HR-Focused AI "MENDAN" Updates "Matching Agent" Service, Continuously Improving Accuracy via Accumulated Placement Data

Zen office Co., Ltd. (Headquarters: Osaka; CEO: Yasuhiro Iwase) has launched the "Matching Agent," a new service update for its HR-specialized voice x AI agent, "MENDAN." This feature combines data extracted from interview audio with job requirements to propose openings with a high probability of receiving offers.

This function is characterized by a flywheel model where matching accuracy improves as it is used, by continuously learning from structured candidate information obtained from interview audio and historical placement data stored in CRMs. It shifts the focus from keyword-based searches to optimizing proposal sequences based on offer rates, enabling AI to replicate the once-personalized "winning combinations."

Improving Accuracy through Accumulated Placement Data In the recruitment industry, data volume in CRMs is increasing annually. However, front-line proposal work often remains focused on basic condition searches like "salary, location, and job category." The Matching Agent addresses these issues: inconsistency in application rates, lack of non-public information like client preferences, and the reliance on individual veteran expertise.

Features of "Matching Agent" 1. Automated Information Structuring from Audio: AI extracts data such as career history, motivations, preferences, and competencies from interview audio. It also structures Must/Want requirements and past hiring trends for jobs, making the matching logic transparent. 2. Continuous Improvement via CRM Data: Automatically integrates with CRMs like PORTERS, Salesforce, and kintone to accumulate data from every stage—proposal, application, interview, and offer. Accuracy improves significantly after 3 to 6 months of use. 3. Optimizing Priority with Case Ranking: Ranks job openings based on unit price, offer rates, and urgency, allowing junior staff to make decisions at a veteran level. 4. Automatic CRM Synchronization: Supports customizable field mapping for major CRMs, ensuring seamless updates to resumes and activity logs.

Implementation Results - Interview-to-Placement Rate: +4% improvement. - Monthly Job Acquisitions: +47 additional jobs. - Reduction in Missed Proposals: Every job in the inventory is reliably considered for proposals. - Faster Onboarding for New Staff: Standardizing decision logic across the organization.

Implementation Flow The process involves hearing/analysis, field design, data linkage, and operational launch followed by verification of improvement in offer rates.