By the seven-week mark, the DevTalent x TechTree collaboration had achieved something remarkable: 31 elite engineers onboarded in less than two months.
That number wasn’t just impressive in isolation. It represented 16% of the fintech’s 200-engineer target, achieved in under a quarter of the allotted time. Compared to the four traditional agencies that had collectively delivered just 22 hires in several months, TechTree’s results were transformative.
This outcome was more than a win; it was validation of a new model of recruitment. AI-driven sourcing combined with human recruiter expertise had delivered speed, scale, and quality simultaneously — something traditional approaches consistently struggle to achieve.
Just as important as the hires made were the offers accepted. With an 80% offer-acceptance rate, TechTree proved that speed and precision in matching didn’t come at the cost of candidate alignment. High acceptance meant less wasted effort, fewer renegotiations, and faster onboarding.
The project achieved a 4:1 interview-to-offer ratio, reflecting the quality of candidates put forward. In many traditional campaigns, ratios can run 8:1 or worse — indicating poor pre-qualification and wasted recruiter and hiring manager time.
By rapidly onboarding 31 engineers, the fintech was able to ramp up its AI hub activities months ahead of schedule. This meant critical research initiatives weren’t delayed, giving the company a competitive edge in its innovation roadmap.
DevTalent’s internal recruiters, freed from the grind of sourcing and qualification, focused on high-value activities like candidate interviews and client management. This shift didn’t just improve results — it boosted recruiter morale and sustainability.
While traditional agencies scrambled to fill roles, TechTree delivered 5.2 times more hires in the same timeframe. For the fintech’s leadership, this proved the superiority of the AI + recruiter network model.
One of the most overlooked factors in recruitment success is candidate experience. Here, it was a clear differentiator. Candidates consistently reported:
These weren’t vanity metrics — they were proof points that the combination of AI and human recruiters can deliver outcomes that outperform traditional models across every dimension.
This wasn’t just about filling 31 roles. It was about proving that enterprise-scale hiring can be faster, smarter, and more candidate-centric when technology and people work together.
In the next article, we’ll distill the entire project into five key lessons every talent leader can apply to their own hiring challenges.