Screening produces a reading order, not a decision
Volume screening makes hiring unjust. By the time two hundred applications arrive, the reviewer gives later applications only a small part of the reading time given to the first fifty.
You automate this so every application is assessed the same way, against the same stated requirements, regardless of its place in the pile.
The risk should be stated, not ignored. A model that evaluates applications can reproduce training patterns that have nothing to do with the role. The university's reputation, the employer's prestige, and the wording of the application can be mistaken for merit. If the assessment stays on the written requirements and each judgment is traceable, the output is reviewable rather than trustworthy on its own.
You get a list with reasons, and a person then reads the applications. What changes is the reading order, not who makes the decision.
How the ActionFlow runs on the canvas
Requirements are set in advance in a Google Sheets or Airtable node. Each role's requirements sit in individual rows, marked essential, desirable, or not to be evaluated, so the criteria are recorded before assessment starts.
A Webhook trigger receives the application and passes it to a Document Upload node. A PDF4me node extracts the PDF.
A Generate Object node assesses each requirement row by row. For each requirement it returns met, partially met, or not evidenced, with the passage the result is based on. A disagreement can be checked because the evaluation is per requirement rather than an overall score.
A Math node counts evidenced must-haves. That count sets the order without a composite score, which would hide how the criteria are weighted.
A Sort node orders the queue. A Filter node puts applications that have no must-have evidence into a separate group rather than rejecting them, because a poorly written CV and an unqualified candidate look the same when processed automatically.
An Airtable node shows the assessment beside each application. A Slack node notifies the hiring manager that the queue is ready.
The flow rejects nothing. Every application reaches the person in the specified order, with the notes.
Nodes this ActionFlow uses
- Airtable (Integration): stores the requirements as rows, including those that are not assessed.
- Webhook (Trigger): receives each application as it arrives.
- Document Upload (Data): takes the CV. PDF4me (Data) extracts it.
- Generate Object (AI Core): assesses per requirement with the supporting passage, not an overall score.
- Math (Util): counts evidenced must-haves to produce an ordering without a hidden weighting.
- Sort (Data): orders the reading queue.
- Filter (Data): separates rather than rejects applications with nothing evidenced.
- Slack (Communication): notifies the hiring manager that the queue is ready to read.
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