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Policy & Regulation4 min read

Designing AI for payroll and admissions calls for deterministic guardrails

The author describes building two AI‑enabled products—an Australian payroll compliance platform and a college admissions tool—where a single mistake can cost money or alter a student’s future. In the U.S., one in five payrolls contains an error, averaging $291 per incident before litigation, and the EU AI Act classifies AI used for admissions and employment management as high‑risk.

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Key points

  • One in five U.S. payrolls contains an error, costing an average $291 per mistake before litigation.
  • The EU AI Act classifies AI used for admissions and employment management as high‑risk, demanding deterministic safeguards.
  • The author’s platforms separate rule‑based logic from language‑model interpretation, logging rule versions and model calls for traceability.

To keep risk low, the systems place deterministic business rules outside the language model. Experts first encode pay rules and admission criteria in structured formats, generate all possible scenarios, and expose only the relevant rule set to the model for retrieval and explanation. The model cannot create or modify rules, and every response is logged with the rule version, model details, and tool calls, allowing human reviewers to trace and verify outputs. When errors still occur, the author tightened the guardrails, moving more logic into explicit code and flagging uncertain cases for human review.

Full story from Unite.AI · by Baber Javed, Founder & CEO, Square63Open source ↗

Where the Language Model Should Stop: Designing AI for Payroll, Admissions, and Other Domains That Punish Mistakes

Unite.AI · 23 October 2026

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This text was published by Unite.AI and written by Baber Javed, Founder & CEO, Square63. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

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The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.

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