Inspiration
We wanted to tackle one of the root blockers to solving housing, zoning, and climate challenges in San Francisco: broken, fragmented land data. Existing tools like SF PIM work for parcel lookup but not for analysis or scale. We envisioned a system where anyone — from urban planners to community advocates — could explore the city’s land in real time and ask meaningful, city-wide questions.
What it does
Seekly unifies parcel data from official sources, cleans data, and exposes it through a public API. It includes an interactive map for exploring parcels and a natural language AI assistant that lets users ask questions like “Show me vacant lots near BART” or “Which parcels in SoMa allow mixed use over 5 stories?” Seekly makes complex land data accessible, searchable, and programmable.
How we built it
Aggregated parcels API from official sources .
Cleaned and standardized the data into a consistent schema
Built a backend API to serve parcel queries
Developed a React-based map UI for interactive exploration
Integrated an LLM-powered AI assistant that translates user queries into API calls and displays responses as maps or tables
Challenges we ran into
Data inconsistencies across agencies required custom cleaning and merging logic
Mapping historical zoning codes to their modern equivalents was complex
Translating vague user questions into precise spatial queries pushed the limits of the AI assistant
Designing an experience that worked for both planners and casual users was a constant balancing act
Accomplishments that we're proud of
Built a working platform that unifies and serves real parcels
Designed a natural language interface that works live with our API
Created a clean, scalable backend architecture ready to handle the city’s 300,000+ parcels
Delivered a real-time demo where users can ask spatial questions and see instant results
What we learned
Public data is powerful, but messy — cleaning is often more important than collecting
Planners and technologists think in very different ways, and designing for both requires empathy and iteration
Natural language interfaces can unlock access to data that was previously hidden behind maps and code
What's next for Seekly
Scale to all 300,000+ parcels in San Francisco
Add more data layers: mobility, equity, climate risk, permits, and infrastructure
Improve AI assistant’s reasoning with structured query translation and better context memory
Open the API to civic developers, nonprofits, and planning departments
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