Automatically score closed Intercom conversations for support quality

Reviews every closed Intercom conversation with AI, scores the support quality, and logs results to a spreadsheet.

How the work actually flows

It branches. Exactly one path is taken.

Pattern: Exclusive Choice (4)

flowchart TD trig>"Intercom conversation closed"]:::trig s0["pull conversation transcript"]:::svc s1["summarize transcript"]:::task s2["AI scores conversation"]:::task s3[("log scores to sheet")]:::store trig --> s0 s0 --> s1 s1 --> s2 s2 --> s3 gx{"× is score low"}:::gate s3 --> gx p00["send coaching feedback"]:::task gx -->|"low score"| p00 p10["no action needed"]:::task gx -->|"acceptable score"| p10 p00 --> out p10 --> out out[/"QA score and feedback logged"/]:::out pay{{"consistent support quality checks"}}:::pay 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 stepAn outside serviceA record or sheetOne path onlyResultPayoff
Build size
Advanced

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

Business functions
AI Agents & Autonomous SystemsSpreadsheet & Database OpsAPI & Webhook IntegrationCustomer Support & Ticketing
Connects
IntercomOpenAIGoogle Sheets

The problem it solves

You want to know if your support team is giving fast, clear, and helpful answers, but reading through every conversation to check is impossible at scale. Problems with tone, urgency, or follow-through often go unnoticed until a customer complains.

Who it fits

Customer support managers who need consistent quality reviews without manual spot-checks.

How it works

  1. When a support conversation is closed in Intercom, the automation is triggered
  2. The full conversation is pulled in and summarized into a clear transcript
  3. GPT reviews the exchange for response time, clarity, tone, and resolution
  4. Scores and notes are logged in a Google Sheet
  5. Low-scoring conversations get coaching-style feedback for the agent
What you get

Quality scores ready for a coach to use

Every closed Intercom conversation gets scored for tone, clarity, and resolution, with notes logged in a sheet so you can spot coaching opportunities fast.

What you get

A running Google Sheet of QA scores and feedback for every closed support conversation.

What you need

An Intercom account, an OpenAI API key, and a Google Sheets account.

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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