Sensay September Summit: The Event of the Year!
In a groundbreaking move that could reshape the future of hiring, Sensay’s CHRO, Alessandro Huaroto, deployed his own AI replica to streamline and scale the company’s recruitment process. What started as an internal pilot quickly became one of the most radical applications of AI in hiring to date. His Sensay replica has now screened over 50,000 candidates per one month - engaging applicants 24/7, asking personalized questions, and surfacing talent traditional filters might miss.
We sat down with Alessandro, to ask what it’s really like to use a digital version of yourself as the front line of recruitment - and what it means for the future of hiring.
When did you first start using your Sensay replica in your hiring process?
We initiated a pilot phase with the Sensay replica in Q1 2025, integrating it into early-stage candidate screenings. The full external launch tomorrow marks its expansion to all hiring stages, following refinements based on internal feedback.
What problem were you trying to solve - and why a replica instead of a traditional solution?
We faced two critical challenges: first, the sheer volume of applications (hundreds of thousands annually) made it impossible to personally engage with every candidate, and second, traditional applicant tracking systems (ATS) lacked the capacity to deliver personalized interactions or effectively mitigate unconscious bias.
The replica solves this by democratizing access - allowing every candidate to participate in a pre-interview screening process through 24/7 conversational assessments. Unlike static ATS tools, it scales engagement dynamically, identifies top talent through bias-aware analytics, and ensures no candidate is overlooked due to human bandwidth constraints. This approach balances fairness with efficiency.
We faced two critical challenges: first, the sheer volume of applications (hundreds of thousands annually) made it impossible to personally engage with every candidate, and second, traditional applicant tracking systems (ATS) lacked the capacity to deliver personalized interactions or effectively mitigate unconscious bias.
The replica solves this by democratizing access - allowing every candidate to participate in a pre-interview screening process through 24/7 conversational assessments. Unlike static ATS tools, it scales engagement dynamically, identifies top talent through bias-aware analytics, and ensures no candidate is overlooked due to human bandwidth constraints. This approach balances fairness with efficiency.
How does your replica handle candidate conversations and filtering?
It uses NLP to analyze responses for skills, cultural fit, and problem-solving agility. It also flags inconsistencies or red flags (e.g., mismatched competencies) for HR review and adapts follow-up questions in real time based on candidate profiles.
It uses NLP to analyze responses for skills, cultural fit, and problem-solving agility. It also flags inconsistencies or red flags (e.g., mismatched competencies) for HR review and adapts follow-up questions in real time based on candidate profiles.
What’s the candidate feedback been like?
Candidates have responded with 85% approval for the process’s transparency and efficiency, particularly valuing the replica’s ability to clarify role expectations upfront. As pioneers in deploying AI replicas for hiring - likely the first globally to do so - applicants are notably engaged, often proactively flagging inconsistencies or technical quirks for our team to refine. For example, early users highlighted occasional misinterpretations of nuanced answers, which led to updates in the NLP’s contextual analysis. This collaborative dynamic between candidates and our developers has been critical for iterative improvements. We also introduced hybrid live-AI sessions to address requests for human interaction in later stages.
Candidates have responded with 85% approval for the process’s transparency and efficiency, particularly valuing the replica’s ability to clarify role expectations upfront. As pioneers in deploying AI replicas for hiring - likely the first globally to do so - applicants are notably engaged, often proactively flagging inconsistencies or technical quirks for our team to refine. For example, early users highlighted occasional misinterpretations of nuanced answers, which led to updates in the NLP’s contextual analysis. This collaborative dynamic between candidates and our developers has been critical for iterative improvements. We also introduced hybrid live-AI sessions to address requests for human interaction in later stages.
How does the hiring process work with your Sensay replica?
Candidates interact directly with the replica via chat, where they can:
Candidates interact directly with the replica via chat, where they can:
- Discuss their background informally
- Answer personalized questions tailored to their experience and the role, generated using NLP analysis of their inputs and job requirements.
- Ask about company culture, Sensay’s hiring traits, or even explore unconventional paths to joining (e.g., “How do I bypass traditional applications?”).
The replica isn’t limited to hiring - it’s a gateway to discover opportunities at Sensay. However, for formal roles, it acts as a gatekeeper: only candidates who consistently demonstrate alignment with our success metrics (skills, cultural fit, problem-solving) trigger an automated Calendly invite to my calendar.
Fair warning: The replica’s scoring is strict - it prioritizes precision over leniency, so candidates often find it tougher to “impress” than a human screener. But that’s intentional - we want the best, not the smoothest talkers.
Fair warning: The replica’s scoring is strict - it prioritizes precision over leniency, so candidates often find it tougher to “impress” than a human screener. But that’s intentional - we want the best, not the smoothest talkers.
Has this changed how you think about scaling your team or evaluating talent?
Yes. We now process 3x more applicants without compromising quality, reducing time-to-hire by 40%. The replica’s data also highlights undervalued skills (e.g., adaptive learning over traditional credentials), reshaping our talent criteria.
Yes. We now process 3x more applicants without compromising quality, reducing time-to-hire by 40%. The replica’s data also highlights undervalued skills (e.g., adaptive learning over traditional credentials), reshaping our talent criteria.
How accurate or helpful has the AI been in making final recommendations?
The replica’s recommendations often align with hiring manager evaluations. When they don’t, it’s usually due to contextual factors like team dynamics. I treat its insights with the same strategic weight as recommendations from C-level leaders, integrating them into a collaborative decision-making framework.
The replica’s recommendations often align with hiring manager evaluations. When they don’t, it’s usually due to contextual factors like team dynamics. I treat its insights with the same strategic weight as recommendations from C-level leaders, integrating them into a collaborative decision-making framework.
Would you trust your replica to make a hire? Why or why not?
For high-volume, entry-level roles? Yes - with predefined success metrics. For leadership or niche technical roles, human judgment remains irreplaceable for assessing soft skills and strategic vision. The replica excels at filtering, not final calls… for now.
For high-volume, entry-level roles? Yes - with predefined success metrics. For leadership or niche technical roles, human judgment remains irreplaceable for assessing soft skills and strategic vision. The replica excels at filtering, not final calls… for now.
What’s something surprising your replica caught that you might have missed?
It identified candidates with non-linear career paths who outperformed in problem-solving tasks, challenging our bias toward traditional tenure. The replica’s outsider perspective forced us to rethink “ideal” candidate profiles. Turns out, sometimes you need an AI to remind humans that résumés are just the cover.
It identified candidates with non-linear career paths who outperformed in problem-solving tasks, challenging our bias toward traditional tenure. The replica’s outsider perspective forced us to rethink “ideal” candidate profiles. Turns out, sometimes you need an AI to remind humans that résumés are just the cover.
Where else in your business do you see replicas making an impact?
- Onboarding: Personalizing training paths based on real-time performance.
- Internal Mobility: Matching employees to projects using skill-behavior models.
- Employee Support: Replicas for managers to simulate tough conversations or DEI scenarios.
What would you tell other hiring managers or founders considering this tech?
Start with a closed pilot to test edge cases (e.g., atypical candidates). Pair AI with human oversight to balance efficiency and empathy. Transparency is key: candidates should know when they’re engaging with a replica and how data is used.Sensay’s replica isn’t just filtering résumés - it’s reshaping what we value in talent and how we find it. The future of hiring might just begin with a conversation… with yourself.
Start with a closed pilot to test edge cases (e.g., atypical candidates). Pair AI with human oversight to balance efficiency and empathy. Transparency is key: candidates should know when they’re engaging with a replica and how data is used.Sensay’s replica isn’t just filtering résumés - it’s reshaping what we value in talent and how we find it. The future of hiring might just begin with a conversation… with yourself.
