AI System for Structuring Candidate Applications Against Role Criteria

The problem

In day-to-day operations, CVs, applications and role criteria arrive through different systems and files, preventing the team from reviewing the same case in one view.

As a result, the link to source evidence can be lost while an evidence-linked candidate summary is prepared, leading to repeated checks and uncertainty about the basis for the decision.

Incomplete automation may overlook equivalent experience or a missing document, while incorrect automation risks biased exclusion.

What we set out to improve

• Time taken for the evidence-linked candidate summary draft to be ready for review
• Correct routing of records involving equivalent experience or a missing document to the appropriate specialist queue
• Record of suggestions corrected or rejected by the recruitment specialist

How the system works

1. 01 Retrieve CVs, applications and role criteria from authorised sources
2. 02 Verify the source system, record identifier and recency information
3. 03 Map the fields to the target process schema
4. 04 Prepare the evidence-linked candidate summary draft
5. 05 Compare the draft with business rules and source records
6. 06 Check for an exception: equivalent experience or a missing document
7. 07 Have the recruitment specialist approve, revise or reject the suggestion
8. 08 Write the input, suggestion, changes and final decision to the audit log

Methods we used

• Source-traceable data extraction for CVs, applications and role criteria
• Structured output that limits the evidence-linked candidate summary fields through business rules
• An exception gate that separates cases involving equivalent experience or a missing document
• A queue that shows the recruitment specialist the source evidence and suggestion together
• Duplicate-processing controls and a reversal record to reduce the risk of biased exclusion

Where people stay involved

Data and security

Only fields required for the task are processed from CVs, applications and role criteria. Source-system permissions are preserved, and personal or commercially sensitive data is minimised before being passed to the model. Every read, suggestion, human change and write to the target system is logged.

Who this suits

Frequently asked questions