How a Global Fashion Unicorn Used MokaHR's AI ATS to Spot Fit Talent at Scale

A rapidly growing fashion-retail unicorn—10K+ employees, hiring both new graduates and experienced talent across 150+ countries—faced the classic scaling problem: thousands of interviews, hundreds of interviewers, and patchy, manual note-taking that buried the best hires under noise. The company needed a way to turn fragmented interview data into clear, actionable signals so HR could make fast, fair, and repeatable decisions. 

The Challenge: Inconsistent notes hid signals across regions

• Limited insight into cohort differences. Relying on manual notes made it hard to capture the distinct competencies and industry perspectives of new graduates versus experienced hires. Without clear, comparable signals across cohorts, HR could not reliably identify under-represented strengths or design role-specific development pipelines—constraining talent diversity and long-term workforce planning.

• Interview management lacked systematic analysis. Interview questions and probes were not tracked or evaluated in a structured way, so the company could not assess whether interviews covered the right competencies or where assessment gaps repeated across teams and countries. That left interviewer training unfocused and reactive rather than evidence-driven.

• Global scale amplified fragmentation. With 10,000+ employees and hiring across 150+ countries for both graduates and experienced talent, regional practices and time-zone logistics multiplied the noise: data was fragmented, hard to search, and slow to turn into shared hiring signals.

The Solution: Detect the differences and fit talent with roles with Moka Eva

Multi-dimensional talent insight

The Global Fashion Unicorn leveraged MokaHR’s AI to structure interview content, uncovering distinct perspectives and strengths across career stages and functions — from new graduates to experienced hires, and from fashion to logistics and technical fields. With these insights, the company was able to move beyond anecdotal impressions, make cohort differences actionable, and design role-appropriate pipelines that strengthened workforce diversity.

Data-driven interviewer enablement

The Global Fashion Unicorn also used Moka's AI interview summary to analyse interview questions, aggregate recurring themes, and identify gaps in assessment coverage. By acting on these signals, HR teams launched targeted training sessions, refined question libraries, and built a more professional interviewer cohort. This systematic approach empowered interviewers to evaluate both technical expertise and soft skills with greater consistency and reliability.

Streamlined Global Hiring

Our client tackled the challenges of cross-timezone coordination and fragmented interview management by adopting Moka AI ATS. Instead of juggling scattered notes and endless scheduling conflicts, their teams now manage all candidate data in a single platform. Calendar integrations and AI chatbots handle scheduling and follow-ups automatically, ensuring interviews run smoothly across regions.

The Results:Scaled globally with 1,700+ interviewers and 19,000+ interviews accelerated

By adopting Moka Eva, the Global Fashion Unicorn scaled adoption rapidly across its organization:

• 1,700+ interviewers actively used AI Interview Summary, which Increases interview feedback rates, evidence-based decision-making, and enhances their interviewing skills.

• 19,000+ interviews were accelerated and transformed into searchable, decision-ready insights, giving hiring managers timely evidence to act on, while ameliorating candidate experience..

With this scale, the company not only spotted differences between new graduates and experienced hires but also aligned talent with the right roles, strengthening both hiring efficiency and workforce diversity at global scale.

Conclusion: Timely feedback plus data-backed assessments Help Hire with Certainty

By facilitating its hiring process with Moka Eva, the Global Fashion Unicorn turned fragmented, manual interview data into a reliable decision-making engine. What once slowed down hiring—uneven notes, inconsistent questions, and dispersed global practices—was transformed into structured, comparable insights at scale.