When TechTree’s AI agent delivered a precision-mapped shortlist of 3,000+ engineers, the foundation was set. But sourcing alone doesn’t close roles. What comes next — outreach, engagement, interviews, and offers — determines whether pipelines convert into hires.
This is where many technology-driven hiring solutions stall. AI can surface names, but it can’t build relationships, manage objections, or persuade top engineers to change jobs. That’s where TechTree’s human recruiter network became the critical multiplier.
Within 24 hours of project launch, TechTree deployed 20 handpicked recruiters with specific experience in engineering and AI-focused markets. Unlike generalist agency recruiters, these specialists knew the language, pain points, and motivations of developers and data scientists.
Their mission wasn’t to replace the AI but to work in tandem with it:
This feedback loop ensured that with every round of outreach, the AI got smarter and the recruiters got more efficient.
The numbers tell the story of how effective the model was:
In recruitment terms, that level of conversion is almost unheard of. In traditional models, recruiters expect a significant proportion of sourced candidates to drop off due to misalignment, lack of interest, or poor outreach. Here, the precision of AI sourcing combined with targeted recruiter engagement virtually eliminated wasted effort.
The success lay in the complementary strengths of each component:
Trust: Building rapport with candidates.
Persuasion: Overcoming objections and selling the opportunity.
Context: Understanding subtle cues around motivation and fit.
Together, they created a recruitment flywheel: AI generated precision leads, recruiters engaged meaningfully, and recruiter feedback improved AI outputs.
In today’s hyper-competitive engineering market, candidate experience can make or break a hiring campaign. Many engineers are bombarded with templated outreach and disengage quickly.
By contrast, TechTree’s recruiters, armed with AI insights, approached candidates with personalized and relevant messages. They could address career motivations directly — whether it was compensation alignment, opportunities for innovation, or the prestige of working on AI at scale.
This combination of personalization and speed meant candidates felt valued and informed from the start, contributing to the high offer-acceptance rates achieved later in the project.
This model didn’t just produce activity — it created momentum. That momentum set the stage for a breakthrough on one of the most stubborn hiring metrics of all: time-to-hire.
In Article 4, we’ll explore how TechTree and DevTalent slashed average time-to-hire by over 70% — from industry norms of 45–60 days down to just 11.4.
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.
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.
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.
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.
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.
For the fintech, every unfilled engineering role had a cascading effect:
These delays weren’t just operational issues — they were strategic risks with direct business consequences.
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:
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.