AI Review Intelligence
Analyze Google and Yelp reviews to surface recurring themes, sentiment trends, and competitor benchmarks.
What you need to build this
Connect these tools, give the AI this data, expect these outputs.
Connect
APIs & tools to set up
OAuth, API keys, or webhooks depending on the tool.
Gather
Data sources to pull from
Give to AI
Inputs per run
- Your Google Business Profile reviews (last 90 days)
- Your Yelp reviews (last 90 days)
- Competitor review samples from Yelp Fusion API
- Business context: services offered, price range, location
Expect
AI outputs
- Theme clusters for praise and complaints with example quotes
- Month-over-month sentiment trend
- Competitor rating comparison table
- Top 3 operational recommendations based on review patterns
Business problem
Business owners read reviews reactively but never systematically analyze patterns. Recurring complaints about wait times or staff go unnoticed until they show up in multiple 1-star reviews.
Expected outcome
Weekly report showing: top praise themes, top complaint themes, sentiment trend vs. last month, and how your ratings compare to 3 nearby competitors.
Who it's for: Local businesses with 20+ reviews across Google and Yelp who want operational insights from customer feedback, not just star ratings.
Required data sources
- Customer ReviewsView access method →
What data goes into the AI
- Your Google Business Profile reviews (last 90 days)
- Your Yelp reviews (last 90 days)
- Competitor review samples from Yelp Fusion API
- Business context: services offered, price range, location
What the AI should output
- Theme clusters for praise and complaints with example quotes
- Month-over-month sentiment trend
- Competitor rating comparison table
- Top 3 operational recommendations based on review patterns
APIs needed to automate this
- Google Business Profile APIAuth & setup →
- Yelp Fusion APIAuth & setup →
- OpenAI APIAuth & setup →
System behavior
- Runs weekly on a schedule
- Read-only — does not post responses (see Review Response Assistant)
- Aggregates across platforms into unified theme view
- Includes verbatim quotes as evidence for each theme
- Flags new complaint themes not seen in prior reports
No-code vs low-code vs custom
No-code
Lowest effort
Non-technical owners, quick validation, under 50 runs/day
3 options listed
Low-code
Medium effort
Operators comfortable with Zapier/n8n, need more control
2 options listed
Custom build
Highest effort
High volume, custom logic, or strict data privacy requirements
3 options listed
| No-code options | Low-code options | Custom build stack |
|---|---|---|
| ChatGPT with pasted review exports (manual weekly) | n8n weekly pull from Google + Yelp → OpenAI analysis → email report | Scheduled job pulling reviews via APIs |
| Birdeye or Podium built-in sentiment dashboards | Relevance AI review analysis agent | OpenAI for theme clustering and summarization |
| Google Sheets + manual review paste + GPT formula | — | Report delivered via email or Notion page |
Start with no-code to validate the workflow. Move to low-code when you hit rate limits or need branching logic. Custom build when volume, privacy, or integration depth requires it.
Step-by-step implementation
- 1Connect Google Business Profile and Yelp Fusion APIs
- 2Identify 3 local competitors for benchmarking
- 3Pull 90 days of reviews for your business and competitors
- 4Run OpenAI analysis with structured output template
- 5Deliver weekly report every Monday morning
- 6Share complaint themes with operations team for action items
Risks and limitations
- Small review volumes produce unreliable theme clusters
- Competitor benchmarking may use businesses that aren't true competitors
- Yelp API terms restrict how review text can be stored and displayed
- AI may over-index on recent negative reviews vs. long-term trends
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