How to Reclaim 40% of Your Consultants’ Time Without Sacrificing Quality
2-3 minutes
Let’s be real: keyword matching is still the go-to method for filtering candidates in most ATS platforms — but it’s fundamentally broken.
On paper, it makes sense. Need a Java Developer with AWS experience? Search for “Java” and “AWS” in resumes, and you’re good to go, right?
Not even close.
The result? You either open the floodgates to a mountain of unqualified CVs or apply strict filters and miss out on top-tier talent.
Here’s why keyword matching fails fast:
- Terminology Isn’t Standardised: Candidates describe the same skill in different ways. One says “Amazon Web Services,” another says “AWS.” A third lists “EC2” or “S3” without ever mentioning AWS at all. Keyword filters miss all of that nuance.
- Context Beats Keywords—Every Time: “I led a migration to AWS infrastructure” shows real-world experience. Compare that to a candidate who simply lists “AWS” next to 19 other buzzwords. Which one would you rather shortlist?
- Great Candidates Undersell Themselves: Some of your best technical talent won’t write CVs tailored to your job spec. They’ll talk in specific, nuanced terms—stuff that keyword filters can’t catch.
- Keyword Gaming Is Real: Smart candidates know how to hack the system. They cram keywords in with no context just to get through automated filters. It works, but it wastes your consultants’ time.
We Put This to the Test: 1,000 DevOps Applications
With keyword matching, over 60% of “qualified” candidates weren’t. Worse still, 19 genuinely qualified people were rejected just because they didn’t write the job description word-for-word.
Real Example:
- Keyword Matching: REJECTED — Candidate had “containerisation experience with Docker” but didn’t say “Kubernetes.”
- Context-Aware AI: QUALIFIED — Recognised Docker experience + cloud orchestration as Kubernetes-adjacent
That candidate was placed and is now a top performer.
Why Context-Aware AI Wins
It doesn’t just scan for terms. It understands relationships between skills. If a candidate has worked in microservices architecture, the AI picks up on their likely exposure to concepts like service mesh—even if “Istio” isn’t mentioned.
But filtering is just the start.
If you really want to unlock 40% of your consultants’ time, you need end-to-end automation at the top of the funnel:
- Instant Qualification: Every application is scored in real-time, 24/7
- Full Evaluation: Every single applicant gets assessed, not just the ones that catch a consultant’s eye
- Personalised Candidate Responses: Tailored feedback at scale
- Structured Data in Your ATS: No more copy-pasting insights into notes
- Ongoing Learning: The system improves with every decision you make
This doesn’t replace recruiters—it amplifies them.
Consultants get to do what they’re great at: engaging people, building trust, and assessing fit. The tech handles the noise.
The Results:
- Consultants gain back 40% of their day
- Shortlists go out in 24 hours instead of days
- Every single CV gets assessed—no missed gems
- Candidates receive personalised updates (goodbye ghosting)
Imagine a typical day: instead of opening your laptop to a stack of CVs, you log in to a shortlist of pre-qualified candidates, ready to engage. You spend more time building pipelines and making placements, and less time sifting through noise.
This isn’t theory—it’s already live across agencies who’ve embraced the shift.
The question isn’t if AI will transform recruitment. It already has.
The real question is: Will your agency lead this change, or get left behind?
See the difference yourself. Start your journey today – Book a meeting with me.
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