Fast Hire, Smart Start: How a Leading Lithium-Battery Manufacturer Cut Hiring Time with MokaHR AI

The client — a leading lithium battery manufacturer — faced engineering-led growth with urgent headcount needs and dense, messy resume flows. Recruiters were swamped by volume and inconsistent interview records, making probation decisions slow and error-prone. The company used MokaHR’s AI ATS (Moka Eva) to transform screening, interviewing, and onboarding into a measurable workflow.

The Challenge: High volume, scattered signals, slow decisions.

Rapid expansion created urgent hiring pressure but slow fills.
The 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.

The Solution: AI screening + structured interviews + actionable summaries.

High-match resume rapid identification — System 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 — Hiring managers focus on what matters.
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. 

The Results: Faster matching, clearer probation, measurable gains.

The client achieved clear production outcomes: the slides report 36,000+ resumes processed by AI screening and 16,800+ interviews covered by AI interview summaries (customer’s internal slide). Implementing role-specific screening cut average time-to-hire for core engineering roles by about 2.5 days from posting to hire, and near 78% of departments started using AI interview summaries as a primary reference during probation — focusing managers on the right development signals. At scale, Moka’s public case data also shows that Moka Eva drives major reductions in time-to-hire and improves evaluation consistency across enterprises. 

Conclusion: Fast hire, fast start — fit backed by evidence.

Adopting MokaHR moved the client from manual triage to a tightly instrumented hiring engine that hires quickly and gets hires productive faster. AI resume screening accelerates shortlists; AI interview summaries convert scattered impressions into auditable capability points used directly in probation and onboarding. The outcome is not rhetoric but operational: shorter time-to-hire, targeted onboarding tasks, and defensible decisions about who to hire.