As part of conversations with our Tech HR India partners, we had Kartik Mandaville, CEO and Founder of Springworks as we explored the transformative potential of automated and nuanced reference checks in modern hiring practices. Our conversation delves into how AI can streamline hiring and backgorund verification process, enhance accuracy, and mitigate bias. Kartik helped elaborate on the benefit of robust, tech-driven reference checks for both employers and candidates, while addressing potential challenges and features necessary for a modern, tech-driven hiring process. Below are the excerpts from the conversation:
How do you see reference checks be a powerful tool to support HR leaders in hiring quickly while ensuring the right talent is hired?
Traditionally, reference checks have been a straightforward, standardized process. HR teams send candidates a basic form—often through Google or Microsoft—containing about 10 generic questions for their references to fill out. These questions typically cover just the surface-level and doesn’t provide meaningful insights into a candidate’s true potential. Despite its limitations, this outdated method is still widely used by most companies.
The more evolved approach involves the hiring manager having a dynamic, real-time conversation with the reference. Over a 15-minute call, they adapt their questions as the conversation progresses. For example, if the reference mentions leadership skills, they dig deeper by asking for specific examples, like how the candidate handled a project crisis or team conflict. This method provides a deeper, more personalized understanding of the candidate’s behavior, but it requires more time and effort, with each call lasting 15 to 30 minutes.
What is SpringVerify doing differently?
At SpringVerify, with AI-powered reference checks, we’re scaling the depth of the personalized approach while also driving fair and bias-free hiring decisions. While HR teams can only manage a limited number of calls per day, AI enables these in-depth, tailored checks for every candidate automatically. AI adjusts questions in real time, probing deeper into how the candidate works and whether they’re a good fit for the role. By focusing on objective data and behavioral insights, this process minimizes unconscious bias and ensures a fairer evaluation. It goes beyond simple fact-checking—it helps companies assess if the candidate is truly the right match for the position.
We believe that every role has unique requirements. Sometimes, you need a self-starter who can work independently, while other times, you need someone who excels within an established team structure. AI customizes the reference checks to highlight these nuanced qualities, ensuring HR leaders are hiring the right person for the business’s specific needs at that moment, without the influence of bias.
How is Springworks enabling HR leaders to leverage automated reference checks and behavioral MCQs to offer deeper insights into candidates?
At Springworks, we’re enabling HR leaders to leverage automated reference checks and behavioral insights by completely transforming the process. We begin by gathering essential details—like the name, email, and background of both the reference and the candidate—along with specifics about the role, such as team size and structure, to craft tailored questions.
What sets us apart is that we don’t rely on traditional multiple-choice questions. Instead, our AI processes detailed responses from references in real time, adjusting follow-up questions dynamically. This results in a personalized, in-depth reference check that goes beyond surface-level information. To save HR leaders time, the AI then generates a concise, attribute-based report. In just 30 to 45 seconds, they can access deep insights that surpass what a resume or interview typically reveals. This approach not only automates the process but also delivers a richer, more comprehensive understanding of the candidate’s fit for the role.
What are the most significant challenges in the hiring process, particularly in terms of efficiency, fairness, and finding the right talent? What role do you see AI playing in addressing these challenges?
The biggest challenge in hiring today is speed. HR teams are great at identifying the right talent, but doing it at scale is the issue. It’s difficult to ask individual questions and create personalized reference checks because the process is incredibly time-consuming. You often face delays—like when a candidate isn’t available or a reference cancels last minute—and that can waste significant time.
This is where AI steps in. AI has no resource limitations—it can manage multiple checks at once, and if a reference cancels, there’s no need to reschedule or waste time. You only pay for the AI when it’s actively working, which makes it much more efficient. Plus, AI removes bias—there’s no influence from factors like voice, gender, or any other personal traits.
By leveraging AI, HR leaders can improve the quality of hires at scale. AI works continuously, day in and day out, generating consistent results. It evaluates candidates on key attributes like collaboration, persistence, humility, enthusiasm, communication, and problem-solving, providing a comprehensive view of each candidate’s potential.
What features or capabilities would you consider essential in an AI hiring solution to address the dynamic nature of hiring needs for organization?
One essential feature of an AI hiring solution is its ability to handle tasks at scale while also delivering easily digestible insights. For example, if you’re dealing with a 30-minute voice recording, it’s challenging for anyone to go through it and extract meaningful information. But if AI can convert that into a clear, concise report, it becomes much easier to review and take action on.
At Springworks, a key feature we offer is this ability to transform complex, time-consuming tasks into straightforward, actionable insights. Our AI generates detailed, PDF-based reports that are easy to understand, allowing HR leaders to quickly assess candidates and make informed decisions without getting bogged down by lengthy processes. This balance between scalability and simplicity is what makes our solution stand out.