Signil

“Daton listened very carefully to the experience of the ETB with this annual recruitment process and delivered an excellent solution which both appealed to applicants and the shortlisting committee in helping them to determine candidates for interview and appointment. Daton provided exemplary ongoing support throughout the process”

— Donncha Ó Treasaigh
Director of Schools with Limerick and Clare Education and Training Board

Volume vs. the Deadline

The client faced a time intensive review process. With dozens of applications, each containing over 1,000 words of narrative, the HR team faced 30+ hours of manual reading, and that created a significant bottleneck. The timeline offered no relief: the role needed to be filled before the start of the new academic term, leaving no room for delays.

Signil answered with instant 24/7 screening, analysing and ranking every submission as it arrived. The HR backlog was eliminated entirely, and the team had an up to date, ranked list from start to finish, rather than a pile of reading waiting for them at the close of applications.

The Fairness Mandate

As a public body, ensuring an equitable, transparent and unbiased process was non-negotiable. Speed could not come at the cost of a decision the ETB would have to stand behind.

Signil delivered a fair, defensible and high-quality shortlist. It saved 25+ staff-hours and, critically, produced a list that was independently validated by the expert panel.

The Candidate Experience

At the front of the funnel sat a sub-optimal candidate experience. A cumbersome application process risked losing the best candidates before they even finished applying, shrinking an already competitive talent pool.

Best practice user experience design afforded a 75% completion rate, against a 10-50% sector average, through a best in class application form system. The pool the panel selected from was wider and stronger as a result.

Validating the Solution

To build trust and measure true performance, we conducted a blind comparison between Signil and results obtained by manual application review.

First, Signil's automated analysis ran on its own. As applications arrived, the AI continuously parsed and ranked them without any human intervention. Alongside it, the traditional review process continued as normal: an independent interview panel, completely unaware of the AI's rankings, manually reviewed the same anonymised applications and created their own shortlist. Only afterwards were the results of both shortlists analysed to assess the efficacy of Signil's analysis.

The Results

The two processes agreed. Signil and the human panel had identical first picks, and the top 10 showed a 90% correlation with the manual review panel. The gains in speed and candidate engagement followed.

90% accuracy

Identical first picks, with 90% correlation across the top 10

6X faster application review

Manual candidate review: 30 hrs → Signil candidate review: <5 hrs

7X faster shortlist generation

Manual shortlisting process: 7 days → With Signil: <24 hrs

75% application completion rate

Industry average: 10-50% → Signil completion rate: 75%

25+ staff-hours saved

Mitigating bias

Underpinning all of it is a multi-layered defence to mitigate bias and ensure fairness at every stage of the hiring process.

Applications are anonymised by default: we strip all personal identifiers, including name, photo, DOB and address, that could trigger bias. Bias prevention goes further, preventing our AI from using data that may act as a proxy for gender, age or ethnicity. The hiring team is always in control, because the AI provides a recommendation while the final hiring decision always rests with them, ensuring accountability. And continuous audits keep it honest over time, as we regularly test our models for demographic parity and disparate impact.