A shortlist is only useful if it is short.
ReferTech Solution is an IT recruitment and staffing firm. We place technology talent — permanent, contract and executive — for companies that need the hire to be right the first time.
Most recruiters cannot tell a good engineer from a good interviewee.
That is the whole problem with technology hiring. A recruiter matching keywords on a CV cannot assess whether someone is actually good at the work, so the filtering gets pushed onto your engineers — who then lose a day a week to first-round screens that were never going to go anywhere.
We are built the other way round. Our recruiters specialise by technology area rather than covering the whole market, and the screening happens before anyone reaches your panel. Three names instead of thirty, each one worth the hour.
Three things we do not trade away.
Shorter, not longer
Three or four names, each one screened. A recruiter who sends thirty CVs has forwarded them, not assessed them.
Straight with both sides
Candidates get the real salary band and the real reason the role is open. Clients get told when a role is priced or scoped wrong.
We stay past the offer
Notice periods, counter-offers and a check-in after they start. The guarantee period only means something if people actually stay.
No account managers between you and the recruiter.
You talk to the recruiter running your mandate, not to someone relaying messages. The person who took the brief is the person sourcing, screening and calling you with the bad news when there is any.
Recruiters carry fewer mandates than the industry norm, on purpose. It is the only way the screening stays real — someone running twenty roles at once is forwarding CVs, whatever they tell you.
The people scoping the work are the people doing it.
Founder & CEO
To be confirmed
Technical direction and client partnerships
Head of Engineering
To be confirmed
Delivery, architecture and engineering standards
Head of Talent
To be confirmed
Staffing, executive search and candidate quality
Head of AI
To be confirmed
Agents, evaluation and applied machine learning