Biradar et al. Reducing Time to Hire

Building on the findings of Biradar et al. (2024), which demonstrated up to an 85 percent reduction in time-to-hire through AI-driven automation, our own approach applies the same principles with a more focused, language-model-based design. While we have not yet published an internal study, our early pilot feedback and workflow simulations strongly align with the efficiencies outlined in Biradar’s analysis.

Rather than relying on rigid AI assessments or pre-built interview modules, our LLM is designed to interpret structured candidate data — experiences, projects, and hyperlinks — and translate it into actionable reasoning for recruiters. This approach eliminates the need for static exams or repetitive screening steps, allowing both employers and candidates to move through the process in a fraction of the usual time.

As we continue to collect operational data from ongoing integrations, our goal is to formalize a longitudinal study that quantifies these gains in the same format as Biradar et al. (2024). Doing so will allow us to benchmark our model’s real-world time savings against accredited academic findings, ensuring transparency and rigor in how we measure impact.