Analyze hotel guest reviews with AI and flag negative ones

Every new review in Airtable gets an automatic sentiment score, summary, and confidence rating from AI.

How the work actually flows

It branches. Exactly one path is taken.

Pattern: Exclusive Choice (4) · Simple Merge (5)

flowchart TD trig[\"new review added"\]:::trigdata s0["send review text to AI"]:::task s1["receive sentiment and summary"]:::task s2["compare rating to sentiment"]:::task s3[("update Airtable record")]:::store trig --> s0 s0 --> s1 s1 --> s2 gx{"× does rating match sentiment"}:::gate s2 --> gx p00["flag record as mismatch"]:::task gx -->|"mismatch found"| p00 p10["leave record unflagged"]:::task gx -->|"ratings align"| p10 jn{"○ update record"}:::gate p00 --> jn p10 --> jn jn --> s3 out[/"review scored and flagged if mismatched"/]:::out pay{{"catch guest problems automatically"}}:::pay s3 --> out out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
Starts itA stepA record or sheetOne path onlyPaths rejoinResultPayoff
Build size
Advanced

A larger build with multiple systems, AI reasoning, and custom rules.

Business functions
Spreadsheet & Database OpsSurvey & Feedback
Connects
AirtableOpenAI
Featured in

The problem it solves

Reading every guest review to gauge sentiment and catch problems early takes time your team doesn't have, and ratings alone don't tell the whole story. Negative experiences can slip through unnoticed until they show up in a public review or a lost repeat guest.

Who it fits

Hotel and hospitality teams tracking guest reviews in Airtable who want automatic sentiment insights.

How it works

  1. A new review is added to Airtable
  2. The review text is sent to AI for analysis
  3. AI returns a sentiment, short summary, and confidence score
  4. Mismatches between the star rating and sentiment are flagged
  5. The Airtable record is updated and marked as processed
What you get

Guest sentiment you can track over time

Every new review gets an automatic sentiment score and summary, giving you a running read on how guests really feel.

What you get

An updated Airtable record with sentiment, summary, and confidence score for every review.

What you need

An Airtable account and an OpenAI API key.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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