About Building Code Assistant
Learn how A.I. technology makes building codes accessible and searchable for everyone.
Instead of searching through hundreds of pages of building codes, you can ask questions in plain English and get accurate answers with specific code references. Whether you're a contractor, architect, homeowner, or student, this tool makes building codes accessible to everyone.
Ask Questions Like:
- • "What is the minimum ceiling height for bedrooms?"
- • "How many electrical outlets do I need in a kitchen?"
- • "What are the fire safety requirements for stairways?"
- • "Do I need a permit for a deck addition?"
Get Answers With:
- • Specific code section references
- • Clear explanations in plain language
- • Safety considerations and requirements
- • Relevant exceptions and special cases
1. You Ask
Type your building code question in plain English
2. AI Searches
Advanced AI finds the most relevant building code sections
3. Codes Retrieved
System pulls actual text from official building code documents
4. You Get Answers
Receive clear explanations with specific code references
Traditional Method:
- • Download 500+ page PDF documents
- • Search through table of contents
- • Read through multiple sections
- • Cross-reference different chapters
- • Takes 30+ minutes per question
AI-Powered Method:
- • Ask questions in plain English
- • Get instant, targeted answers
- • Automatic code section citations
- • Related requirements included
- • Complete answers in under 30 seconds
60x Faster
30 seconds vs 30 minutes per question
Always Current
No outdated downloads or missing updates
Complete Context
Related requirements and exceptions included
This system uses cutting-edge technologies that go beyond traditional enterprise RAG solutions like Microsoft's Chat-with-your-data accelerator, delivering superior performance and user experience.
Microsoft's Enterprise Solution
- • Azure OpenAI: GPT-4 with high costs
- • Azure AI Search: Basic vector + keyword search
- • No Streaming: Batch responses only
- • No HyDE: Direct query embedding
- • Enterprise Focus: Built for corporate document chat
- • Azure Lock-in: Single cloud dependency
Our Advanced Architecture
- • AWS Bedrock: Llama 3.1 70B with cross-region inference
- • Pinecone: Specialized vector database with advanced filtering
- • Real-time Streaming: Server-Sent Events for instant responses
- • HyDE + Safety: Advanced search with guardrails
- • Domain-Specific: Optimized for legal/building codes
- • Multi-Cloud: AWS + Vercel + Pinecone ecosystem
3x Faster
Real-time streaming vs. batch processing
5x Cheaper
Llama 3.1 70B vs. GPT-4 pricing
40% Better
HyDE search accuracy vs. basic embedding
Key Technological Advantages
The system uses HyDE (Hypothetical Document Embeddings), an advanced AI search technique that dramatically improves search accuracy. Instead of just searching with your exact question, HyDE first generates a conceptual understanding of the type of information you're seeking, then finds the most relevant building code sections that match those concepts.
Built-in Safety Guardrails
The HyDE implementation includes specific safeguards to prevent the generation of false building code numbers, incorrect measurements, or fake regulatory citations. The system focuses on conceptual understanding rather than specific details, ensuring reliable search results.
How HyDE Works Safely
When you ask "What is the minimum ceiling height?", traditional HyDE might generate: "Section 1208.2 requires 8-foot ceilings..." But this system instead focuses on concepts like "interior space requirements, building dimensions, and residential standards" — avoiding specific numbers that could be incorrect.
This conceptual approach maintains HyDE's search improvements while preventing the generation of false code citations or measurements that could mislead users.
40% Better
Retrieval accuracy compared to traditional keyword search
Concept-Based
Understands the concepts behind your questions
Zero-Shot
Works without training on specific building code examples
The system uses Meta Llama 3.1 70B Instruct, one of the most advanced open-source LLMs available, providing accurate and cost-effective responses to building code questions through AWS Bedrock's cross-region inference.
Performance
- • Llama Efficiency: Higher throughput with cross-region inference eliminates throttling delays
- • Cached Vectors: Building codes are pre-embedded (one-time cost)
- • Enhanced Search: Safe HyDE + 10 relevant sections = better accuracy
- • Comprehensive Answers: 3,000 tokens = fewer follow-up questions
Sustainability
- • AWS Green Energy: Running on renewable-powered data centers
- • Efficient Processing: Optimized prompts reduce computational waste
- • Smart Caching: Reduces redundant processing
- • Cross-Region Optimization: Reduces latency and energy
Cost Per Question Breakdown
Safe conceptual analysis with guardrails (~1,000 tokens)
Converting safe HyDE analysis to searchable format
~1,000 similarity comparisons across building code database
~2,000 input + 3,000 output tokens
Why 200 Questions/Day Limit
At ~$0.0051 per question × 200 questions = ~$1.02/day in computational costs. This sustainable limit allows the service to remain free while maintaining quality with advanced HyDE technology with safety guardrails and comprehensive 3,000-token responses. Meta Llama 3.1 70B provides excellent accuracy and cost-effectiveness through AWS Bedrock's cross-region inference.
Environmental Impact Comparison
• Llama + HyDE Search: ~0.002 kWh (equivalent to 1 minute of LED bulb)
• Driving to City Hall: ~2-3 kWh (1,500x more energy)
• Printing Code Book: ~15 kWh (7,500x more energy)
• Multiple Follow-ups Saved: Comprehensive answers reduce total energy per project