A four-module AI layer that took a high-velocity affiliate marketing recruiting team from manual notes and ad hoc sourcing to standardised briefs, evaluations and branded assets, live in four weeks.
Project overview
An in-house recruiting team hiring 20 to 50 roles at once, affiliate managers, media buyers, performance marketers, ad ops and tech, was running on manual notes, recruiter-by-recruiter judgment and hand-built searches. Kick-off meetings turned into hours of reformatting, candidate scoring varied depending on who was in the room, and every banner or job post got rebuilt from scratch.
We integrated a four-module AI layer directly into the client's existing stack, automating intake, evaluation, sourcing and branding while keeping every hiring decision with the recruiter.
Challenges to resolve
Every hiring-manager meeting produced hours of notes that had to be reformatted into a usable brief by hand. Repeated across 15 to 40 open roles, that admin delayed vacancy launches and ate into candidate time.
Scoring depended on whichever recruiter ran the interview, with no shared competency baseline across managers, buyers and tech roles. Hire quality was hard to predict and candidates hard to compare.
Sourcing meant searching by hand and adding each profile to the ATS one at a time. That kept talent pools small and time-to-shortlist slow, no matter how many roles were open.
Every banner and job post was rebuilt from scratch per campaign, and profiles landed in the ATS ad hoc, often incomplete or duplicated, which made reporting unreliable.
Tech stack used
Four purpose-built modules wired into the client's existing recruiting workflow.
The backbone that routes each role through intake, evaluation, sourcing and branding, handing every decision back to the recruiter.
The models that read conversations and notes to draft briefs and structured evaluations, on bounded, reviewable outputs.
Automated candidate search wired straight into the existing ATS, writing scored profiles into the pipeline.
On-brand banners and job posts produced from a role's hooks and audience, with no designer in the loop.
The solution
The AI analyses the recorded hiring-manager conversation, extracts the business need, and auto-populates a vacancy brief with must-have versus nice-to-have criteria and seniority signals. What took hours now takes under 5 minutes, and the brief looks the same no matter who ran the intake.
The AI reads the kick-off form for that role, then turns the recruiter's interview notes into a standardised competency assessment with a risk section and strengths summary, comparable across every candidate and recruiter.
Reads the completed kick-off form and runs searches across channels, writing matching profiles into the ATS with a role-fit score and summary. The recruiter reviews and approves the pool before any outreach goes out.
Turns a role title, key hooks and target audience into on-brand recruitment visuals, so affiliate, media buyer and tech campaigns all look consistent without a designer.
Every module sticks to deterministic, bounded outputs rather than open-ended generation, and every AI result passes through a human review gate before it reaches a candidate or the market.
Why it matters
What took hours of reformatting now lands as a consistent, ready-to-use brief in under 5 minutes, regardless of who ran the intake.
Evaluations follow the same structure for every candidate and recruiter, so comparisons across affiliate, buyer and tech roles actually mean something.
The sourcer builds the shortlist automatically, so recruiters spend their time on conversations, not searches.
Banners and job posts come out consistent across every vertical, with no per-post design cycle.
"We were hiring across five different role types at once and every recruiter scored candidates differently. Now every brief, every evaluation and every job post comes out the same way, no matter who is running the process."