For a global leader in lithium battery manufacturing, rapid engineering-led growth created an unintended bottleneck: hiring crisis.
As candidate pipelines became overwhelmed and interview data remained siloed and inconsistent, the firm struggled with mounting recruiting debt and erratic onboarding decisions.
By adopting MokaHR, the company pivoted from chaotic, manual volume-screening to a precise, AI-driven talent workflow that turned recruiting into a quantifiable competitive advantage.
Challenge
Urgent hiring pressure but slow fills
Our client faced explosive demand for engineers and technicians which make recruiters overwhelmed. Large, noisy resume inflows clogged shortlists and lengthened screening cycles — recruiters spent time sifting rather than deciding, so open roles stayed unfilled longer.
Fragmented interview notes undermined probation planning
Interview feedback was scattered and fragmented, making it hard for managers to link probation tasks to interview performance and track priorities. These information gaps often caused them to miss the critical observation window during the probation period.Critical observation windows were missed and onboarding adjustments were reactive, not planned.
Solution
MokaHR automatically identifies high-fit candidates
To tackle the resume overload, the client enabled MokaHR’s AI resume screening to convert raw resumes into ranked, role-specific fit scores. Powered by MokaHR, the hiring team extracts required skills, seniority signals and contextual keywords from job templates. Our client leveraged Moka HR’s AI to automatically identify and prioritize high-match resumes. The system provides explainable recommendations to inform human decisions
Summaries extract capability points
To eliminate scattered interview notes and weak probation planning, the client mandated AI interview summaries that standardize evidence to assess position fit During interviews the system captures transcripts or structured notes, synthesizes core capability points (e.g., problem solving, domain knowledge), and produces a one-page summary with a recommended next action.
Results
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Scalability: Automated the processing of 36,000+ resumes and 16,800+ interviews.
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Time-to-Hire: Shortened the engineering hiring cycle by 2.5 days.
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Probation Optimization: Enabled managers to utilize AI summaries as a primary decision-making tool in nearly 78% of departments.
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Operational Excellence: Standardized evaluation criteria across the enterprise, improving the overall quality of hires.
Conclusion
Moka Eva did more than automate screening; it operationalized the entire talent lifecycle. By converting raw data into actionable capability points, the team moved past the bottlenecks of manual hiring. Today, they operate with newfound speed and clarity—cutting time-to-hire, streamlining onboarding, and ensuring that every headcount decision is both defensible and productive
