Automatically score interview feedback for quality and bias

Reads interviewer notes, scores them for quality and bias, and messages the interviewer with tips to improve.

How the work actually flows

It branches. Exactly one path is taken; runs once per each feedback entry.

Pattern: Exclusive Choice (4) · Multiple Instances without Synchronization (12)

flowchart TD trig(("someone runs the check")):::human s0[("read feedback entries")]:::store s1[["score for quality and bias"]]:::mi s2["flag vague language"]:::task s3["send slack report"]:::task trig --> s0 s0 -->|"one per each feedback entry"| s1 s1 --> s2 s2 --> s3 gx{"× is the score low"}:::gate s3 --> gx p00["send training resources"]:::task gx -->|"low score"| p00 p10["no further action"]:::task gx -->|"adequate score"| p10 p00 --> out p10 --> out out[/"quality score and bias flags"/]:::out pay{{"catches weak interview feedback early"}}:::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
A stepRuns once per itemA personA record or sheetOne path onlyResultPayoff
Build size
Advanced

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

Business functions
Messaging & NotificationsSpreadsheet & Database OpsReporting & AnalyticsSurvey & FeedbackEducation & TrainingSecurity & Compliance
Connects
Google SheetsSlackOpenAI

The problem it solves

You know some interview feedback is too vague to be useful, but you don't have time to review every write-up for bias or missing detail. Weak feedback slips through, and by the time you notice a pattern of shallow evaluations, hiring decisions have already been made.

Who it fits

Hiring teams and HR leaders who rely on structured interviewer feedback to make fair hiring decisions.

How it works

  1. Someone runs the check on feedback entries in a spreadsheet
  2. AI reads each write-up and scores it for specificity, bias, and depth
  3. The system flags vague language and calculates a weighted quality score
  4. The interviewer gets a Slack message with the score and specific feedback
  5. Anyone scoring low gets sent training resources on better interviewing
What you get

Interview writeups flagged for bias and depth

You get every interviewer's notes scored for specificity and bias, with tailored coaching tips sent straight to them in Slack.

What you get

A quality score, bias flags, and a Slack report for each interviewer, plus training resources for low scorers.

What you need

A Google Sheets account, an OpenAI API key, and a Slack workspace.

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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