sources gdrive runbook-folder-id --name support-runbooks

Step 2: Add ticket history​

If your support tool does not have a native VectorAmp connector yet, use its API to export tickets as JSON or CSV. Then ingest the export.

curl -H "Authorization: Bearer $SUPPORT_API_TOKEN" \
  "https://support.example.com/api/tickets?updated_after=2026-01-01" \
  -o support-tickets.json

vectoramp --dataset ds_123 datasets ingest-files ./support-tickets.json --source-name "Support ticket export"

Step 3: Search with product filters​

from vectoramp import VectorAmp

client = VectorAmp(api_key="vsk_...")
dataset = client.datasets.get("ds_123")

results = dataset.search(
    text="customers see ingestion timeout for large S3 prefixes",
    top_k=10,
    filters={"product_area": "ingestion", "status": "resolved"},
    include_documents=True,
)

Step 4: Ask escalation questions​

Useful prompts:

  • “Has this error happened before?”
  • “What runbook covers delayed S3 ingestion?”
  • “What changed in the release that could explain this failure?”
  • “Summarize similar resolved tickets and cite each source.”

Step 5: Add support workflow guardrails​

  • Use cited answers as drafts.
  • Escalate to engineering when citations are weak or contradictory.
  • Filter by product version where possible.
  • Keep customer-specific data in tenant-appropriate datasets.

Validation checklist​

  • Runbooks are cited ahead of stale tickets.
  • Ticket exports include useful metadata.
  • Filters reduce unrelated results.
  • Support agents can open original cited documents.