The case for going blind
Most hiring teams want to be fair. The problem is that fairness under time pressure is hard. Names, photos, school logos and accents leak signal that most interviewers would, on a calm day, agree shouldn't matter.
Anonymised screening removes that signal at the point of shortlisting. It's a small change that produces measurable differences in who reaches the interview stage.
What we anonymise in TwikRecruit
- Name and photo on initial CV review.
- School / university names (toggle).
- Free-text demographic markers in cover letters (best-effort).
- Location below city level.
AI summaries that don't decide for you
The point of an AI summary isn't to rank candidates. It's to compress a CV into the same structured shape every time so the recruiter is comparing like with like:
- Relevant experience.
- Demonstrated outcomes.
- Skills against the JD.
- Gaps and ambiguities worth probing in interview.
The recruiter still decides. The AI just removes the bit of the job that benefits no one.
Does it work?
Internal data from teams using TwikRecruit's anonymised flow shows more consistent shortlist composition and faster time-to-shortlist — without changing the bar.
A reasonable starting point
If you're new to anonymised hiring, start with the first sift. Keep the rest of the process exactly as it is, and measure for three months. The change is usually obvious.