{"id":"d5962db0-dc89-40b2-bd56-5d9cf4b79d6b","kind":"companies","code":"UB-C194","name":"Duomian","nameBasis":"source-english","summary":"Duomian is a company headquartered in Beijing, China, founded in 2020.","description":"Duomian (Beijing) Technology Co., Ltd. was founded in 2019","country":"China","category":null,"originalName":"多面科技","eventDate":null,"website":"https://www.duomian.com/","sourceUpdatedAt":"2026-10-05T00:00:00.000Z","englishUpdatedAt":"2026-10-15T20:43:42.419Z","reviewedAt":"2026-10-15T20:43:42.419Z","snapshotAt":"2026-10-15T14:23:27.067Z","sourceHash":"dc1b9150ab44a844ff3dac5310bd617b7ea4a4c84dc2de4670b5904581d9887d","companyId":null,"foundedYear":2020,"logoUrl":null,"originalText":"AI赋能\n\n多面（北京）科技有限公司成立于2019年","sources":[],"related":[{"id":"21d4bba6-42de-4728-9fdb-e5bc0cad54d1","kind":"people","code":"UB-P722","name":"Dai Kebin","nameBasis":"transliterated","summary":"Dai Kebin, founder and CEO of Liepin, graduated in finance from Sun Yat-sen University in 2003 and worked as a P&G Greater China brand manager for Ariel, Whisper, Oral-B and Crest. Began entrepreneurship in 2008 and launched Liepin in 2011 for mid- and high-end talent, pioneering a B+H+C platform model. Led its Hong Kong listing in 2018 as the market’s first technology HR stock. Upgraded it to Tongdao Liepin Group in 2020, moving from a single technology company to a diversified group. Appeared three times on Fortune China’s 40 Under 40 list and is a representative entrepreneur from the post ’80 generation.\nDai Kebin, chairman and CEO of Tongdao Liepin Group.\nGraduated in finance from Sun Yat-sen University in 2003.\nWorked at P&G during 2003-2008 on Ariel, Whisper, Oral-B and Crest. Began entrepreneurship in 2008, building China’s first career-development platform for mid- and high-end talent.\nLaunched Liepin in 2011 with the pioneering B+H+C model.\nLed its Hong Kong listing in 2018 as the first technology HR stock.\nCreated Tongdao Liepin Group in 2020, diversifying into recruitment, assessment and training, flexible staffing, online research, and individual career training and services.\nLed AI exploration since 2014, establishing the industry’s first big-data and AI team in 2014, holding its first human-machine precision talent matching contest in 2017, releasing a first-generation AI interview product in 2019 and transforming Liepin into an AI recruitment platform in 2025.\nAppeared three times on Fortune China’s 40 Under 40 list and is a leading entrepreneur from the post ’80 generation.","description":"Chinese person.","country":null,"category":null,"originalName":"戴科彬","eventDate":null,"website":null,"sourceUpdatedAt":"2026-10-10T18:49:26.266Z","englishUpdatedAt":"2026-10-16T00:56:44.609Z","reviewedAt":"2026-10-16T00:56:44.609Z","snapshotAt":"2026-10-15T14:23:27.067Z","sourceHash":"aad5682996cc6f8cea747ed03a96b6c54818100566944610c910f232e05b843a","companyId":"d5962db0-dc89-40b2-bd56-5d9cf4b79d6b","foundedYear":null,"logoUrl":null,"path":"/people/dai-kebin"},{"id":"985174a6-2831-4404-9389-ef4d66ffd1af","kind":"products","code":"UB-D916","name":"Duomian ·AI Mianshi","nameBasis":"transliterated","summary":"The company’s AI product is Duomian AI Interview.\n1. Performance\nBuilt independently using McClelland’s iceberg and competency models, Liepin’s years of HR expertise, practical experience and specialist data, plus AI. Emphasizes accurate assessment, ease and efficiency, and value.\n2. Applications\nServes large companies and SMEs in campus and experienced hiring, assessing university students and white-, grey- and blue-collar workers across recruitment scenarios:\n1) Hiring: consistent scientific role criteria and rapid AI screening.\n2) Probation: assess whether employees meet permanent-role requirements.\n3) Talent reviews: assess development potential to optimize staffing.\n4) Transfers and promotions: role-specific models and questions assess fit and identify stronger candidates.\n3. Technical innovation\n1) Validity and reliability: LLMs and tuned algorithms achieve over 90% scoring consistency with senior interviewers.\n2) Broad iceberg-model dimensions: 7 levels and nearly a hundred dimensions match people, roles and organizations.\n3) Evidence-Based (EB) technology: claimed industry-first multi-layer probing improves assessment precision.\n4) Resume-based personalized questions.\n5) Comprehensive intelligent anti-cheating for fairness.\n6) Multimodal analysis of voice, semantics and images.\n7) Multilingual scoring with fluent Cantonese and English and extensibility to other languages/dialects.\n8) Custom rooms, interviewers and questions reinforce employer identity.\n4. Core advantages and industry needs\n1) Accurate assessment: 1 model, 3 questioning methods, 2 evaluations and 1 anti-cheating approach.\nA. 1 model: scientific and intelligent modeling.\nCovers 7 iceberg-model levels and 140+ dimensions, including physical qualities, knowledge/skills, role experience, ability, character, motivation and values. Recruiters enter a role and basic requirements; Doris generates an editable JD, then a competency model based on the confirmed requirements, iceberg theory and industry data, with manual adjustment supported.\nB. 3 questioning methods: competency questions, proprietary resume-analysis questions and pioneering EB deep probing.\nGenerates editable interview questions from competency models and recruiter needs. Automatically identifies resume strengths and risks for targeted, realistic individual interviews. Patented Evidence-Based multi-stage follow-ups aim to reveal actual capabilities.\nC. 2 evaluations: anchor-comparison scoring and multimodal assessment.\nThe team argues LLMs cannot directly evaluate candidates reliably. Its proprietary anchor-comparison method combines models and industry expertise, scoring against key dimension anchors for more scientific precision. Voice, meaning, expressions and body movement inform holistic assessment, replicating human interview practice.\nD. 1 anti-cheating approach throughout.\nThe source reports candidate cheating above 15%, with conventional methods insufficient. LLM-assisted answer analysis identifies cheating using large models, described as an industry first.\n2) Efficient and easy to use\nCreate an interview in four steps:\na. Confirm role information and resume-question elements.\nb. Generate the competency model from the role.\nc. Set interviewer appearance, background, language and opening remarks.\nd. Configure interview behavior requirements.\nCustomize questions, rooms and interviewers:\na. Tailor dimensions, scoring standards and questions to enterprise competency models.\nb. Match employer branding through rooms, opening statements, automated answers, promotional videos and warm-up questions.\nc. Customize 2D or 3D digital interviewers to improve candidate engagement and employer presentation.\nRich interview reports and filterable report lists help evidence-based screening. Smooth experiences across desktop, mini programs and apps fit different scenarios.\nMultilingual interviews support local talent needs. Speech recognition and NLP enable natural English, Cantonese and other language/dialect interactions, letting candidates express themselves fully and contributing diverse perspectives to inclusive talent ecosystems.\n3) Value: save money, time and effort\nMoney: interview costs are 1/5-1/8 of offline interviews; attendance rises from 60% to 85%.\nTime: average interview execution cycles fall to 60%~70% of their original duration.\nEffort: intelligent resume questions identify strengths/risks, patented EB follow-ups probe deeply, anchor-based scoring improves precision and multimodal evaluations combine meaning, voice and expressions. LLM-based anti-cheating addresses impersonation, leaked questions, looking up information and memorized answers, with claimed detection accuracy of 95%.","description":"The company’s AI product is Duomian AI Interview.\n1. Performance\nBuilt independently using McClelland’s iceberg and competency models, Liepin’s years of HR expertise, practical experience and specialist data, plus AI. Emphasizes accurate assessment, ease and efficiency, and value.\n2. Applications\nServes large companies and SMEs in campus and experienced hiring, assessing university students and white-, grey- and blue-collar workers across recruitment scenarios:\n1) Hiring: consistent scientific role criteria and rapid AI screening.\n2) Probation: assess whether employees meet permanent-role requirements.\n3) Talent reviews: assess development potential to optimize staffing.\n4) Transfers and promotions: role-specific models and questions assess fit and identify stronger candidates.\n3. Technical innovation\n1) Validity and reliability: LLMs and tuned algorithms achieve over 90% scoring consistency with senior interviewers.\n2) Broad iceberg-model dimensions: 7 levels and nearly a hundred dimensions match people, roles and organizations.\n3) Evidence-Based (EB) technology: claimed industry-first multi-layer probing improves assessment precision.\n4) Resume-based personalized questions.\n5) Comprehensive intelligent anti-cheating for fairness.\n6) Multimodal analysis of voice, semantics and images.\n7) Multilingual scoring with fluent Cantonese and English and extensibility to other languages/dialects.\n8) Custom rooms, interviewers and questions reinforce employer identity.\n4. Core advantages and industry needs\n1) Accurate assessment: 1 model, 3 questioning methods, 2 evaluations and 1 anti-cheating approach.\nA. 1 model: scientific and intelligent modeling.\nCovers 7 iceberg-model levels and 140+ dimensions, including physical qualities, knowledge/skills, role experience, ability, character, motivation and values. Recruiters enter a role and basic requirements; Doris generates an editable JD, then a competency model based on the confirmed requirements, iceberg theory and industry data, with manual adjustment supported.\nB. 3 questioning methods: competency questions, proprietary resume-analysis questions and pioneering EB deep probing.\nGenerates editable interview questions from competency models and recruiter needs. Automatically identifies resume strengths and risks for targeted, realistic individual interviews. Patented Evidence-Based multi-stage follow-ups aim to reveal actual capabilities.\nC. 2 evaluations: anchor-comparison scoring and multimodal assessment.\nThe team argues LLMs cannot directly evaluate candidates reliably. Its proprietary anchor-comparison method combines models and industry expertise, scoring against key dimension anchors for more scientific precision. Voice, meaning, expressions and body movement inform holistic assessment, replicating human interview practice.\nD. 1 anti-cheating approach throughout.\nThe source reports candidate cheating above 15%, with conventional methods insufficient. LLM-assisted answer analysis identifies cheating using large models, described as an industry first.\n2) Efficient and easy to use\nCreate an interview in four steps:\na. Confirm role information and resume-question elements.\nb. Generate the competency model from the role.\nc. Set interviewer appearance, background, language and opening remarks.\nd. Configure interview behavior requirements.\nCustomize questions, rooms and interviewers:\na. Tailor dimensions, scoring standards and questions to enterprise competency models.\nb. Match employer branding through rooms, opening statements, automated answers, promotional videos and warm-up questions.\nc. Customize 2D or 3D digital interviewers to improve candidate engagement and employer presentation.\nRich interview reports and filterable report lists help evidence-based screening. Smooth experiences across desktop, mini programs and apps fit different scenarios.\nMultilingual interviews support local talent needs. Speech recognition and NLP enable natural English, Cantonese and other language/dialect interactions, letting candidates express themselves fully and contributing diverse perspectives to inclusive talent ecosystems.\n3) Value: save money, time and effort\nMoney: interview costs are 1/5-1/8 of offline interviews; attendance rises from 60% to 85%.\nTime: average interview execution cycles fall to 60%~70% of their original duration.\nEffort: intelligent resume questions identify strengths/risks, patented EB follow-ups probe deeply, anchor-based scoring improves precision and multimodal evaluations combine meaning, voice and expressions. LLM-based anti-cheating addresses impersonation, leaked questions, looking up information and memorized answers, with claimed detection accuracy of 95%.","country":null,"category":"Recruiting tools","originalName":"多面·AI面试","eventDate":null,"website":null,"sourceUpdatedAt":"2026-10-11T18:07:18.310Z","englishUpdatedAt":"2026-10-16T01:36:48.246Z","reviewedAt":"2026-10-16T01:36:48.246Z","snapshotAt":"2026-10-15T14:23:27.067Z","sourceHash":"1f96ae91cccc7c9198c050e63990c278faf35fed72a92098f68951f967e52e35","companyId":"d5962db0-dc89-40b2-bd56-5d9cf4b79d6b","foundedYear":null,"logoUrl":null,"path":"/products/duomian-ai-mianshi"}],"finance":{"funding":{"metric":"funding","count":0,"latest":null,"years":[],"points":[],"linePoints":[],"conflictGroups":0,"omittedRecordCount":0,"records":[],"totalRaised":null},"valuation":{"metric":"valuation","count":0,"latest":null,"years":[],"points":[],"linePoints":[],"conflictGroups":0,"omittedRecordCount":0,"records":[]},"revenue":{"metric":"revenue","count":0,"latest":null,"years":[],"points":[],"linePoints":[],"conflictGroups":0,"omittedRecordCount":0,"records":[]}},"events":[],"team":[{"id":"21d4bba6-42de-4728-9fdb-e5bc0cad54d1","kind":"people","code":"UB-P722","name":"Dai Kebin","nameBasis":"transliterated","summary":"Dai Kebin, founder and CEO of Liepin, graduated in finance from Sun Yat-sen University in 2003 and worked as a P&G Greater China brand manager for Ariel, Whisper, Oral-B and Crest. Began entrepreneurship in 2008 and launched Liepin in 2011 for mid- and high-end talent, pioneering a B+H+C platform model. Led its Hong Kong listing in 2018 as the market’s first technology HR stock. Upgraded it to Tongdao Liepin Group in 2020, moving from a single technology company to a diversified group. Appeared three times on Fortune China’s 40 Under 40 list and is a representative entrepreneur from the post ’80 generation.\nDai Kebin, chairman and CEO of Tongdao Liepin Group.\nGraduated in finance from Sun Yat-sen University in 2003.\nWorked at P&G during 2003-2008 on Ariel, Whisper, Oral-B and Crest. Began entrepreneurship in 2008, building China’s first career-development platform for mid- and high-end talent.\nLaunched Liepin in 2011 with the pioneering B+H+C model.\nLed its Hong Kong listing in 2018 as the first technology HR stock.\nCreated Tongdao Liepin Group in 2020, diversifying into recruitment, assessment and training, flexible staffing, online research, and individual career training and services.\nLed AI exploration since 2014, establishing the industry’s first big-data and AI team in 2014, holding its first human-machine precision talent matching contest in 2017, releasing a first-generation AI interview product in 2019 and transforming Liepin into an AI recruitment platform in 2025.\nAppeared three times on Fortune China’s 40 Under 40 list and is a leading entrepreneur from the post ’80 generation.","description":"Chinese person.","country":null,"category":null,"originalName":"戴科彬","eventDate":null,"website":null,"sourceUpdatedAt":"2026-10-10T18:49:26.266Z","englishUpdatedAt":"2026-10-16T00:56:44.609Z","reviewedAt":"2026-10-16T00:56:44.609Z","snapshotAt":"2026-10-15T14:23:27.067Z","sourceHash":"aad5682996cc6f8cea747ed03a96b6c54818100566944610c910f232e05b843a","companyId":"d5962db0-dc89-40b2-bd56-5d9cf4b79d6b","foundedYear":null,"logoUrl":null,"path":"/people/dai-kebin"}],"productsRelated":[{"id":"985174a6-2831-4404-9389-ef4d66ffd1af","kind":"products","code":"UB-D916","name":"Duomian ·AI Mianshi","nameBasis":"transliterated","summary":"The company’s AI product is Duomian AI Interview.\n1. Performance\nBuilt independently using McClelland’s iceberg and competency models, Liepin’s years of HR expertise, practical experience and specialist data, plus AI. Emphasizes accurate assessment, ease and efficiency, and value.\n2. Applications\nServes large companies and SMEs in campus and experienced hiring, assessing university students and white-, grey- and blue-collar workers across recruitment scenarios:\n1) Hiring: consistent scientific role criteria and rapid AI screening.\n2) Probation: assess whether employees meet permanent-role requirements.\n3) Talent reviews: assess development potential to optimize staffing.\n4) Transfers and promotions: role-specific models and questions assess fit and identify stronger candidates.\n3. Technical innovation\n1) Validity and reliability: LLMs and tuned algorithms achieve over 90% scoring consistency with senior interviewers.\n2) Broad iceberg-model dimensions: 7 levels and nearly a hundred dimensions match people, roles and organizations.\n3) Evidence-Based (EB) technology: claimed industry-first multi-layer probing improves assessment precision.\n4) Resume-based personalized questions.\n5) Comprehensive intelligent anti-cheating for fairness.\n6) Multimodal analysis of voice, semantics and images.\n7) Multilingual scoring with fluent Cantonese and English and extensibility to other languages/dialects.\n8) Custom rooms, interviewers and questions reinforce employer identity.\n4. Core advantages and industry needs\n1) Accurate assessment: 1 model, 3 questioning methods, 2 evaluations and 1 anti-cheating approach.\nA. 1 model: scientific and intelligent modeling.\nCovers 7 iceberg-model levels and 140+ dimensions, including physical qualities, knowledge/skills, role experience, ability, character, motivation and values. Recruiters enter a role and basic requirements; Doris generates an editable JD, then a competency model based on the confirmed requirements, iceberg theory and industry data, with manual adjustment supported.\nB. 3 questioning methods: competency questions, proprietary resume-analysis questions and pioneering EB deep probing.\nGenerates editable interview questions from competency models and recruiter needs. Automatically identifies resume strengths and risks for targeted, realistic individual interviews. Patented Evidence-Based multi-stage follow-ups aim to reveal actual capabilities.\nC. 2 evaluations: anchor-comparison scoring and multimodal assessment.\nThe team argues LLMs cannot directly evaluate candidates reliably. Its proprietary anchor-comparison method combines models and industry expertise, scoring against key dimension anchors for more scientific precision. Voice, meaning, expressions and body movement inform holistic assessment, replicating human interview practice.\nD. 1 anti-cheating approach throughout.\nThe source reports candidate cheating above 15%, with conventional methods insufficient. LLM-assisted answer analysis identifies cheating using large models, described as an industry first.\n2) Efficient and easy to use\nCreate an interview in four steps:\na. Confirm role information and resume-question elements.\nb. Generate the competency model from the role.\nc. Set interviewer appearance, background, language and opening remarks.\nd. Configure interview behavior requirements.\nCustomize questions, rooms and interviewers:\na. Tailor dimensions, scoring standards and questions to enterprise competency models.\nb. Match employer branding through rooms, opening statements, automated answers, promotional videos and warm-up questions.\nc. Customize 2D or 3D digital interviewers to improve candidate engagement and employer presentation.\nRich interview reports and filterable report lists help evidence-based screening. Smooth experiences across desktop, mini programs and apps fit different scenarios.\nMultilingual interviews support local talent needs. Speech recognition and NLP enable natural English, Cantonese and other language/dialect interactions, letting candidates express themselves fully and contributing diverse perspectives to inclusive talent ecosystems.\n3) Value: save money, time and effort\nMoney: interview costs are 1/5-1/8 of offline interviews; attendance rises from 60% to 85%.\nTime: average interview execution cycles fall to 60%~70% of their original duration.\nEffort: intelligent resume questions identify strengths/risks, patented EB follow-ups probe deeply, anchor-based scoring improves precision and multimodal evaluations combine meaning, voice and expressions. LLM-based anti-cheating addresses impersonation, leaked questions, looking up information and memorized answers, with claimed detection accuracy of 95%.","description":"The company’s AI product is Duomian AI Interview.\n1. Performance\nBuilt independently using McClelland’s iceberg and competency models, Liepin’s years of HR expertise, practical experience and specialist data, plus AI. Emphasizes accurate assessment, ease and efficiency, and value.\n2. Applications\nServes large companies and SMEs in campus and experienced hiring, assessing university students and white-, grey- and blue-collar workers across recruitment scenarios:\n1) Hiring: consistent scientific role criteria and rapid AI screening.\n2) Probation: assess whether employees meet permanent-role requirements.\n3) Talent reviews: assess development potential to optimize staffing.\n4) Transfers and promotions: role-specific models and questions assess fit and identify stronger candidates.\n3. Technical innovation\n1) Validity and reliability: LLMs and tuned algorithms achieve over 90% scoring consistency with senior interviewers.\n2) Broad iceberg-model dimensions: 7 levels and nearly a hundred dimensions match people, roles and organizations.\n3) Evidence-Based (EB) technology: claimed industry-first multi-layer probing improves assessment precision.\n4) Resume-based personalized questions.\n5) Comprehensive intelligent anti-cheating for fairness.\n6) Multimodal analysis of voice, semantics and images.\n7) Multilingual scoring with fluent Cantonese and English and extensibility to other languages/dialects.\n8) Custom rooms, interviewers and questions reinforce employer identity.\n4. Core advantages and industry needs\n1) Accurate assessment: 1 model, 3 questioning methods, 2 evaluations and 1 anti-cheating approach.\nA. 1 model: scientific and intelligent modeling.\nCovers 7 iceberg-model levels and 140+ dimensions, including physical qualities, knowledge/skills, role experience, ability, character, motivation and values. Recruiters enter a role and basic requirements; Doris generates an editable JD, then a competency model based on the confirmed requirements, iceberg theory and industry data, with manual adjustment supported.\nB. 3 questioning methods: competency questions, proprietary resume-analysis questions and pioneering EB deep probing.\nGenerates editable interview questions from competency models and recruiter needs. Automatically identifies resume strengths and risks for targeted, realistic individual interviews. Patented Evidence-Based multi-stage follow-ups aim to reveal actual capabilities.\nC. 2 evaluations: anchor-comparison scoring and multimodal assessment.\nThe team argues LLMs cannot directly evaluate candidates reliably. Its proprietary anchor-comparison method combines models and industry expertise, scoring against key dimension anchors for more scientific precision. Voice, meaning, expressions and body movement inform holistic assessment, replicating human interview practice.\nD. 1 anti-cheating approach throughout.\nThe source reports candidate cheating above 15%, with conventional methods insufficient. LLM-assisted answer analysis identifies cheating using large models, described as an industry first.\n2) Efficient and easy to use\nCreate an interview in four steps:\na. Confirm role information and resume-question elements.\nb. Generate the competency model from the role.\nc. Set interviewer appearance, background, language and opening remarks.\nd. Configure interview behavior requirements.\nCustomize questions, rooms and interviewers:\na. Tailor dimensions, scoring standards and questions to enterprise competency models.\nb. Match employer branding through rooms, opening statements, automated answers, promotional videos and warm-up questions.\nc. Customize 2D or 3D digital interviewers to improve candidate engagement and employer presentation.\nRich interview reports and filterable report lists help evidence-based screening. Smooth experiences across desktop, mini programs and apps fit different scenarios.\nMultilingual interviews support local talent needs. Speech recognition and NLP enable natural English, Cantonese and other language/dialect interactions, letting candidates express themselves fully and contributing diverse perspectives to inclusive talent ecosystems.\n3) Value: save money, time and effort\nMoney: interview costs are 1/5-1/8 of offline interviews; attendance rises from 60% to 85%.\nTime: average interview execution cycles fall to 60%~70% of their original duration.\nEffort: intelligent resume questions identify strengths/risks, patented EB follow-ups probe deeply, anchor-based scoring improves precision and multimodal evaluations combine meaning, voice and expressions. LLM-based anti-cheating addresses impersonation, leaked questions, looking up information and memorized answers, with claimed detection accuracy of 95%.","country":null,"category":"Recruiting tools","originalName":"多面·AI面试","eventDate":null,"website":null,"sourceUpdatedAt":"2026-10-11T18:07:18.310Z","englishUpdatedAt":"2026-10-16T01:36:48.246Z","reviewedAt":"2026-10-16T01:36:48.246Z","snapshotAt":"2026-10-15T14:23:27.067Z","sourceHash":"1f96ae91cccc7c9198c050e63990c278faf35fed72a92098f68951f967e52e35","companyId":"d5962db0-dc89-40b2-bd56-5d9cf4b79d6b","foundedYear":null,"logoUrl":null,"path":"/products/duomian-ai-mianshi"}],"investors":[],"path":"/companies/duomiankeji","structured":{"city":"Beijing","country":"China","location":null,"role":null,"category":null,"company":null},"definition":"Duomian is a company headquartered in Beijing, China, founded in 2020.","summaryBasis":"generated","summaryIssue":"too-short","thin":false,"canonical":"https://uniquebase.ai/companies/duomiankeji"}