Reducing Time-to-Fill by 71% with AI-Powered Talent Intelligence

A fast-growing mid-market SaaS company needed to scale engineering from 40 to 120 developers in nine months to hit its roadmap. An internal team of three was drowning — 68 days to fill technical roles, offer acceptance under 45%, and 20+ hours a week lost to manual resume screening. Passive candidates went unseen entirely. Two critical cloud infrastructure roles had been open for five months, stalling a major platform migration, while top candidates slipped to faster-moving competitors before a first interview could even be scheduled.

We deployed our AI-enhanced RPO model: a managed recruiting team augmented by proprietary ML sourcing. Custom models trained on the client's own hire and performance data scored candidate fit across technical skill, career trajectory, and cultural alignment. The sourcing engine scanned millions of profiles across LinkedIn, GitHub, Stack Overflow, and academic databases, surfacing passive talent that keyword search missed. Automated workflows handled parsing, qualification, and interview scheduling, while a recruitment intelligence dashboard gave leadership live visibility into pipeline health and bottlenecks. Everything integrated with the client's Greenhouse ATS via API — no platform migration required.

• Average time-to-fill fell 71%, from 68 days to 19.5 days for technical roles
• 82 engineers hired in nine months, beating the target of 80 ahead of schedule
• Offer acceptance climbed from 45% to 78% on better matching and a faster process
• Recruiter screening workload cut 60%, freeing the internal team for candidate experience
• 3.5x more qualified passive candidates in the pipeline via ML sourcing across four platforms