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A healthcare technology company specializing in revenue cycle management software for hospital systems had a staffing problem that was limiting their product roadmap. Their time-to-hire for engineering roles had stretched to 73 days on average — a figure that had crept up over three years as their talent bar increased while their recruiting process stayed the same. They were working with three legacy agencies. The quality of shortlists was inconsistent. The agency coordinators turned over frequently. And the placement retention at 12 months was running at 71% — meaning roughly one in three hires wasn't working out within a year.
The cost of a failed placement in this environment was significant. Between severance, recruiter fees for the replacement search, and the productivity loss during the vacant period, each failed placement was costing the company approximately $120,000 to $180,000 in fully-loaded terms. At their hiring velocity — 18–22 engineering hires per year — even a marginal improvement in retention had substantial financial implications.
The company's VP of Engineering articulated the core problem clearly: "We weren't getting shortlists of candidates who were likely to succeed here. We were getting shortlists of candidates who had the right keywords on their resume. Those aren't the same thing."
The company transitioned to Exolios for global engineering staffing in Q3 2025, replacing their three legacy agency relationships. The transition was phased — Exolios ran on three open roles in parallel with the existing agencies for six weeks, allowing a direct comparison before full transition.
The role analysis process was the first visible difference. Rather than starting with the job description, Exolios conducted a structured intake with the VP of Engineering and two senior engineering managers. The questions weren't about technical requirements — those were documented in the JD. They were about the environment: how decisions get made, what the collaboration pattern looks like between product and engineering, what the typical career path looks like for engineers who have thrived, and what the characteristics were of the hires who hadn't worked out.
That last question produced the most useful data. Three of the four underperforming hires in the prior 18 months had come from large enterprise software companies and struggled with the autonomy and ambiguity inherent in a mid-size health-tech environment. Two had strong formal qualifications in health data standards (HL7, FHIR) but limited experience with the fast-iteration product culture the company operated in. These patterns became explicit signals in the matching model — not disqualifying factors, but weighted inputs that adjusted candidate rankings.
The initial candidate pool for the three pilot roles totaled 1,840 profiles. The AI matching process scored candidates across seven dimensions: technical depth, health-tech domain familiarity, company size trajectory (whether their career history suggested increasing or decreasing company scale), autonomy indicators from work history patterns, collaboration signals from project descriptions and role structures, technical communication quality from written work samples, and trajectory alignment with the specific role's expected growth path.
The shortlists were notably different from what the legacy agencies had been producing. Several candidates that would have been screened out by keyword matching — a senior engineer from a fintech company with adjacent but not identical health data experience, a developer who had built revenue management tooling in a non-healthcare context — ranked highly on the full multi-dimensional score and were included in the shortlist with explicit rationale.
The hiring managers' response to the shortlists was immediate. "These candidates make sense," the VP of Engineering noted after the first shortlist review. "I can read the rationale and understand why each person is on the list. That's completely different from the agency shortlists, which felt like a wall of resumes with no explanation of why they were selected."
At the 12-month mark following full transition to Exolios, the numbers were definitive. Average time-to-hire had dropped from 73 days to 26 days — a 65% reduction. The agency coordination overhead — managing three vendor relationships, normalizing their different processes, running parallel candidate tracking — had been replaced by a single point of accountability. Hiring manager satisfaction scores (measured via internal quarterly surveys) had improved from 58 to 84 out of 100.
The retention figure was the most significant outcome. Of the 19 hires placed by Exolios in the 12 months following full transition, 17 were still in role at the 12-month mark — a 91% retention rate, compared to the 71% the company had been running under the legacy model. At the company's average replacement cost of $150,000, the improvement in retention alone produced approximately $600,000 in avoided cost over the year.
An unexpected secondary outcome was a measurable improvement in the diversity of the hired cohort. The prior 18 months of agency-driven hiring had produced a cohort that was 78% male and predominantly from the same 4–5 universities in the hiring managers' professional networks. The 12 months of AI-matched hiring produced a cohort that was 61% male, with substantially greater variation in educational background and prior employer type.
The hiring managers noted that the shortlist composition had changed, but that the quality bar felt the same or higher. The AI matching was surfacing candidates from paths that keyword-based screening had systematically missed — not because those candidates were under-qualified, but because their paths didn't produce the same resume signals that the legacy screening had optimized for.
When asked what they would change, the VP of Engineering pointed to the phased transition. "We should have done a full transition from the start. The six-week parallel period was useful for generating confidence, but the legacy agency relationships were already a drag on speed during that period. In retrospect, the data we already had on agency performance was sufficient. We didn't need six more weeks of it."
The second point was the intake process. The structured role analysis that Exolios conducted at the start of the engagement was thorough, but the VP of Engineering noted that the team should have done more work internally before the intake — specifically, documenting the characteristics of the engineers who had worked out and those who hadn't, in greater detail. "The data from that retrospective made the matching model better. We had more of it available than we used."
