How We'd Automate Candidate Brief Drafting for an International Tech Recruiter

The workflow as it typically runs

International tech recruiters operate a process that looks like judgment but contains a significant structured layer:

1. Client submits a role brief — job title, must-have stack, team context, salary band, location constraints
2. A consultant or researcher searches candidate databases (LinkedIn Recruiter, internal ATS) against the brief
3. For each shortlisted candidate, a consultant writes a candidate brief: why this person fits this role, what their background shows, what to watch for in an interview
4. The brief goes to the client before the candidate call or interview is booked

The judgment work is in the search and the fit assessment. The structured work is in the brief itself: taking the consultant's shortlist decision and translating it into a consistent, formatted document that gives the client the information they need.

In firms placing 10+ roles per month, brief drafting is a recurring time sink — the same format, the same sections, produced from the same input sources, once per shortlisted candidate.

Where the method applies

A candidate brief has a defined structure: candidate summary, relevant experience mapped to the role requirements, a note on cultural or team fit, compensation expectations, availability, and a recommended next step.

All of those sections draw from inputs the consultant already has: the candidate's LinkedIn or CV, the client's role brief, the consultant's shortlist notes. The AI system's job is to assemble those inputs into a formatted draft brief — the consultant then edits, corrects the fit assessment, and approves before the brief goes to the client.

This is a synthesis task with a defined output schema, drawing from structured inputs. It sits in the same category as the 30%-ruling packet summary: the AI produces the first version; the human corrects and approves.

The automation boundary we would draw

AI handles:

• Parsing the role brief to extract must-haves, nice-to-haves, and key context fields
• Extracting relevant experience sections from a candidate's LinkedIn profile or CV
• Drafting a structured brief in the firm's defined template format
• Flagging where the candidate's background doesn't match a must-have (so the consultant either addresses the gap in their notes or reconsiders the shortlist decision)

Consultant retains:

• The shortlist decision itself — who goes into the brief pipeline
• The fit judgment: is this person actually right for this client?
• The relationship context that doesn't appear in a CV
• Sign-off on every brief before client delivery

The AI produces a draft that is 70–80% complete. The consultant's time shifts from "write the brief from scratch" to "review and correct the draft." For consultants writing 5–10 briefs per week, that is a different working day — not because the AI is faster, but because reviewing and correcting is a different cognitive task than drafting from a blank page.

What the build looks like

The system needs four inputs to function: a role brief template (your standard format), a candidate profile source (LinkedIn URL, CV PDF, or ATS export), the shortlist decision (the consultant's notes on why this candidate), and the firm's brief template.

We build this as a managed agent with a structured output schema. The operator — the consultant or a research assistant — feeds in the inputs and reviews the output. The system runs on infrastructure designed for EU data residency. Every output is logged.

The operations map at the start of the engagement determines: what's the current brief volume, where is the quality inconsistency highest, and is the bottleneck in brief drafting specifically or upstream in the search. Those answers determine whether this implementation is the right first build.

What this teardown is not claiming

• We are not claiming to have delivered this for an international recruiter. We are describing the method.
• "70–80% complete" is a characterization of the task type, not a measured figure from a deployment.
• Brief quality depends on the quality of the consultant's input notes. If the shortlist notes are sparse, the draft brief will be sparse. Garbage-in applies here as it does everywhere.