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ZoningPal: Toronto Zoning By-Law AI
This talk details building an AI engine to normalize 30 years of inconsistent Toronto zoning by-laws from messy PDFs into structured, auditable data.
We’ll walk through how we built ZoningPal’s data ingestion and normalization engine that transforms scattered, inconsistent municipal zoning data into structured, query-able intelligence. This is a messy, real-world AI application dealing with:
PDF hell: Non-searchable scans, broken links, cross-references across 100+ documents
Data chaos: 30+ years of amendments, overlays, exceptions, and site-specific rules with zero standardization
Version drift: Multiple conflicting sources that don’t agree with each other
Accuracy requirements: Every answer needs to link back to the exact by-law clause
We’ll demo the live system, show actual running code, and share what worked (and what spectacularly failed) when building an AI system that municipal planners and architects trust.
AI instantly analyzes Toronto zoning bylaws, generating comprehensive property reports.