Enterprise Knowledge Base Management System (Deployed — AI Solutions)
Deployed enterprise Knowledge Base Management System that ingests and structures content for 15 applications, publishes curated packs to NotebookLM, and cut time to answer (TTA) from 15 minutes to 2 minutes—saving 150+ hours/month.
Problem
Docs lived across vendors, SOPs, and old tickets; agents spent 15 minutes hunting per question.
Approach
Python scrapers/API pulls and file watchers convert sources to Markdown/HTML, tag metadata (app, version, audience, last-reviewed), chunk for retrieval, and publish curated packs to NotebookLM. Prompt catalog standardized queries.
Results
Time-to-answer dropped 15 → 2 minutes across 15 apps. Estimated 150+ hours/month returned to IT. Relevance increased after prompt-catalog rollout; curated packs keep drift low.
Architecture
Ingestion → Normalize → Metadata/Chunk → Deterministic folder/schema + JSON sidecars → Scheduled publishing → Prompt catalog
- Python scrapers and API pulls
- File watchers and change detection
- Metadata tagging and normalization
- NotebookLM publishing pipeline
- Prompt catalog and standardization
Key Challenges
- Normalization across vendors
- Metadata freshness
- Change-detection and safe updates
Lessons Learned
- Schema discipline compounds
- Prompt standardization lifts answer quality
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