Build a local SEO audit with AI Export Google Maps data, then let ChatGPT and Claude Code score it

Feed your AI with real business data

A local SEO audit answers one question: how visible is a business on Google Maps, and where is it leaking rankings. The inputs are all public. Categories, review counts, average rating, whether the profile links to a website, address consistency, and photo volume all sit right there on the map.

The slow part has always been collecting those inputs at scale and turning them into a verdict. That is exactly the job AI is good at, once you give it structured data instead of screenshots. So the workflow is simple: scrape the map, export a file, and let the model read it.

The Vonsel extension handles the capture. You open Google Maps, run a search like dentists in Austin, and the extension collects every listing into a table you can export. From there the file becomes fuel for whatever AI you prefer. This is the core of the feed your AI with business data idea.

Which fields your audit needs

Before the AI can score anything, the file has to carry the right columns. A local SEO audit leans on a handful of fields that the extension pulls straight from each listing. The cleaner the export, the sharper the analysis, so keep the columns consistent across every capture.

Google itself explains how these elements feed local ranking in its guide to improving your local ranking on Google. Relevance, distance, and prominence are the three pillars, and prominence is where reviews, categories, and web presence do their work. Your export gives the AI a proxy for all three. For the mechanics of the file itself, see export CSV, XLSX, and JSON options.

The no-code audit in ChatGPT

ChatGPT is where most people start, because it takes a file upload and a plain-language prompt. Drag your CSV or Excel export into the chat, then describe the audit you want. No scripts, no setup.

A prompt that works well looks like this: You are a local SEO consultant. Score each business in this file from 0 to 100 on Google Maps visibility using category fit, review count, average rating, website presence, and NAP completeness. Return a table with the score, the single biggest weakness, and one recommended fix per business.

ChatGPT reads the columns, applies consistent logic across every row, and hands back a ranked table. Ask it to sort ascending and you instantly see which businesses are weakest, which is precisely who an SEO agency wants to pitch. You can go deeper with the same file: pull the review text through a second pass to surface complaints, then cross-reference it with our guide to Google review sentiment analysis. If you want more prompt patterns for list work, the walkthrough on how to analyze lead lists with ChatGPT covers the follow-up questions worth asking.

Repeatable audits with Claude Code

ChatGPT is perfect for one file. When you audit an entire city, or run the same audit every month for a client, you want something repeatable. That is where Claude Code earns its place.

Point Claude Code at your exported JSON and describe the scoring rules once. It writes a small script that reads the file, applies the weights, and outputs a scored spreadsheet or a formatted report. Because the logic lives in code, the next export runs through the exact same rules, so your March audit and your July audit are directly comparable.

JSON is the better format here. It preserves nested fields like the review breakdown and keeps types intact, which matters when a script is doing the reading rather than a chat window. The companion piece on analyzing leads with Claude Code shows the same pattern applied to prospecting. You can take it a step further and build a lead dashboard with Claude Code that renders the audit as a live map of weak and strong profiles.

A typical Claude Code request: point it at austin-dentists.json, tell it to flag every business with no website, fewer than 25 reviews, or a rating below 4.0, and ask for the output grouped by severity. It handles the whole loop and you review the result.

Codex, local models, and structured output

Where the Vonsel dashboard closes the loop

A scored spreadsheet is a finding, not a sale. The point of a local SEO audit is usually to sell a service: a new website, review management, a Google Business Profile cleanup. That means the businesses with the worst scores are your hottest leads.

This is where the Vonsel dashboard takes over from the raw file. The same captures live inside a mapped CRM, so a weak-profile list becomes a territory you can see. Email Intelligence drafts a tailored message per business built on its actual gaps, and you can add reviews to captured businesses or reopen a scrape later to enrich it. The audit stops being a PDF and becomes a working, contextualized database of prospects with a reason to buy.

That is the whole arc. The extension is the means, the AI is the analyst, and the dashboard is where the finding turns into revenue. Browse more of these builds in the AI Workflows hub.