⚙️ Building a RAG Assistant That Actually Works: Evrone’s Case

Evrone didn’t want another chatbot — they wanted a tool that solves real workflow problems ⚡.

Their challenge was simple: employees kept interrupting HR with repetitive questions, even though answers existed in the Wiki.

🚀 What Evrone Built

Evrone created a RAG-powered assistant embedded directly into their ERP.

The system works as follows:

1. A user asks a question
2. Relevant Wiki chunks are retrieved
3. The LLM generates a contextual answer

🧠 Key Technical Choices

• FastAPI backend for performance
• React + WebSockets for real-time UX
• Vector embeddings for semantic search

📌 Content Structuring

Evrone discovered that chunking strategy matters. Instead of splitting text randomly, they:

• Used Markdown headers
• Kept chunks topic-focused
• Balanced size vs. context

🔄 Keeping Data Fresh

Evrone automated updates:

• Scripts detect Wiki changes
• Indexes rebuild instantly
• AI reflects the latest knowledge

🔐 Security Approach

Evrone avoided external AI services to maintain full control.

They implemented:

• Topic ограничения
• Rate limits
• Internal access control

📊 Impact

The result is measurable:

• Fewer HR interruptions
• Faster responses
• Continuous system improvement

Evrone shows that RAG is not just about models — it’s about thoughtful system design 🧩.

🧠 Lessons from Evrone: Making AI Useful Inside ERP Systems.