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AI Integration

DowntownDry

A directory of alcohol-free bars and venues, kept honest by a verification pipeline that pairs Google APIs with Gemini.

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DowntownDry website screenshot

The problem

What was wrong

Alcohol-free venues open and close quickly, and plenty of listings are wrong in a way that matters: a place marked alcohol-free that serves beer is worse than no listing at all. Checking thousands of venues by hand does not scale.

What we built

What we built

  • An address-first check that anchors each venue to its exact Google Places record before anything else runs.
  • Targeted Google searches per venue, with the snippets passed to Gemini to decide keep, remove or uncertain.
  • Category validation that catches flags like "alcohol-free" on venues that serve alcohol.
  • Generated descriptions and extracted contact details, written back to the database for review.
  • City pages across the country, with scripts to find and add cities that are missing.

Stack

What it runs on

  • Astro
  • Supabase
  • Tailwind CSS
  • Google Places and Search APIs
  • Gemini
  • Vercel

Results

The numbers

2,705

Pages in sitemap

What we'd tell another builder

One lesson

Language models do not browse. The pipeline searches first, hands the model real snippets, and asks for a decision with evidence. Anchoring on the exact address keeps it from verifying the wrong venue.

From the blog

Further reading

AI Integration

We Ran Our AI-Readiness Scanner on Our Own Sites. Here's What It Found.

We pointed our free AI-readiness scanner at the seven sites we run. One was blocking AI crawlers without us knowing, and none had an llms.txt.

Read the post

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