AI Assistant Using Your Business Knowledge Base — RAG on Your Documents

THE PROBLEM Q&A bots break down when knowledge lives in documents: a 100-page manual has no "questions" to match, it can't fit into a prompt, and generic chatbots hallucinate instead of admitting what they don't know.

THE SOLUTION A RAG (Retrieval-Augmented Generation) knowledge base: documents are split into meaningful chunks, embedded into a vector index, and the assistant answers from the right sections — by meaning, not keywords.

Any format as-is: PDF, DOCX, TXT, Markdown — 100+ pages is fine

Answers grounded in YOUR documents — it says "I don't have that information" rather than inventing

Source references — every answer shows which document and section it came from

Runs on your infrastructure — documents never leave your control

One command to re-index after updating documents — documented, no programmer needed

LIVE DEMO Ask it about my products, pricing, deployment and support — it answers from 6 real documents with sources: https://kb-assistant-demo.dvc-ai.workers.dev

The 'I don't have that information' line is the part most RAG builds skip, and it's the one that matters. How do you set the cutoff? On mine, a fixed similarity threshold broke once I filtered results by user role. Scores shifted and it refused questions it could answer.

Lost 2 days building a complex n8n workflow. Here's what I learned.✌️ The project: A 12-node AI Receptionist automation for a luxury resort in Bangladesh. Multi-channel (WhatsApp, Telegram, Voice), 9 Google Sheets, Gemini API, payment automation, customer CRM. What went wrong: Added a new voice integration node. Accidentally deleted the entire workflow. Why it happened: No JSON exports after milestones No version control (Git) Testing changes LIVE on production workflow No documentation Building locally with zero redundancy The fix (now implemented): ✅ JSON backups after every 2-3 nodes ✅ GitHub versioning ✅ Clone workflows for testing ✅ Modular design ✅ Complete documentation For automation professionals: You're not just builders—you're engineers. Treat workflows like software: Documentation Version control Backup strategy Modular testing Client delays = lost revenue + credibility damage. Make the systems bulletproof. How do you prevent data loss in your automation builds? 👇 #n8n #Automation #WorkflowAutomation #NoCode #LessonsLearned #make #AIAutomation #DataAnalyst #MachineLearning

I'd try restoring one of those JSON backups into a cloned workflow before the next handoff. That way you know it can actually get you running again.