When one of the world’s leading fintech “decacorns” set out to build two new AI R&D hubs, the mission looked bold but achievable on paper. The mandate was clear: hire 200 highly skilled software engineers in just six months. These hires would power the company’s entry into advanced artificial intelligence research and development, laying the foundation for global innovation and keeping pace with fast-moving competitors.

But the early results were sobering. After months of work, four established staffing agencies engaged on the project had collectively managed just 22 hires. That’s an average of only 5–6 hires per agency. At that pace, the 200-engineer target wasn’t just unlikely — it was mathematically impossible.

The story of why these agencies failed is a lesson for every talent leader facing high-volume, high-stakes hiring.

Why the Traditional Approach Broke Down

1. Aggressive Hiring Timelines vs. Industry Norms

The benchmark for time-to-hire in engineering has long hovered around 45–60 days per candidate. That cycle might work for steady, incremental hiring. But when multiplied by 200 roles with a six-month deadline, it became untenable. The math simply didn’t work: even at the faster end of 45 days, the project would spill over into the following year.

2. Specialized Technical Demands

This wasn’t about filling generic developer seats. The fintech required specialists proficient in Java, Python, Kubernetes, and machine learning frameworks. These are not only competitive skill sets, but ones in which top candidates are already highly sought after. Finding them required wide-scale talent mapping and the ability to filter candidates beyond surface-level resume keywords.

3. The Limitations of Agency Sourcing

Traditional recruitment agencies rely heavily on existing candidate databases, LinkedIn searches, and reactive applications. They operate in silos, often duplicating effort, and lack the infrastructure to systematically scan global talent markets. That means speed and scale are inherently capped, and quality often becomes a secondary consideration.

4. Candidate Experience Bottlenecks

Finally, agencies focused on pushing volume outreach into crowded markets. Engineers, especially those in-demand, were hit with generic messages and inconsistent communication. In industries where candidates juggle multiple offers, poor experience directly reduces acceptance rates.

The Hidden Costs of Delay

For the fintech, every unfilled engineering role had a cascading effect:

• Productivity loss: Existing teams were stretched, leading to burnout and reduced delivery capacity.
• Innovation slowdown: Key AI initiatives were stalled due to lack of hands on deck.
• Competitive risk: In a global race toward AI breakthroughs, being months behind competitors was not an option.

These delays weren’t just operational issues — they were strategic risks with direct business consequences.

The Takeaway for Talent Leaders

This case highlights a reality that many talent acquisition (TA) teams face today: traditional recruitment models cannot keep pace with modern hiring challenges.

When scale, speed, and quality are all non-negotiable, a new model is needed — one that combines:

• AI-powered talent mapping to scan global markets at scale.
• Data-driven shortlisting to prioritize high-fit candidates.
• Human recruiter networks to build trust, engagement, and close offers quickly.
  
  

KPI Insight Recap

• Traditional time-to-hire: 45–60 days
• Required time-to-hire: <15 days
• Agency results: 22 hires in months
• Target: 200 hires in six months

This was the point at which DevTalent, a specialist recruitment partner engaged by the fintech, turned to TechTree. What followed was a collaboration that redefined what’s possible in enterprise-scale hiring.

In the next article, we’ll unpack the first stage of that success: how AI-first talent mapping generated 10× more qualified candidates than manual sourcing.