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Solution Overview Introduction

Ai-Butler GenManual

Automatic writing of user and repair service manuals for small and mid-sized manufacturers | Ontology knowledge DB + LLM (RDF RAG)

Put your product data in, get a draft manual out

Existing manuals, design documents, BOMs and requirements become knowledge → generative AI writes the document-and-illustration structure automatically
User manual and repair/service manual together, in one pass

RDF Triples5M+ (manual structure)
Instances3M+ (manuals/BOM)
IP5 patents filed

[Conventional practice / ERP document management] vs [Ai-Butler GenManual]

Writing manuals the old way (by hand or outsourced)

Slow, uneven
• Long to write, costly
• Quality varies with the writer
• Slow to reflect changes
• Rising service and support costs

Ai-Butler GenManual

Generated from data
• Draft written automatically from product data
• Structure and terminology standardized by ontology
• RAG grounds the text in evidence (minimal hallucination)
• User and repair/service manuals at once

Automation roadmap

Stage 1: 70% AI + 30% human (semi-automatic)
Stage 2: under 10% human (highly automated)
Features Features

How it works (Ontology RDF RAG + manual generation)

INPUT → KB → OUTPUT

INPUT Existing manuals, design documents, requirements BOM and parts data
ONTOLOGY Manual structure schema generated automatically (for text and illustrations)
RAG+LLM RDF RAG on Qwen and Llama3 Generation, summary and Q&A grounded in evidence
OUTPUT User manual + repair/service manual + online Q&A (turned into a chatbot)

An example of ontology-based manual structure

Ontology diagram

Value for small and mid-sized manufacturers (End-Customer Benefits)

1
Shorter lead time Faster to reflect launches and changes ↑
2
Standardized quality Manual quality at an international standard
3
Lower service costs More efficient repair and customer support ↑

Quick Matrix: what sets it apart (against ERP and other tools)

Item Typical ERP or tool GenManual
Purpose Storing work and documents Generating manuals automatically
Grounding Templates and people Ontology+RAG
Reflecting changes Updated by hand Regenerated from data
Service Centered on PDFs Turned into Q&A (chatbot)