“Troogue was founded on the principle of decentralization of opportunities to enable enterprises and talent to connect remotely.” – Mr. Girish Ramadurgam, the CTO and Co-founder of Troogue
Since Troogue sells itself as an AI-driven matchmaker between enterprises and IT specialists, what kind of core signals and models do you use to match talent to projects?
Troogue’s AI-powered interview platform specifically formulates an interview questionnaire which is designed to gauge both the technical expertise and soft skills required for any identified role in an enterprise. Since the platform utilizes the three main forms of communication – video, audio and text – it can pinpoint and identify insights that are likely to be overlooked in a manual or face-to-face interview.
Even within technical skills, which are quite obviously the most important, our platform analyses them at a micro-level to determine in which areas do candidates have practical experience and depth of knowledge. So say, in addition to concept clarity, if a role demands skills in problem solving, experience of engaging with clients and stakeholders or ability to work at scale and teamwork, Troogue’s AI-powered platform will put together questions designed to identify talent which will be optimal for the role or project.
- Given the criticality of skills verification, what kind of processes, both technical and human, does Troogue draw upon to validate a candidate’s skills in order to prevent a mismatch and even fraud so that enterprises are ensured a consistent quality of talent?
We follow a triangulated interview format approach which adopts usage of different interview formats in order to determine a candidate’s suitability for a given role. This could include a structured interview wherein a candidate’s technical expertise and objective knowledge is evaluated. Then we have an unstructured interview format where questions are designed to elicit responses from a candidate’s real-life experience of situations encountered in project execution and decision-making skills. In addition, there’s a coding assessment which tests a candidate’s ability to apply theory into problem solving and engineering thinking.
Troogue’s inbuilt fraud prevention controls use a combination of tools, such as audio checks to detect anomalies, face detection during the interview, browser and device event detection, screen focus monitoring to detect context switching and monitoring of behavioural and cognitive cues during responses. For shortlisted candidates, extensive background checks, both online and manual, are conducted for identity verification and validation of employment history.
We also offer detailed feedback on each candidate to the enterprise, listing the strengths, areas of improvement and upskilling recommendations along with giving enterprises access to the full interview so that there’s complete transparency and trust in our candidate evaluation process.
- You’ve highlighted creating a sustainable and equitable future through Troogue. What does that exactly mean? (If it’s about environmental benefits from flexible gig engagement, how would Troogue measure the reduction in carbon footprint?)
Troogue was founded on the principle of decentralization of opportunities to enable enterprises and talent to connect remotely. This allowed a wider latitude for talent in Tier-2 and Tier-3 cities to monetise their skills by engaging with enterprises at a global level, obliterating the need for physical relocation to large tech hubs. That itself has a positive impact on carbon footprint as it reduces commuting and migration.
Remote working also helps in reducing and even eliminating the need for large office infrastructure which needs to be created for a conventional office work model, leading to further reduction on carbon footprint. The impact of this can be measured through a variety of means, such as number of remote engagements, commuting distance avoided, reduction in relocation travel and duration of remote work engagements.
Moreover, it’s not just environmental sustainability that stands to benefit – there’s also social sustainability as it allows talent to build a career from their hometown, allowing them to create a meaningful work-life balance and help spread economic opportunity and prosperity from a few mega-cities to secondary and tertiary towns.
- How do you see AI-driven evaluation platforms transforming the way enterprises identify and engage technical talent over the next few years?
Whereas conventional hiring focused more on resumes and short face-to-face interviews, allowing for a limited time to evaluate a candidate’s suitability, which in turn often led to high-new recruit turnover, AI-powered hiring platforms like Troogue focus on capability based talent evaluation. This means that talent will get evaluated more on demonstrated capabilities in problem solving, coding and real-life situations that have tested a candidate’s decision-making skills, rather than skillsets listed on a CV.
As such, enterprises save a lot of time and cost in sifting through stacks of CVs and engaging only with shortlisted candidates, who in turn are increasingly taking ownership of their profiles to make them more accurate so that they can establish their credibility with prospective employers.
This allows enterprises to depend increasingly on data driven signals instead of subjective evaluations which can often be prone to human biases, even unintended. Hiring cycles get reduced even as enterprise-talent matchmaking quality improves.
This will have a domino effect on team composition which can be geography-agnostic allowing enterprises to continually offer their services outside of their time-zones.
- There is a concern that since AI platforms are trained on historical human behavioural data, there could be an inadvertent bias that could creep in, resulting in unfair outcomes. How are you safeguarding against this and could you illustrate it with a specific example of a human-led bias that your platform has mitigated against?
Let me start with an example of a very common bias that creeps in during a conventional hiring or recruitment process. In conventional hiring, it usually happens that enterprises tend to be biased in favour of candidates from better known companies or those who have studied at elite institutions, even prior to evaluating their capability. This puts candidates from non-premier institutes, who are usually from smaller towns or cities and whose work experience may not include marquee corporate brand names instantly at a disadvantage. With our AI-powered platform, we eliminate this bias by evaluating a candidate’s interview and coding performance, enabling all candidates to compete on an equal footing and ensuring that talent selection is purely on merit.
So for instance, during the primary evaluation process, there will be no markers as to the geography, college education and companies worked with that might allow a bias to creep in.
Moreover, since AI-driven evaluation models are based on large volumes of interview interactions across roles and skills, they are able to focus on a candidate’s technical depth, problem solving skills, communication and reasoning abilities rather than demography or background.
