Redefining Candidate Experience at Scale with Mochi

Scale is no longer the differentiator, experience is

In today’s hiring landscape, scale is no longer the differentiator, experience is. Organisations can attract thousands of candidates, but the real competitive advantage lies in how effectively they engage, assess, and learn from them.

Q1 data from Mochi, Maki’s AI voice screening solution, provides a clear view into how hiring is evolving. Across Q1, candidates were invited at scale, with over 90% successfully reached and nearly 60% engaging with the screening.

This reflects a shift away from process-heavy, manual screening and towards structured, experience-led systems that generate both efficiency and insight.

From outreach to engagement: candidates are choosing to participate

The data shows that candidates are not simply moving through a funnel passively. They are actively deciding whether the experience is worth their time.

That makes engagement a strategic metric. When the process is clear, relevant, and easy to complete, candidates are more likely to participate fully. With an average screening duration of around 8 minutes, Mochi shows that meaningful evaluation does not need to come at the expense of candidate experience.

This is reflected directly in how candidates describe the experience. Several noted that it “didn’t even feel like it was a bot,” highlighting how conversational design can remove friction and increase comfort from the outset.

This also aligns with broader market expectations. Research from organisations such as Gartner highlights that candidate trust in AI-driven hiring remains low when processes lack transparency. The implication is clear. AI in hiring must be structured, explainable, and designed with the candidate in mind.

Candidate feedback is becoming a core hiring metric

One of the strongest signals in the Q1 data is the volume and quality of candidate feedback. We captured structured feedback from over 5,000 candidates in Q1, across a high-volume hiring population, with an 84% feedback completion rate .

This matters because candidate experience is often discussed but rarely measured at scale. Mochi creates a direct feedback loop, giving organisations real-time insight into how candidates perceive the process.

Importantly, the feedback is not just quantitative, but qualitative. Candidates consistently describe the experience as “comfortable,” “clear,” and “engaging,” with one noting that it felt like “an amazingly comfortable way of giving a screening interview.”

Others highlighted the informational value of the interaction itself, sharing that they “got more information about the role and company” through the process. This reinforces a key shift: screening is no longer just an evaluation step, but an experience that informs and engages candidates.

Efficiency and experience are no longer in tension

The Q1 data shows that efficiency and experience can reinforce one another. A shorter, more focused screening experience helps candidates stay engaged while still giving hiring teams consistent, structured insight.

This challenges a long-standing assumption in recruitment. More time spent does not necessarily mean better evaluation. In high-volume hiring, long and repetitive processes often create fatigue rather than signal.

Mochi demonstrates a different model. By keeping the experience focused and conversational, organisations can reduce manual screening effort while improving the quality and consistency of candidate data.

The candidate perspective reinforces this. Feedback such as “everything is good, happy to go further” reflects a process that feels smooth, efficient, and forward-moving rather than burdensome.

This aligns with broader market perspectives from organisations such as McKinsey, which emphasise that the real value of AI comes from redesigning workflows, not simply automating existing ones.

Fairness and consistency are becoming measurable

As hiring processes scale, fairness becomes a central concern. What this Q1 data shows is that structured, AI-enabled screening can improve consistency rather than compromise it.

~90% of candidates reported that the process felt fair, reinforcing the strength of a standardised and transparent evaluation approach.

This perception is echoed directly in candidate feedback, with comments such as “it was a good interview, it felt fair,” demonstrating that consistency is not just a design principle, but something candidates actively experience.

Every candidate is assessed against the same criteria, in the same format, reducing variability and limiting subjective bias early in the funnel. At the same time, recruiters retain full visibility through transcripts, audio, and structured outputs, ensuring human oversight remains central.

For executives, this is a critical shift. Fairness is no longer an abstract principle or compliance requirement. It becomes something that can be designed into the process, measured through candidate feedback, and continuously improved.

Candidate experience directly shapes brand perception

Candidate experience is increasingly acting as a proxy for brand perception. Every interaction in the hiring journey influences how candidates view the organisation, regardless of outcome.

The Q1 data shows a strong positive signal. ~95% of candidates said the screening improved their perception of the company, demonstrating that a well-designed AI experience can enhance employer brand rather than detract from it.

This is reflected in how candidates interpret the use of technology itself. Some noted that they “liked that the company is open to using new technology,” while others felt that the AI demonstrated a “profound knowledge of the company.”

These reactions are significant. They show that when implemented well, AI does not distance candidates from the organisation. It can actually reinforce perceptions of innovation, clarity, and professionalism.

In a market where candidate sentiment towards AI hiring can be mixed, this distinction matters. Organisations that prioritise transparency, clarity, and experience are able to turn AI from a potential risk into a brand differentiator.

As Deloitte highlights in its Human Capital Trends research, organisations that successfully adopt AI are those that balance technological advancement with human outcomes.

Designing for how candidates actually engage

The behavioural data reinforces the importance of designing for real-world usage. The majority of candidates completed the process on desktop, while a meaningful proportion engaged via mobile. Language distribution also highlights the need for flexibility, with most candidates completing in English alongside growing multilingual participation .

This underlines a simple but critical point. Candidate experience is shaped not just by what is asked, but by how and where it is delivered.

Organisations that optimise for accessibility, device flexibility, and language inclusivity are better positioned to engage diverse talent pools at scale.

From process to continuous learning system

Taken together, the Q1 data points to a broader transformation. Hiring is moving from a static, process-driven model to a dynamic, feedback-led system.

Every candidate interaction generates insight. Every piece of feedback enables organisations to refine and improve their approach.

This creates a continuous loop of optimisation, where hiring becomes progressively more efficient, more consistent, and more aligned with both candidate expectations and business needs.

The future of hiring is experience-led and data-driven

The organisations leading in this space are not simply processing more candidates. They are building systems that allow them to understand, engage, and evaluate talent at scale.

Mochi demonstrates that AI voice screening can support this shift when it is designed around transparency, consistency, and candidate experience.

The future of hiring is not about replacing human decision making. It is about equipping organisations with better data, better processes, and better insight to make those decisions with confidence.

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