I recently launched https://locas.dev, a side project that uses AI to answer one of the most critical questions in real estate and business: “Is this location a good fit for what I want to do?”

What It Does Locas.dev takes a single input – a natural language question like:

“Is Downtown Chicago a good place to buy land?”

“Would Miami Beach be a good spot for opening a restaurant?”

Then it fetches real-world geo-tagged data like school ratings, hospitals, police stations, restaurants, air quality, transit nodes, green space, and even pollen levels — processes it using a custom-built LLM + rules engine — and responds with a structured, explainable breakdown.

Each answer includes:

Ratings across dimensions like services, amenities, transport, environment, and competition

Google Maps links to nearby landmarks

Human-readable insights with pros and cons

A clear summary and final verdict

Tech Stack Built with Python, LangChain, and a custom vector lookup engine

LLM-driven reasoning with deterministic safety layers

Google Maps, OpenStreetMap, and public datasets for grounding

Fully stateless – just one GET request per analysis

Why I Built It As someone working in AI and education, I constantly run into the challenge of selecting the “right place” for programs, centers, or even partnerships. Existing solutions are either too manual, map-based, or lack narrative reasoning. I wanted something that thinks about a location the way a human researcher or consultant would — but on-demand, in seconds.

Try It There are a few public demos on the homepage — feel free to test it with your own questions.

→ https://locas.dev

Would love feedback from fellow HNers! I'm especially curious about:

Potential use cases you see

What features you'd want next (e.g., scoring your own address, neighborhood comparisons, investment suggestions)

Integrations (Slack, Notion, etc.)

Thanks for reading! – Azhar