{"id":24673,"date":"2026-07-31T06:47:26","date_gmt":"2026-07-31T06:47:26","guid":{"rendered":"https:\/\/recruiterflow.com\/glossary\/?p=24673"},"modified":"2026-07-31T06:52:45","modified_gmt":"2026-07-31T06:52:45","slug":"ai-candidate-matching","status":"publish","type":"post","link":"https:\/\/recruiterflow.com\/glossary\/ai-candidate-matching\/","title":{"rendered":"What is AI candidate matching?"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">AI candidate matching uses machine-learning and natural-language systems to compare role requirements with candidate data, then rank or recommend people whose experience appears relevant. It helps recruiters find relevant prospects across an <\/span><a href=\"https:\/\/recruiterflow.com\/blog\/database-resurrection-recruiting\/\"><span style=\"font-weight: 400;\">existing database<\/span><\/a><span style=\"font-weight: 400;\"> or incoming applicant pool.<\/span><\/p>\n<p><b>The recruiter still decides who deserves outreach, assessment, and submission.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">The core output is a ranked set of candidates linked to stated qualification criteria. Strong matching tools show why each person surfaced, where the evidence came from, and which criteria remain uncertain. That makes the result a starting point for recruiter review rather than a hiring decision.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"AI_candidate_matching_at_a_glance\"><\/span><span style=\"font-weight: 400;\">AI candidate matching at a glance<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Input:<\/b><span style=\"font-weight: 400;\"> a role brief, qualification criteria, and candidate records<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Process:<\/b><span style=\"font-weight: 400;\"> extraction, semantic comparison, scoring, and ranking<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Output:<\/b><span style=\"font-weight: 400;\"> a shortlist or recommended-candidate set with supporting evidence<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Best use:<\/b><span style=\"font-weight: 400;\"> database rediscovery, shortlist preparation, and search refinement<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Human role:<\/b><span style=\"font-weight: 400;\"> review context, correct weak signals, and choose the next action<\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"How_AI_candidate_matching_works\"><\/span><span style=\"font-weight: 400;\">How AI candidate matching works<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">AI candidate matching usually follows five connected stages.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Translate the role into criteria.<\/b><span style=\"font-weight: 400;\"> The system identifies requirements such as skills, seniority, sector knowledge, location, tenure, credentials, and career pattern. <\/span><a href=\"https:\/\/help.recruiterflow.com\/en\/articles\/12146879-how-to-customize-the-criteria-in-aira-matchmaker\"><span style=\"font-weight: 400;\">Recruiters may edit the criteria<\/span><\/a><span style=\"font-weight: 400;\"> and indicate which ones carry more weight.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Structure candidate evidence.<\/b><span style=\"font-weight: 400;\"> Resume text, profile fields, notes, prior submissions, communication history, and other permitted records become comparable data points. The quality and completeness of these records affect the result.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Compare meaning, not just exact words.<\/b> <a href=\"https:\/\/recruiterflow.com\/blog\/what-true-natural-language-search-looks-like\/\"><span style=\"font-weight: 400;\">Semantic methods can connect related terms<\/span><\/a><span style=\"font-weight: 400;\"> and experiences. A profile that describes &#8220;enterprise account growth&#8221; may be relevant to a search for &#8220;strategic sales,&#8221; even when the phrasing differs.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Score and rank.<\/b><span style=\"font-weight: 400;\"> The system estimates how closely each record aligns with the role criteria. A score is useful when recruiters can inspect the contributing factors rather than seeing an unexplained number.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Review and refine.<\/b><span style=\"font-weight: 400;\"> Recruiters examine the evidence, adjust broad or incorrect criteria, and rerun the match. Each iteration can narrow the list and expose gaps in the brief or database.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Research on person-job fit has explored combinations of resume and job text, structured entities, and historical application data. More recent work has paired resume information extraction with semantic similarity and readable rationales. These approaches support a practical principle: matching quality depends on both the comparison method and the information available for comparison.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Example_from_a_firms_recruiting_workflow\"><\/span><span style=\"font-weight: 400;\">Example from a firm&#8217;s recruiting workflow<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">A firm receives a search for a finance director at a private-equity-backed manufacturing company. The brief calls for multi-site operations, acquisition integration, lender reporting, and experience leading a small team.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A <\/span><a href=\"https:\/\/recruiterflow.com\/blog\/boolean-search-in-recruitment\/\"><span style=\"font-weight: 400;\">keyword search<\/span><\/a><span style=\"font-weight: 400;\"> may find profiles that repeat those phrases. AI candidate matching can surface a candidate whose record uses different language, such as post-merger finance integration, covenant reporting, and divisional controllership. It may rank that person highly across several criteria and show the evidence for each match.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The recruiter then reviews details the system cannot settle from the record: the scale of the integrations, the candidate&#8217;s role in the work, compensation expectations, travel tolerance, client conflicts, and interest in a move. A relevant recommendation saves research time, but the recruiter turns that recommendation into a credible submission.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For executive search, the same pattern can help researchers revisit longlists from earlier assignments. A candidate who was not right for one mandate may fit a new role after a promotion, sector move, or geographic change. Matching makes the database easier to revisit; relationship knowledge makes the outreach credible.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"AI_candidate_matching_versus_keyword_search_and_candidate_screening\"><\/span><span style=\"font-weight: 400;\">AI candidate matching versus keyword search and candidate screening<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table>\n<thead>\n<tr>\n<th>\n<p style=\"text-align: left;\"><b>Point<\/b><\/p>\n<\/th>\n<th style=\"text-align: left;\"><b>AI candidate matching<\/b><\/th>\n<th style=\"text-align: left;\"><b>Keyword search<\/b><\/th>\n<th>\n<p style=\"text-align: left;\"><b>Candidate screening<\/b><\/p>\n<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><span style=\"font-weight: 400;\">Main purpose<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Rank people against role criteria<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Retrieve records containing selected terms<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Assess whether a person meets a defined bar<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Typical input<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Role criteria and candidate evidence<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Search terms and filters<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Application answers, evidence, or assessment results<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Typical output<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Ranked recommendations with fit signals<\/span><\/td>\n<td><span style=\"font-weight: 400;\">A result set<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Pass, fail, band, or review outcome<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Strength<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Finds semantic and multi-factor relevance<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Gives recruiters direct query control<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Applies a repeatable evaluation step<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Main limitation<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Depends on criteria, data, and score interpretation<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Misses related language and implicit experience<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Can occur after discovery and may use narrower evidence<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">These methods can work together. A recruiter might use filters to define a reachable population, matching to rank that population, and <\/span><a href=\"https:\/\/recruiterflow.com\/blog\/ai-screening\/\"><span style=\"font-weight: 400;\">screening<\/span><\/a><span style=\"font-weight: 400;\"> to test a smaller group against job-specific requirements.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Where_matching_helps_recruiting_firms\"><\/span><span style=\"font-weight: 400;\">Where matching helps recruiting firms<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span style=\"font-weight: 400;\">Database rediscovery<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Firm databases often contain candidates gathered across many assignments, consultants, and years. Matching can <\/span><a href=\"https:\/\/recruiterflow.com\/blog\/talent-rediscovery\/\"><span style=\"font-weight: 400;\">bring older records back into view<\/span><\/a><span style=\"font-weight: 400;\"> when their experience fits a current brief. This can reduce <\/span><a href=\"https:\/\/recruiterflow.com\/blog\/ai-sourcing\/\"><span style=\"font-weight: 400;\">duplicate sourcing<\/span><\/a><span style=\"font-weight: 400;\"> and make prior research more useful.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">First-pass shortlist preparation<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">A ranked list gives recruiters a focused review queue. The gain comes from spending less time opening clearly weak records and more time testing plausible candidates. The list should remain editable, auditable, and easy to rerun.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Brief calibration<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">An unexpectedly small or broad result set can reveal a problem with the search criteria. Recruiters can review which requirements are truly mandatory, which are preferences, and which need clearer wording. Matching then becomes a way to test the brief, not just the database.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Shared search logic<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Visible criteria help a delivery team discuss the same definition of fit. Researchers, recruiters, and account leaders can compare evidence against agreed requirements rather than relying on private search strings or undocumented intuition.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"What_still_requires_recruiter_judgment\"><\/span><span style=\"font-weight: 400;\">What still requires recruiter judgment<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Candidate records rarely capture the full hiring context. A profile may show the right title but not the scope of responsibility. A resume may omit a relevant project. Notes may be old. A strong match may be unavailable, off-limits, uninterested, or misaligned with the client&#8217;s working style.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Recruiters add context in areas such as:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Evidence depth and recency<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Career trajectory and reasons for change<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Motivation, availability, and compensation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Client relationships and prior interactions<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Confidentiality, off-limits rules, and search strategy<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The quality of a candidate&#8217;s actual contribution<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The score should guide attention, not replace a documented review. Recruiters should be able to disagree with a recommendation, record why, and update the criteria or candidate data when the disagreement exposes a repeatable issue.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_to_evaluate_matching_quality\"><\/span><span style=\"font-weight: 400;\">How to evaluate matching quality<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Time saved matters, but speed alone can reward a fast stream of weak suggestions. A practical review combines quality, efficiency, and coverage signals.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Shortlist acceptance rate:<\/b><span style=\"font-weight: 400;\"> the share of reviewed suggestions that recruiters accept for outreach or deeper review<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Qualified-to-reviewed ratio:<\/b><span style=\"font-weight: 400;\"> how many reviewed candidates meet the agreed qualification bar<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Recruiter correction rate:<\/b><span style=\"font-weight: 400;\"> how often recruiters change a criterion, weight, or candidate classification after inspecting the evidence<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Time to first credible shortlist:<\/b><span style=\"font-weight: 400;\"> elapsed time from an agreed brief to a recruiter-approved initial list<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Database coverage:<\/b><span style=\"font-weight: 400;\"> the share of an eligible candidate population that the matching process evaluated<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Explanation quality:<\/b><span style=\"font-weight: 400;\"> whether reviewers can trace a recommendation to relevant, current evidence<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Track these signals by role family or search type. A useful benchmark for high-volume staffing may not fit a retained leadership search. Review rejected suggestions too; recurring rejection reasons often point to stale records, missing fields, or criteria that are too broad.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Common_mistakes\"><\/span><span style=\"font-weight: 400;\">Common mistakes<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span style=\"font-weight: 400;\">Treating the score as the decision<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">A high score means the recorded evidence aligns with the selected criteria. It does not prove interest, performance, identity, availability, or final suitability. Make recruiter review an explicit stage before outreach or submission.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Starting with vague criteria<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Terms such as &#8220;strong leader&#8221; or &#8220;culture fit&#8221; are hard to test and easy to interpret inconsistently. Convert them into observable evidence, such as team size, operating scope, stakeholder level, or examples of change leadership.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Ignoring data quality<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Incomplete profiles, duplicated records, old notes, and inconsistent skill labels weaken matching. Define ownership for record maintenance and give recruiters a simple way to correct material errors during review.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Measuring only activity<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">More recommendations do not mean better matching. Pair volume and speed with acceptance, qualification, and outcome measures. Separate system performance from brief quality and recruiter process.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Practical_checklist\"><\/span><span style=\"font-weight: 400;\">Practical checklist<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Confirm the role criteria with the client or hiring team.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Separate mandatory evidence from useful preferences.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Check whether the candidate data is current enough for the search.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Review the evidence behind high and low scores.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Test a sample of rejected or low-ranked records.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Record why recruiters accept or reject recommendations.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Adjust criteria when results expose a brief problem.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Compare quality measures by role family and search type.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Keep the final outreach, assessment, and submission decisions with recruiters.<\/span><\/li>\n<\/ol>\n<h2><span class=\"ez-toc-section\" id=\"Questions_recruiters_ask\"><\/span><span style=\"font-weight: 400;\">Questions recruiters ask<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span style=\"font-weight: 400;\">Is AI candidate matching the same as resume parsing?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">No. Resume parsing extracts information from a resume into structured fields. Matching compares candidate evidence with role criteria and ranks the results. Parsing can supply some of the data used in matching.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Can matching search an existing candidate database?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Yes. Database rediscovery is a valuable use for firms. Results will be more useful when records contain current experience, reliable notes, and consistent fields.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Does the highest score identify the right candidate?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">No. It identifies the record with the closest calculated alignment to the selected criteria. Recruiters still need to test evidence, interest, scope, and client-specific context.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">What is AIRA Matchmaker?<\/span><\/h3>\n<p><a href=\"https:\/\/help.recruiterflow.com\/en\/collections\/15076282-aira-matchmaker\"><span style=\"font-weight: 400;\">AIRA Matchmaker<\/span><\/a><span style=\"font-weight: 400;\"> is Recruiterflow&#8217;s AI matching capability within its <\/span><a href=\"https:\/\/recruiterflow.com\/blog\/ai-native-ats-and-crm\/\"><span style=\"font-weight: 400;\">AI-native ATS and CRM<\/span><\/a><span style=\"font-weight: 400;\">. Recruiters can define qualification criteria in plain language, <\/span><span style=\"font-weight: 400;\">review ranked candidates with a Criteria Score<\/span><span style=\"font-weight: 400;\"> and criterion-level evidence, edit the criteria, and rerun the match. Product claims and terminology should be confirmed during editorial review before publication.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">How should a firm start using matching?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Begin with one repeatable role family, a reviewed set of candidate records, and clear qualification criteria. Compare recommendations with recruiter judgments, capture rejection reasons, and improve the criteria and data before widening use.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Recruiterflow_resources\"><\/span><span style=\"font-weight: 400;\">Recruiterflow resources<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/recruiterflow.com\/blog\/candidate-matching\/\"><span style=\"font-weight: 400;\">AI Candidate Matching: A Complete Guide<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/recruiterflow.com\/ai\"><span style=\"font-weight: 400;\">Recruiterflow AI and AIRA agents<\/span><\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>AI candidate matching uses machine-learning and natural-language systems to compare role requirements with candidate data, then rank or recommend people whose experience appears relevant. It helps recruiters find relevant prospects across an existing database or incoming applicant pool. The recruiter still decides who deserves outreach, assessment, and submission. The core output is a ranked set <a href=\"https:\/\/recruiterflow.com\/glossary\/ai-candidate-matching\/\" class=\"more-link\">&#8230;<span class=\"screen-reader-text\">  What is AI candidate matching?<\/span><\/a><\/p>\n","protected":false},"author":31,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[235],"tags":[],"class_list":["post-24673","post","type-post","status-publish","format-standard","hentry","category-ai"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.7 (Yoast SEO v28.1) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>What is AI candidate matching? - Recruiterflow Glossary<\/title>\n<meta name=\"description\" content=\"AI candidate matching uses machine-learning and natural-language systems to compare role requirements with candidate data, then rank or recommend people whose experience appears relevant.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/recruiterflow.com\/glossary\/ai-candidate-matching\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What is AI candidate matching?\" \/>\n<meta property=\"og:description\" content=\"AI candidate matching uses machine-learning and natural-language systems to compare role requirements with candidate data, then rank or recommend people whose experience appears relevant.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/recruiterflow.com\/glossary\/ai-candidate-matching\/\" \/>\n<meta property=\"og:site_name\" content=\"Recruiterflow Glossary\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/recruiterflow\" \/>\n<meta property=\"article:published_time\" content=\"2026-07-31T06:47:26+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-07-31T06:52:45+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/recruiterflow.com\/glossary\/wp-content\/uploads\/2026\/07\/Group-6754-e1683281335979.png.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"672\" \/>\n\t<meta property=\"og:image:height\" content=\"140\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Abhishek Sharma\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@recruiterflow\" \/>\n<meta name=\"twitter:site\" content=\"@recruiterflow\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Abhishek Sharma\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/ai-candidate-matching\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/ai-candidate-matching\\\/\"},\"author\":{\"name\":\"Abhishek Sharma\",\"@id\":\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/#\\\/schema\\\/person\\\/e49b0f8dc111400d65599717285b3172\"},\"headline\":\"What is AI candidate matching?\",\"datePublished\":\"2026-07-31T06:47:26+00:00\",\"dateModified\":\"2026-07-31T06:52:45+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/ai-candidate-matching\\\/\"},\"wordCount\":1589,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/#organization\"},\"articleSection\":[\"AI\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/ai-candidate-matching\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/ai-candidate-matching\\\/\",\"url\":\"https:\\\/\\\/recruiterflow.com\\\/glossary\\\/ai-candidate-matching\\\/\",\"name\":\"What is AI candidate matching? 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