From scattered docs to grounded support replies
A clear, six-stage pipeline. Built so your team stays in the loop on every reply that goes out.
How a ticket becomes a reviewed reply
Each stage is connected. Each stage is observable. Each stage is improvable from the inbox.
- 01
Connect sources
Add docs, public site, FAQs, PDFs, CSV exports, and previous ticket history. Sources stay synced as you write.
- 02
Parse & structure product knowledge
Pages are chunked, parsed, and normalized. Entities and relationships are extracted automatically: features, plans, errors, policies, integrations.
- 03
Build relationships
The knowledge graph maps how plans gate features, how features depend on permissions, and how errors connect to remediation steps.
- 04
Resolve tickets
When a ticket arrives, retrieval combines vector search with graph traversal. The copilot drafts a contextual reply with citations and a confidence score.
- 05
Review & improve
Your team reviews the draft, edits if needed, and approves. Edits and approvals feed back into retrieval and ranking.
- 06
Measure gaps
Coverage analytics surface questions your knowledge does not yet answer, so the docs that retire the most tickets get written first.
The data flow under the hood
Sources flow into ingestion, parsing, embeddings, graph construction, retrieval, drafting, and human approval.
Step 1
Sources
Docs, web, PDFs, tickets
Step 2
Ingestion
Crawl, fetch, normalize
Step 3
Parsing
Chunk, extract, structure
Step 4
Embeddings & RAG
Vector retrieval
Step 5
Knowledge Graph
Entities & relations
Step 6
AI Draft
Grounded reasoning
Step 7
Human Approval
Review & edit
Step 8
Reply
Sent with citations
Want to see this pipeline running on your product?
Join early access and we'll walk through what each stage looks like for your knowledge sources.