Feed your AI with business data from Google Maps

Your AI is only as good as its input

Everyone wants to point an AI at their market and get a ranked list of who to call first. The catch is that models like ChatGPT and Claude cannot open a map, walk a neighborhood, or read a review page for you. They reason over the text you provide. Feed your AI clean business data and it becomes genuinely useful. Feed it a messy paste and it hallucinates.

Google Maps is the richest public source of local business data on earth: names, categories, ratings, review counts, phone numbers, websites, and addresses for tens of millions of companies. The problem has never been the data. It has been getting that data out of the map and into a file your AI can actually read.

That is the entire job of this workflow. Capture the businesses that match your target, export them in a structured format, and hand the file to your model with a clear instruction. The rest is prompting.

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CSV, Excel, or JSON: which one to hand your AI

All three formats carry the same business data, but they suit different tasks. The rule of thumb: CSV for reasoning, Excel for humans, JSON for structure. Here is how they compare when the reader is an AI model.

For most people the answer is CSV. It is plain text, every model parses it without complaint, and you can attach it or paste a few hundred rows directly into a conversation. The CSV format is a documented standard, which is exactly why it travels so well between tools.

Reach for JSON when a single business carries a list inside it, for example ten recent reviews or a set of opening hours. JSON keeps that structure intact so the model does not have to guess where one review ends and the next begins. Vonsel exports all three from the same captured search, so you never have to reshape data by hand.

From map to model in four steps

Here is the full loop, the way a working salesperson or agency actually runs it. No code required, and no scraping scripts to babysit.

What to ask once the data is in

Once your file is attached, the model becomes an analyst that never gets tired. A few prompts that pay off immediately:

Score and rank. "Here is a CSV of restaurants. Score each one from 1 to 10 as a prospect for a website redesign service, weighting a low rating, a high review count, and a missing website. Return the top 20 as a table." The model reads your columns and applies the logic across every row.

Segment automatically. "Group these businesses into three tiers by review count and rating, and describe the outreach angle for each tier." This turns a flat list into a plan in seconds.

Draft outreach. "For each business in the top tier, write a two sentence opener that references its category and rating." Because the data is structured, the model personalizes at the row level instead of writing one generic template.

If you want to go further and run this as a repeatable pipeline, our guide on analyzing leads with Claude Code shows how to loop a whole export through a model programmatically. If you are curious where these fields originate, Google documents them in its Places API reference, the same catalog of business attributes you are handing to the model.

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Clean data in, useful answers out

The difference between a helpful AI response and a wrong one usually comes down to the export, not the prompt. A few habits keep your data model ready.

Trim to the fields the task needs. If you are scoring on rating and review count, you do not need to feed the model the full address string for every row. Fewer, cleaner columns produce sharper answers and cost fewer tokens.

Keep headers consistent. A column called rating in one export and stars in the next will confuse a model comparing two files. A one click export from a single tool keeps naming stable, which matters more than most people expect.

Mind the source and the rules. Public business details are generally low risk to collect, but how you store and contact people is governed by privacy law. The official GDPR overview is worth a read, and we cover the specifics in our guide on whether it is legal to scrape Google Maps. If you want to understand why fresh, self collected data outperforms purchased lists, see Google Maps data vs bought lists.

You can always export raw and analyze elsewhere, or skip the round trip entirely. Inside Vonsel the same captured businesses already come with AI analyzed reviews and per business email drafts, so the AI step happens on the data without a manual handoff.