5 AI Workflows for Local SEO

Workflow 3: Competitive analysis with chained agents

Competitive analysis is one of my favorite AI use cases because AI is great at finding patterns across large datasets. The key is giving each agent one job.

Give each agent a job

Each agent completes one task before passing structured output to the next stage:

Agent 1, GBP categories: Looks at the categories for the business I’m working with, finds additional categories, and reviews competitor categories.

Agent 2, Location and service area pages: Looks at competitors’ location or service area pages, compares them to ours, and identifies differences.

Agent 3, Content gaps: If a tool shows we have content gaps, this agent helps me plan next steps based on search volume.

Consolidate into a final agent: The final agent combines those outputs to identify patterns and anything the earlier agents missed.

Collect data

Before AI analyzes anything, I collect structured data. For Google Business Profile analysis, this includes:

For location page analysis, I collect:

Page type
Internal links
Content structure compared to competitors
Historical organic performance

Run competitive analysis in four steps

The whole competitive analysis runs in four steps: define your dataset, pull the data, run patterns, and identify gaps.

Here’s an example prompt for GBP categories specifically:

“You are analyzing local SEO signals for [business type] in [city]. Use ONLY the data provided below. Do not draw on outside knowledge. Identify: 1) the most common primary category, 2) secondary categories in 3+ competitors, 3) any category patterns in the top 3 that differ from others. Return a structured table. Flag any business with a significantly different pattern.”

The workflow quickly surfaces patterns such as: