OpenClaw Internal Knowledge Base Setup

Background

Teams often face:

• Docs scattered across drives and wikis
• Hard to find the right answer during work
• Onboarding relies on tribal knowledge
• No way to track which questions are unanswered

This use case builds import → index → retrieve → Q&A.

Key steps

1. Import documents: policy, process, product docs, FAQs.
2. Index and chunk: structure for retrieval, avoid giant blobs.
3. Q&A interface: query → retrieve → generate answer with citations.
4. Track misses: log questions with no good match for human follow-up.
5. Incremental updates: add new docs without full reindex.

Workflow sketch

Docs in → Chunk & index → Query → Retrieve → Generate answer + cite → Log misses

Skill recommendations

openclaw skills install @clawhub/summarize

Use RAG or embedding skills if your OpenClaw setup supports them. Content Collector can help ingest links into Feishu or similar.

Expected results

• Indexed documents with usable chunks
• Q&A returning relevant answers with source references
• Miss log for unanswered or low-confidence questions
• Onboarding flow that points to the right docs

Common pitfalls

• Indexing everything without quality filters
• No citation or source in answers
• Ignoring miss log and letting gaps grow

Related pages

• Customer support automation

• Content Collector

• Get started

• Official docs → docs.openclaw.ai

• Issues → GitHub Issues