LLMs in Action: How Norkart Leverages AI - Mathilde Ørstavik & Malte Loller-Andersen

Norwegian municipalities handle ~90,000 building permit applications yearly, with 40% containing errors due to complex zonal regulations and documentation

Norkart developed “Plandprat” - an LLM-based system to analyze zonal plans and help understand building regulations by:

• Using property boundaries to identify relevant zonal plans
• Converting documents into searchable chunks through preprocessing
• Employing RAG (Retrieval Augmented Generation) architecture to find relevant information
• Providing property-specific answers about building regulations

Key technical components include:

• Python/Flask backend with Docker deployment
• Azure OpenAI GPT-4 for text generation
• Norwegian-specific LLM models (NURVAT) for better language understanding
• Custom embedding model (Plambert) trained on 70,000 zonal plans
• RagAS framework for evaluation metrics

System evaluation focuses on:

• Context precision and recall
• Faithfulness of LLM responses
• Answer correctness
• Hallucination detection

Challenges addressed:

• Processing various document formats (scanned, tables, maps)
• Handling multiple applicable zonal plans
• Converting complex legal language into understandable responses
• Maintaining accuracy while providing concise answers
• Dealing with Norwegian language specifics in LLM processing

The system improves building permit applications by:

• Reducing time spent searching through regulations
• Making complex rules more accessible
• Providing property-specific answers
• Helping identify relevant regulations early in the process