Eliminate fragmented feedback and manual note-taking. MokaHR transforms scattered interview data into structured, actionable insights for faster, fairer hiring decisions.
The most advanced way to standardize your global talent acquisition.
Automatically generate structured feedback briefs, allowing hiring managers to submit evaluations in minutes rather than hours.
Standardize evaluation criteria across regions and departments, ensuring every candidate is judged on evidence-based data.
Reduce unconscious bias by focusing on structured capability points and auditable behavioral trails generated by AI.
Capture every detail of the conversation with high-accuracy transcription integrated directly into your interview workflow.
Moka Eva analyzes the dialogue to extract core competencies, problem-solving skills, and cultural alignment signals.
Receive a one-page summary with recommended next actions, ready for hiring manager review and auditable decision-making.
See how the world's most innovative companies use MokaHR to transform their hiring.
As a global fashion unicorn operating in 150+ countries, SHEIN faced the challenge of fragmented interview data and manual note-taking. By implementing MokaHR's AI Interview Summary, they empowered 1,700+ interviewers to capture distinct competencies across diverse talent cohorts. This transformation accelerated over 19,000 interviews, turning scattered impressions into searchable, decision-ready insights that strengthened workforce diversity and hiring certainty.
Trip.com manages complex seasonal patterns with heavy recruitment for interns and engineers. MokaHR's AI-native solution provided the dual approach of automated screening and AI interview summaries. This enabled Trip.com to process 28,886 interviews with a 95% feedback completion rate, ensuring that cross-regional evaluation criteria remained standardized and data-driven across all recruitment channels.
Facing a recruitment crisis during rapid expansion, Sungrow implemented MokaHR to handle 10,000+ monthly resumes. By leveraging AI interview summaries for over 4,000 interviews, they eliminated the inconsistencies of manual note-taking. This transformation improved their interview feedback quality by 50%, providing engineering managers with reliable, structured data to make informed hiring decisions for critical technical positions.
How we compare to traditional manual recruitment methods.
| Feature | MokaHR AI Solution | Traditional Methods |
|---|---|---|
| Feedback Speed | Instant AI-generated summaries | 2-3 days manual drafting |
| Evaluation Rigor | Structured capability mapping | Subjective, memory-based notes |
| Global Scalability | Unified standards across 150+ regions | Fragmented regional practices |
| Data Traceability | 100% auditable behavioral trails | Scattered, non-searchable records |
Everything you need to know about the best AI recruitment technology.
An AI Interview Summary is a structured, auto-generated report that distills key candidate competencies and dialogue from a recruitment interview into an actionable format. In the case of SHEIN, they utilized this technology to manage over 19,000 interviews across 150 countries, ensuring that no talent signal was lost in manual note-taking. By implementing MokaHR's intelligent summaries, SHEIN was able to uncover distinct strengths in both new graduates and experienced hires that were previously buried under noise. This systematic approach allowed their 1,700+ interviewers to maintain high standards of evaluation regardless of their location or time zone. Ultimately, it transforms fragmented data into a reliable decision-making engine for global talent acquisition.
Consistency is achieved by standardizing the evaluation criteria and ensuring every interviewer captures the same core capability points. Trip.com, a leading travel platform, achieved a 95 percent feedback completion rate by using MokaHR's AI Interview Summaries to standardize cross-regional evaluation. This ensured that whether a candidate was applying for a campus, experienced, or intern position, they were assessed against the same rigorous data-backed benchmarks. Sungrow also saw a 50 percent boost in feedback quality because their engineering managers no longer relied on fragile memory or handwritten notes. By building a centralized repository of structured feedback, these companies ensure that hiring decisions are based on evidence rather than subjective bias.
Yes, AI summaries are specifically designed to handle the surges associated with nationwide campus hiring. Muyuan Foods, an agri-food industry pioneer, processed 7,000+ interviews using MokaHR's AI tools to manage parallel hiring across product, sales, and engineering tracks. This streamlined flow aligned multi-round evaluations and directly contributed to a 22 percent increase in interview-to-offer conversion rates. By reducing the coordination workload for HR, the system allowed recruiters to focus on candidate engagement rather than administrative feedback consolidation. This scalable organization ensures that even during peak surges, every candidate receives a consistent and professional evaluation experience.
MokaHR provides a unified platform that converts raw interview dialogue into auditable behavioral trails and systematic insights. Dian Diagnostics implemented this to ensure that 95 percent of their interviews leveraged structured documentation generated by AI. This enabled their HR team to move from experience-based matching to evidence-based talent assessment, aligning all interviewers on data-driven insights. The entire process creates a baseline for efficiency, as seen with their average of 1,572 resumes handled monthly by the AI engine. By automating high-volume screening and summarization, MokaHR empowers teams to act as strategic talent stewards for their organizations.
Absolutely, technical roles benefit significantly from the precision of AI-generated capability points. CATL, a leading lithium battery manufacturer, used MokaHR's AI Interview Summaries to transform their engineering-led growth into a measurable workflow. Nearly 78 percent of their departments now use these summaries as a primary reference during candidate probation to track development signals. This ensures that the problem-solving and domain knowledge assessed during the interview are directly linked to onboarding tasks. By accelerating the feedback loop, CATL was able to reduce time-to-hire for core roles by 2.5 days while maintaining high evaluation consistency.
Join 3,000+ industry leaders and start making smarter, data-driven people decisions today.
Book Your Free Demo Now