Three years before Leap Tiger™ had a name, it was a spreadsheet. A long, dense, manually maintained spreadsheet that one of our senior recruiters updated every Friday afternoon — a running scorecard of candidate fit attributes mapped against the active requisitions inside a large telecom MSP we were supporting.
That spreadsheet worked. It was the reason our submitted-to-interview rate was consistently above 38% when the program average across all vendors sat around 22%. But it was also fragile, single-threaded, and completely dependent on one person's pattern recognition.
When we finally turned that logic into software, the results surprised even us.
The problem with "fast" recruiting
Enterprise MSP programs measure their vendors on speed-to-submit as a proxy for engagement. The faster a vendor submits, the assumption goes, the more active they are. That logic is backwards.
Speed-to-submit measures how quickly a recruiter can paste a résumé into a VMS portal. It says nothing about fit, compliance readiness, or interest level. A recruiter who submits 12 candidates in 4 hours is almost certainly submitting candidates who have not been screened for the actual requirements — they are spraying and praying that one of the 12 gets traction.
The downstream cost is substantial. A candidate who reaches a hiring manager interview without proper vetting wastes 60–90 minutes of stakeholder time and adds noise to the signal. In regulated industries like financial services or telecom, an unvetted candidate who passes the résumé screen but fails the compliance background check costs the MSP an average of 18 days of pipeline time.
"Speed-to-submit measures how quickly a recruiter can paste a résumé. It says nothing about fit. The programs that optimize for speed usually get the candidates they deserve."
How Leap Tiger™ changes the math
Leap Tiger™ does three things that the Friday spreadsheet could not do at scale.
First, it parses the requisition, not just the job title. Most ATS systems keyword-match on job title and a handful of hard skills. Leap Tiger™ reads the full requisition — including manager comments, team context, and compliance requirements — and builds a weighted fit profile with 40+ attributes. A "Senior Network Engineer" at a telecom carrier has a very different attribute set than a "Senior Network Engineer" at a cloud SaaS company. The platform knows the difference.
Second, it matches against a live, enriched candidate database. Our 20-year candidate database is structured, tagged by vertical experience, compliance clearance history, and recency of skill use — not just what appears on a résumé. When Leap Tiger™ surfaces a candidate, that candidate has been cross-referenced against current salary bands, geographic availability, and prior placement outcomes.
Third, it scores the shortlist, not just the candidate. The output is not a ranked list of candidates; it is a ranked list of candidate-role pairs, scored on fit against the specific requisition, not against a generic job category. A candidate who is a 96% fit for this role at this client may be a 61% fit for the same title at a different client.
What changed operationally
When Leap Tiger™ went live inside the telecom MSP program, the first thing that changed was not the metrics — it was where our recruiters spent their time.
Before the platform, a recruiter spent roughly 65% of their time on sourcing: searching job boards, refreshing candidate pipelines, manually reading résumés. After the platform, that ratio inverted. Sourcing dropped to approximately 25% of recruiter time. The other 75% moved to validation, relationship management, and candidate preparation.
The downstream effect on quality was immediate. Our submitted-to-interview rate, which had been holding at 38–40% for several years on manual processes, climbed to 48% within two quarters. Our interviewed-to-offer rate held steady at 48%, meaning the quality improvement was happening at the screening stage, not through a pipeline flush.
What AI matching does not do
Every staffing firm now claims to be "AI-powered." Most of what passes for AI in this industry is a keyword-matching engine bolted onto a 1990s ATS architecture. We want to be precise about what Leap Tiger™ does — and what it does not do — because overclaiming is exactly the kind of behavior that erodes trust with enterprise procurement teams.
Leap Tiger™ does not replace recruiter judgment. It compresses the time a recruiter needs to get to a high-confidence shortlist. The recruiter still makes the call on whether a candidate is a genuine fit for the team culture, the manager's working style, and the project timeline. Those judgments require human context that no platform can replicate.
Leap Tiger™ does not guarantee placement speed. It reduces the time wasted on low-probability candidates. In a high-volume MSP program, that reduction compounds across hundreds of requisitions and produces the 2.3× time-to-hire improvement we observed. In a low-volume executive search, the effect is smaller because the bottleneck is rarely sourcing.
Leap Tiger™ does not eliminate the need for recruiter training. Our 52-week training program remains the primary driver of recruiter quality. Leap Tiger™ makes a trained recruiter faster; it does not make an untrained recruiter reliable.
Implications for MSP program managers
If you run an enterprise MSP program and you are evaluating staffing vendors, here are the questions that Leap Tiger™'s performance history suggests you should be asking:
- What is the vendor's submitted-to-interview rate, not just their time-to-submit? Speed without quality is noise.
- How does the vendor's shortlist quality hold up across different hiring managers on the same program? Consistency across stakeholders reflects process, not luck.
- What happens when a submitted candidate fails a compliance screen? Does the vendor have a ready replacement, or does the requisition go back to day one?
- What is the vendor's fill ratio on your program — not overall, but specifically on your hardest requisitions?
Our fill ratio at SiteOne Landscape Supply is 48%. Our fill ratio at Cox Communications across a 14-year engagement held top-3 among 30+ vendors, including firms ten times our size. That consistency across very different clients, in very different verticals, is what we attribute to Leap Tiger™ plus trained recruiter judgment — not to either element alone.
"A tool that makes a great recruiter faster is an advantage. A tool that tries to replace a recruiter is a liability."
The bottom line
AI matching reduces time-to-hire when it is built on real candidate data, trained on genuine placement outcomes, and paired with recruiters who know how to use the shortlist as a starting point, not an ending point.
The Friday spreadsheet that started all of this worked because it encoded 20 years of placement pattern recognition into a reusable, repeatable process. Leap Tiger™ is that process at scale — running continuously, improving with every placement, and freeing our recruiters to do the part of recruiting that AI cannot: building the trust that gets a candidate to say yes.
If you want to see Leap Tiger™ applied to your open requisitions, schedule a 15-min consult and we will run a live demo against real job descriptions.