{"id":2300,"date":"2026-03-23T13:59:56","date_gmt":"2026-03-23T13:59:56","guid":{"rendered":"https:\/\/pacific.ai\/staging\/3667\/?page_id=2300"},"modified":"2026-03-31T14:02:28","modified_gmt":"2026-03-31T14:02:28","slug":"gatekeeper","status":"publish","type":"page","link":"https:\/\/pacific.ai\/staging\/3667\/gatekeeper\/","title":{"rendered":"Gatekeeper"},"content":{"rendered":"<div id=\"bsf_rt_marker\"><\/div>\n<figure class=\"left-yellow-cir top-anime poa\"><svg width=\"2107\" height=\"2107\" viewBox=\"0 0 2107 2107\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n    <circle cx=\"1053.5\" cy=\"1053.5\" r=\"1053.5\" fill=\"url(#paint0_radial_427_45)\" fill-opacity=\"0.8\" \/>\n    <defs>\n      <radialGradient id=\"paint0_radial_427_45\" cx=\"0\" cy=\"0\" r=\"1\" gradientUnits=\"userSpaceOnUse\" gradientTransform=\"translate(1053.5 1053.5) rotate(90) scale(1053.5)\">\n        <stop offset=\"0.315\" stop-color=\"#FFF6EB\" \/>\n        <stop offset=\"1\" stop-color=\"white\" stop-opacity=\"0\" \/>\n      <\/radialGradient>\n    <\/defs>\n  <\/svg><\/figure>\n<figure class=\"right-green-cir top-anime poa green-cir-1\"><svg width=\"1586\" height=\"1586\" viewBox=\"0 0 1586 1586\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n    <circle cx=\"793\" cy=\"793\" r=\"793\" fill=\"url(#paint0_radial_427_42)\" fill-opacity=\"0.25\" \/>\n    <defs>\n      <radialGradient id=\"paint0_radial_427_42\" cx=\"0\" cy=\"0\" r=\"1\" gradientUnits=\"userSpaceOnUse\" gradientTransform=\"translate(793 793) rotate(90) scale(793)\">\n        <stop offset=\"0\" stop-color=\"#32DEAE\" \/>\n        <stop offset=\"1\" stop-color=\"white\" stop-opacity=\"0\" \/>\n      <\/radialGradient>\n    <\/defs>\n  <\/svg><\/figure>\n\n\n\n<div class=\"wp-block-group container top-container top-testing\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-columns row nowrap_991 is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column col-12 df col-md-12 col-lg-5 is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"top-inner guard-box\">\n    <h1><strong class=\"inline\">Gatekeeper:<\/strong> Automated LLM, ML, and Agentic AI Testing<\/h1>\n    <p>Run test suites and build CI\/CD release gates on real-world medical AI tasks, social and cognitive bias, red teaming, and regulatory compliance.<\/p>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column col-12 df col-md-12 col-lg-7 is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"video-container shadow\">\n    <div class=\"video-overlay vimeo-wrapper img_radius\" style=\"background-image: url('https:\/\/pacific.ai\/staging\/3667\/wp-content\/uploads\/2026\/03\/gate_top_1.png');\" data-vimeo-id=\"1178824446\">\n        <button class=\"play-button df\"><figure><svg width=\"32\" height=\"36\" viewBox=\"0 0 32 36\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M30.496 20.5943C32.4975 19.4399 32.4975 16.5514 30.496 15.397L4.49892 0.401868C2.49892 -0.751728 0 0.691719 0 3.00057V32.9907C0 35.2996 2.49893 36.743 4.49893 35.5894L30.496 20.5943Z\" fill=\"white\" \/><\/svg><\/figure><\/button>\n    <\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<section class=\"wp-block-group container gatekeep-section pt0\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-columns row nowrap_991 is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column col-12 col-lg-6 is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"row video-post-wrap\"><div class=\"col-12 col-md-12 col-lg-12\"><div class=\"video-post-item\" style=\"background-image: url('https:\/\/pacific.ai\/staging\/3667\/wp-content\/uploads\/2025\/04\/bg_users.svg');\"><a target=\"_blank\" class=\"df\" href=\"https:\/\/pacific.ai\/staging\/3667\/testing-for-bias-of-large-language-models-in-clinical-applications\/\"><div class=\"video-left\"><div class=\"video-post-title\">Testing for Bias of Large Language Models in Clinical Applications<\/div><\/div><div class=\"video-right\"><div class=\"video-speaker df\"><figure class=\"video-post-image\"><img decoding=\"async\" src=\"https:\/\/pacific.ai\/staging\/3667\/wp-content\/uploads\/2025\/04\/Louis.webp\" alt=\"Testing for Bias of Large Language Models in Clinical Applications\" loading=\"lazy\"><\/figure><div class=\"video-right-inner\"><div class=\"video-speaker-name\">Louis Ehwerhemuepha<\/div><div class=\"video-speaker-position\">Data Science Research Director at Children\u2019s Hospital of Orange County<\/div><\/div><\/div><\/div><figure class=\"play-video-item\"><img decoding=\"async\" src=\"https:\/\/pacific.ai\/staging\/3667\/wp-content\/uploads\/2025\/04\/play.svg\" alt=\"\" loading=\"lazy\"><\/figure><\/a><\/div><\/div><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column col-12 col-lg-6 df is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"testimonial-wrap\">\n  <figure><svg width=\"72\" height=\"65\" viewBox=\"0 0 72 65\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M22.7879 0C7.27273 14.9259 0 30.8148 0 45.0185C0 56.5741 8.72727 65 17.4545 65C25.2121 65 31.5152 58.7407 31.5152 51.037C31.5152 41.1667 24.2424 36.1111 13.5758 36.1111C13.5758 24.3148 17.2121 17.3333 28.6061 5.77778L22.7879 0ZM63.2727 0C47.7576 14.9259 40.4848 30.8148 40.4848 45.0185C40.4848 56.5741 49.2121 65 57.9394 65C65.697 65 72 58.7407 72 51.037C72 41.1667 64.7273 36.1111 54.0606 36.1111C54.0606 24.3148 57.697 17.3333 69.0909 5.77778L63.2727 0Z\" fill=\"#BB9844\" \/><\/svg><\/figure>\n  <p>You can\u2019t assume fairness. You have to test for it \u2014 by swapping genders, names, or cultural cues and tracking how the model\u2019s response shifts.<\/p>\n<div class=\"testim-author\">Louis Ehwerhemuepha, Data Science Research Director at Children\u2019s Hospital of Orange County<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-group standart-section oh\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-group container\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-columns row is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column col-12 is-layout-flow wp-block-column-is-layout-flow\">\n<h2 class=\"mb22\">Holistic Safety for Healthcare AI <\/h2>\n<div class=\"home-three-wrap fww reg-ind-wrap\">\n  <div class=\"home-three-item shadow\">\n      <figure class=\"df\"><svg width=\"45\" height=\"45\" viewBox=\"0 0 45 45\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path opacity=\"0.5\" d=\"M3.75 22.5C3.75 13.6612 3.75 9.24175 6.49587 6.49587C9.24175 3.75 13.6612 3.75 22.5 3.75C31.3388 3.75 35.7583 3.75 38.5041 6.49587C41.25 9.24175 41.25 13.6612 41.25 22.5C41.25 31.3388 41.25 35.7583 38.5041 38.5041C35.7583 41.25 31.3388 41.25 22.5 41.25C13.6612 41.25 9.24175 41.25 6.49587 38.5041C3.75 35.7583 3.75 31.3388 3.75 22.5Z\" fill=\"white\" \/><path d=\"M41.25 9.375C41.25 12.4816 38.7316 15 35.625 15C32.5184 15 30 12.4816 30 9.375C30 6.2684 32.5184 3.75 35.625 3.75C38.7316 3.75 41.25 6.2684 41.25 9.375Z\" fill=\"white\" \/><path d=\"M27.1875 20.1562C26.4109 20.1562 25.7812 19.5266 25.7812 18.75C25.7812 17.9734 26.4109 17.3438 27.1875 17.3438H31.875C32.6516 17.3438 33.2812 17.9734 33.2812 18.75V23.4375C33.2812 24.2141 32.6516 24.8438 31.875 24.8438C31.0984 24.8438 30.4688 24.2141 30.4688 23.4375V22.145L26.6952 25.9185C25.4138 27.2 23.3362 27.1999 22.0548 25.9185L19.0815 22.9452C18.8984 22.7621 18.6016 22.7621 18.4185 22.9452L14.1194 27.2444C13.5702 27.7935 12.6798 27.7935 12.1306 27.2444C11.5815 26.6952 11.5815 25.8048 12.1306 25.2556L16.4298 20.9565C17.7112 19.675 19.7888 19.675 21.0702 20.9565L24.0435 23.9298C24.2266 24.1129 24.5234 24.1129 24.7065 23.9298L28.48 20.1562H27.1875Z\" fill=\"white\" \/><\/svg><\/figure>\n      <h3>Clinical Task Performance<\/h3>\n      <p>Real-world benchmarks for clinical decision support, note generation, patient communication, and workflow administration.<\/strong><\/p>\n  <\/div>\n  <div class=\"home-three-item shadow\">\n      <figure class=\"df\"><svg width=\"47\" height=\"45\" viewBox=\"0 0 47 45\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M23.2275 5.80566C24.2643 5.80579 24.8867 6.6354 24.8867 7.46484V41.8896H32.7666C33.5961 41.8896 34.4257 42.304 34.4258 43.3408C34.4258 44.3777 33.5961 45 32.7666 45H14.1025C13.0658 44.9999 12.4434 44.3777 12.4434 43.3408C12.4434 42.3041 13.2731 41.6817 14.1025 41.6816H21.5684V7.46484C21.5684 6.42796 22.1907 5.80566 23.2275 5.80566ZM37.3291 2.90332C37.4671 2.90335 37.5466 2.92153 37.585 2.95508C37.9672 3.04827 38.2702 3.27931 38.5723 3.73242L46.4521 18.6641C46.8668 19.4935 46.6594 20.3225 45.8301 20.7373C45.6227 20.9447 45.4154 20.9453 45.208 20.9453C44.5859 20.9453 43.9632 20.7374 43.7559 20.1152L37.3271 7.87988L30.8984 20.3232C30.4838 20.9451 30.0693 21.1523 29.4473 21.1523C29.2399 21.1523 29.0325 21.1525 28.8252 20.9453C27.9957 20.5306 27.7884 19.4936 28.2031 18.6641L34.4248 6.2207H26.5449C25.5083 6.22064 24.8859 5.59909 24.8857 4.5625C24.8857 3.52568 25.7155 2.9034 26.5449 2.90332H37.3291ZM9.33203 2.90234L20.1162 2.90332C21.153 2.90341 21.7754 3.73303 21.7754 4.5625C21.7753 5.39189 21.153 6.0143 20.1162 6.22168H12.0635L18.457 18.6631C18.8716 19.4924 18.6641 20.5287 17.835 20.7363C17.6277 20.9436 17.4202 20.9443 17.2129 20.9443C16.5908 20.9443 15.9682 20.7369 15.7607 20.3223L9.33203 8.08691L2.90332 20.3223C2.48868 20.9441 2.07404 21.1513 1.45215 21.1514C1.24486 21.1514 1.03737 21.1515 0.830078 20.9443C0.0005687 20.5296 -0.206747 19.4926 0.208008 18.6631L7.88086 3.73145C7.88265 3.72965 7.88395 3.72738 7.88574 3.72559C8.16271 3.23946 8.67451 2.94919 9.20312 2.9082C9.2451 2.9042 9.28806 2.90235 9.33203 2.90234Z\" fill=\"white\" fill-opacity=\"0.4\"\/><path d=\"M23.4328 0C20.7369 0 18.6631 2.07377 18.6631 4.5623C18.6631 7.05083 20.7369 9.1246 23.2254 9.1246C25.7139 9.1246 27.7877 7.05083 27.7877 4.5623C27.9951 2.07377 25.9213 0 23.4328 0Z\" fill=\"white\"\/><path d=\"M45.2074 23.8479H29.6541C28.8246 23.8479 27.9951 24.47 27.9951 25.5069C27.9951 30.484 32.1427 34.6315 37.3271 34.6315C42.5115 34.6315 46.6591 30.484 46.6591 25.5069C46.6591 24.6774 46.0369 23.8479 45.2074 23.8479Z\" fill=\"white\"\/><path d=\"M17.2123 23.8464H1.65902C0.829509 23.8464 0 24.4686 0 25.5055C0 30.4825 4.14755 34.6301 9.33198 34.6301C14.5164 34.6301 18.664 30.4825 18.664 25.5055C18.664 24.6759 18.0418 23.8464 17.2123 23.8464Z\" fill=\"white\"\/><\/svg>\n<\/figure>\n      <h3>Robustness &#038; Bias<\/h3>\n      <p>Detecting demographic bias and robustness against clinical data perturbations.<\/p>\n  <\/div>\n  <div class=\"home-three-item shadow\">\n      <figure class=\"df\"><svg width=\"39\" height=\"45\" viewBox=\"0 0 39 45\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M19.4593 0V45C19.3666 45 19.274 44.9889 19.1836 44.9666C13.6766 43.612 9.12003 40.6223 5.51394 35.9974C-0.07584 28.8283 -0.0351899 20.7175 0.00735504 12.2287C0.0110678 11.4878 0.0147956 10.7441 0.0147956 9.9978C0.0147956 7.91082 1.32651 6.05293 3.28284 5.36899L17.8662 0.270567C18.3822 0.0901889 18.9207 0 19.4593 0Z\" fill=\"white\"\/><path opacity=\"0.5\" d=\"M19.4604 45V0C19.999 0 20.5376 0.0901895 21.0535 0.270567L35.6369 5.36899C37.5932 6.05293 38.9049 7.91083 38.9049 9.9978C38.9049 10.7441 38.9087 11.4878 38.9124 12.2287C38.9549 20.7174 38.9956 28.8283 33.4058 35.9974C29.7997 40.6223 25.2432 43.612 19.7362 44.9666C19.6457 44.9889 19.5531 45 19.4604 45Z\" fill=\"white\"\/><path d=\"M19.5 9C16.6116 9 14.7876 12.3058 11.1396 18.9175L10.6851 19.7413C7.65364 25.2356 6.13793 27.9827 7.50782 29.9913C8.87772 32 12.2669 32 19.0454 32H19.9546C26.733 32 30.1223 32 31.4922 29.9913C32.8621 27.9827 31.3464 25.2356 28.3149 19.7413L27.8604 18.9175C24.2124 12.3058 22.3884 9 19.5 9Z\" fill=\"#219780\"\/><path d=\"M19.5 14C20.1213 14 20.625 14.4822 20.625 15.0769V22.2564C20.625 22.8512 20.1213 23.3333 19.5 23.3333C18.8787 23.3333 18.375 22.8512 18.375 22.2564V15.0769C18.375 14.4822 18.8787 14 19.5 14Z\" fill=\"white\"\/><path d=\"M19.5 28C20.3284 28 21 27.3571 21 26.5641C21 25.7711 20.3284 25.1282 19.5 25.1282C18.6716 25.1282 18 25.7711 18 26.5641C18 27.3571 18.6716 28 19.5 28Z\" fill=\"white\"\/><path d=\"M19.5 14C20.1213 14 20.625 14.4822 20.625 15.0769V22.2564C20.625 22.8512 20.1213 23.3333 19.5 23.3333C18.8787 23.3333 18.375 22.8512 18.375 22.2564V15.0769C18.375 14.4822 18.8787 14 19.5 14Z\" stroke=\"white\" stroke-width=\"0.6\"\/><path d=\"M19.5 28C20.3284 28 21 27.3571 21 26.5641C21 25.7711 20.3284 25.1282 19.5 25.1282C18.6716 25.1282 18 25.7711 18 26.5641C18 27.3571 18.6716 28 19.5 28Z\" stroke=\"white\" stroke-width=\"0.6\"\/><\/svg><\/figure>\n      <h3>Continuous Red Teaming<\/h3>\n      <p>Real-time adversarial loops for ethical violations, HIPAA breaches, and jailbreaking.<\/p>\n  <\/div>\n  <div class=\"home-three-item shadow\">\n      <figure class=\"df\"><svg width=\"45\" height=\"45\" viewBox=\"0 0 45 45\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path opacity=\"0.5\" d=\"M3.75 22.5C3.75 13.6612 3.75 9.24175 6.49587 6.49587C9.24175 3.75 13.6612 3.75 22.5 3.75C31.3388 3.75 35.7583 3.75 38.5041 6.49587C41.25 9.24175 41.25 13.6612 41.25 22.5C41.25 31.3388 41.25 35.7583 38.5041 38.5041C35.7583 41.25 31.3388 41.25 22.5 41.25C13.6612 41.25 9.24175 41.25 6.49587 38.5041C3.75 35.7583 3.75 31.3388 3.75 22.5Z\" fill=\"white\" \/><path d=\"M25.3694 12.1306L23.4944 10.2556C22.9452 9.70646 22.0548 9.70646 21.5056 10.2556L19.6306 12.1306C19.0815 12.6798 19.0815 13.5702 19.6306 14.1194C20.0263 14.515 20.5991 14.6256 21.0938 14.4512V23.8902C20.9601 23.8341 20.8229 23.7838 20.6824 23.7399L18.0509 22.9176C17.0725 22.6118 16.4062 21.7057 16.4062 20.6805V19.8405C17.5148 19.3139 18.2812 18.1839 18.2812 16.875C18.2812 15.0628 16.8122 13.5938 15 13.5938C13.1878 13.5938 11.7188 15.0628 11.7188 16.875C11.7188 18.1839 12.4852 19.3139 13.5938 19.8405V20.6805C13.5938 22.9358 15.0594 24.9294 17.212 25.6021L19.8435 26.4244C20.5873 26.6568 21.0938 27.3457 21.0938 28.125V28.9095C19.9852 29.4361 19.2188 30.5661 19.2188 31.875C19.2188 33.6872 20.6878 35.1562 22.5 35.1562C24.3122 35.1562 25.7812 33.6872 25.7812 31.875C25.7812 30.6759 25.138 29.627 24.1778 29.0545C24.3982 28.7026 24.7402 28.4295 25.1565 28.2994L27.788 27.4771C29.9406 26.8044 31.4062 24.8108 31.4062 22.5555V21.9533C31.7816 21.8802 32.2183 21.7212 32.5948 21.3448C33.0369 20.9027 33.1791 20.3773 33.2344 19.9661C33.2816 19.6151 33.2814 19.1992 33.2813 18.8146V18.6854C33.2814 18.3008 33.2816 17.8849 33.2344 17.5339C33.1791 17.1227 33.0369 16.5973 32.5948 16.1552C32.1527 15.7131 31.6273 15.5709 31.2161 15.5156C30.8651 15.4684 30.4492 15.4686 30.0646 15.4687H29.9354C29.5508 15.4686 29.1349 15.4684 28.7839 15.5156C28.3727 15.5709 27.8473 15.7131 27.4052 16.1552C26.9631 16.5973 26.8209 17.1227 26.7656 17.5339C26.7184 17.8849 26.7186 18.3008 26.7187 18.6853V18.6854V18.8146V18.8146C26.7186 19.1992 26.7184 19.6151 26.7656 19.9661C26.8209 20.3773 26.9631 20.9027 27.4052 21.3448C27.7817 21.7212 28.2184 21.8802 28.5938 21.9533V22.5555C28.5938 23.5807 27.9275 24.4868 26.9491 24.7926L24.3176 25.6149C24.1771 25.6588 24.0399 25.7091 23.9062 25.7652V14.4512C24.4009 14.6256 24.9737 14.515 25.3694 14.1194C25.9185 13.5702 25.9185 12.6798 25.3694 12.1306Z\" fill=\"white\" \/><\/svg><\/figure>\n      <h3>Medical Cognitive Biases<\/h3>\n      <p>Identifying reasoning flaws like anchoring, confirmation, and availability bias.<\/p>\n  <\/div>\n  <div class=\"home-three-item shadow\">\n      <figure class=\"df\"><svg width=\"46\" height=\"46\" viewBox=\"0 0 46 46\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path opacity=\"0.4\" d=\"M6.70849 19.531H12.2477C16.7902 19.531 20.4893 15.8318 20.4893 11.2893V5.75016C20.4893 4.696 21.3518 3.8335 22.406 3.8335H30.5327C36.436 3.8335 41.2085 7.66683 41.2085 14.5093V31.491C41.2085 38.3335 36.436 42.1668 30.5327 42.1668H15.4677C9.56433 42.1668 4.79183 38.3335 4.79183 31.491V21.4477C4.79183 20.3935 5.65433 19.531 6.70849 19.531Z\" fill=\"white\"\/><path d=\"M15.7166 4.23578C16.5024 3.44995 17.8633 3.98661 17.8633 5.07911V11.7683C17.8633 14.5666 15.4866 16.8858 12.5924 16.8858C10.7716 16.9049 8.24161 16.9049 6.07578 16.9049C4.98328 16.9049 4.40828 15.6208 5.17495 14.8541C7.93495 12.0749 12.8799 7.07245 15.7166 4.23578Z\" fill=\"white\"\/><path d=\"M32.9083 29.7769L42.4492 38.3944C42.6544 38.5996 42.8596 39.01 42.8596 39.3177C42.8596 39.6255 42.757 40.0359 42.4492 40.241L41.5259 41.1644C41.3207 41.3695 40.9104 41.5747 40.6026 41.5747C40.2948 41.5747 39.8845 41.4721 39.6793 41.1644L30.6514 31.9312\" fill=\"white\"\/><path d=\"M32.9083 29.7769L42.4492 38.3944C42.6544 38.5996 42.8596 39.01 42.8596 39.3177C42.8596 39.6255 42.757 40.0359 42.4492 40.241L41.5259 41.1644C41.3207 41.3695 40.9104 41.5747 40.6026 41.5747C40.2948 41.5747 39.8845 41.4721 39.6793 41.1644L30.6514 31.9312\" stroke=\"white\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><path d=\"M22.9553 29.6737C26.1356 28.0323 28.8029 25.4676 30.5469 22.3899C30.7521 22.1847 31.1625 21.9795 31.4703 21.9795C31.778 21.9795 32.1884 22.0821 32.3936 22.3899L35.9842 25.8779C36.1894 26.0831 36.3946 26.4935 36.3946 26.8012C36.3946 27.109 36.292 27.5194 35.9842 27.7245C32.8039 29.366 30.1366 31.9307 28.3926 35.0084C28.1874 35.2136 27.777 35.4188 27.4692 35.4188C27.1615 35.4188 26.7511 35.3162 26.5459 35.0084L22.9553 31.5204C22.6475 31.3152 22.5449 31.0074 22.5449 30.5971C22.5449 30.2893 22.6475 29.9815 22.9553 29.6737Z\" fill=\"white\" stroke=\"white\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><path d=\"M32.1242 41.7394C32.1242 40.8051 31.3613 40.1045 30.4893 40.1045H22.315C21.4431 40.1045 20.6802 40.8051 20.6802 41.7394V43.3742H32.1242V41.7394Z\" fill=\"white\" stroke=\"white\" stroke-width=\"1.2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><path d=\"M19.8618 43.3745H32.9407H19.8618Z\" fill=\"white\"\/><path d=\"M19.8618 43.3745H32.9407\" stroke=\"white\" stroke-width=\"1.2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/figure>\n      <h3>Regulatory Hardening<\/h3>\n      <p>Enforcing 2026 legal standards (e.g., California AB 489) for emergency escalation and preventing AI impersonation of licensed professionals<\/p>\n  <\/div>\n  <div class=\"home-three-item shadow\">\n      <figure class=\"df\"><svg width=\"49\" height=\"48\" viewBox=\"0 0 49 48\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M1.5 24.0005L8.26514 24.0005\" stroke=\"white\" stroke-width=\"3\" stroke-linecap=\"round\" \/><path d=\"M39.8355 24.0005L46.6006 24.0005\" stroke=\"white\" stroke-width=\"3\" stroke-linecap=\"round\" \/><path d=\"M24.0503 46.5L24.0503 39.75\" stroke=\"white\" stroke-width=\"3\" stroke-linecap=\"round\" \/><path d=\"M24.0503 8.25L24.0503 1.5\" stroke=\"white\" stroke-width=\"3\" stroke-linecap=\"round\" \/><path d=\"M12.7752 4.5101C16.0921 2.59569 19.943 1.5 24.0504 1.5C36.5047 1.5 46.6009 11.5736 46.6009 24C46.6009 36.4264 36.5047 46.5 24.0504 46.5C11.5962 46.5 1.5 36.4264 1.5 24C1.5 19.9018 2.59814 16.0595 4.51685 12.75\" stroke=\"#6BC6B4\" stroke-width=\"3\" stroke-linecap=\"round\" \/><ellipse cx=\"23.79\" cy=\"23.7401\" rx=\"7.02533\" ry=\"7.00962\" fill=\"white\" \/><\/svg><\/figure>\n      <h3>System Specific Goals<\/h3>\n      <p>Build custom test suites and judging panels to match your specific clinical and business goals.<\/p>\n  <\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<figure class=\"wp-block-image size-full tac\"><a href=\"https:\/\/appliedaisummit.org\/from-guardrails-to-guardians-continuous-red-teaming-and-holistic-safety-for-agentic-healthcare-ai\/\" target=\"_blank\" rel=\" noreferrer noopener\"><img loading=\"lazy\" decoding=\"async\" width=\"890\" height=\"525\" src=\"https:\/\/pacific.ai\/staging\/3667\/wp-content\/uploads\/2026\/03\/guard_red.png\" alt=\"\" class=\"wp-image-2495\" srcset=\"https:\/\/pacific.ai\/staging\/3667\/wp-content\/uploads\/2026\/03\/guard_red.png 890w, https:\/\/pacific.ai\/staging\/3667\/wp-content\/uploads\/2026\/03\/guard_red-300x177.png 300w, https:\/\/pacific.ai\/staging\/3667\/wp-content\/uploads\/2026\/03\/guard_red-768x453.png 768w\" sizes=\"auto, (max-width: 890px) 100vw, 890px\" \/><\/a><\/figure>\n\n\n\n<section class=\"wp-block-group nocenter standart-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-group container\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-columns row nowrap_991 fdcr_991 is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\" id=\"testing-features\">\n<div class=\"wp-block-column col-12 col-lg-6 is-layout-flow wp-block-column-is-layout-flow\">\n<div style=\"padding-bottom: 53.8%;\" class=\"video-container shadow fig_add v1 fig_add_2 fig_yellow\">\n    <div class=\"video-overlay vimeo-wrapper img_radius\" style=\"background-image: url('..\/wp-content\/uploads\/2025\/11\/compre_2-1.webp');\" data-vimeo-id=\"1134622924\">\n        <button class=\"play-button\"><figure><img decoding=\"async\" src=\"..\/wp-content\/uploads\/2025\/07\/MainController.svg\" alt=\"\" loading=\"lazy\"><\/figure><\/button>\n    <\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column col-12 col-lg-6 is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"test-item\">\n  <h3>Benchmark results across versions, models, and environments with precision.<\/h3>\n  <ul class=\"list1\">\n    <li>Define multiple test suites per AI system<\/li>\n    <li>Reuse dozens off-the-shelf test datasets and benchmarks, or upload your own<\/li>\n    <li>Integrate directly in CI\/CD pipelines<\/li>\n    <li>Publish results &#038; metrics in your system\u2019s model card<\/li>\n  <\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-group container medhelm-section tac\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-columns row is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column col-12 is-layout-flow wp-block-column-is-layout-flow\">\n<h2 class=\"mb22\">MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks<\/h2>\n<div class=\"ht-title tac\">MedHELM, built by Stanford\u2019s Center for Research on Foundation Models, is a framework for assessing LLM performance for medical tasks, comprising a taxonomy with 5 categories, 22 subcategories, and 121 distinct real-world clinical tasks as well as 35 distinct benchmarks (14 private, 7 gated-access, and 14 public). Pacific AI is the first commercial implementation of MedHELM, making it easily usable by a larger audience.<\/div>\n<\/div>\n<\/div>\n\n\n\n<figure class=\"wp-block-image size-large shadow bd_rads\"><img decoding=\"async\" src=\"..\/wp-content\/uploads\/2025\/11\/medhelm.svg\" alt=\"\" class=\"wp-image-1329\" loading=\"lazy\" \/><\/figure>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-group container aiwork-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-columns row is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column col-12 is-layout-flow wp-block-column-is-layout-flow\">\n<h2>LangTest: Automated Evaluation of Custom <br>Language Models<\/h2>\n<\/div>\n<\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-group container\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-columns row nowrap_991 fdcr_991 is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\" id=\"testing-features\">\n<div class=\"wp-block-column col-12 col-lg-5 is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"lang-test\">\n  <p>LangTest, built by Pacific AI, can automatically generate and run 100+ test types, focused on evaluating the fairness and robustness of large language models. It supports testing common tasks like question answering, summarization, and classification across all major LLM models and APIs.<\/p>\n  <figure class=\"lt-img\"><img decoding=\"async\" src=\"..\/wp-content\/uploads\/2025\/11\/lang_pdf.webp\" alt=\"\" loading=\"lazy\"><\/figure>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column col-12 col-lg-7 is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"..\/wp-content\/uploads\/2025\/11\/langTest.svg\" alt=\"\" class=\"wp-image-1322\" loading=\"lazy\" \/><\/figure>\n<\/div>\n<\/div>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-group container aiwork-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-columns row is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column col-12 is-layout-flow wp-block-column-is-layout-flow\">\n<h2 class=\"mb22\">Red Teaming: Ensuring General &#038; Medical Safety<\/h2>\n<div class=\"ht-title mb0 tac\">The Red Teaming engine performs adversarial safety testing across 50+ categories and subcategories. It covers general-purpose safety risks (jailbreaking, prompt injection, illegal activity, \u2026), healthcare-specific safety risks (consent, patient privacy, conflicts of interest, \u2026), and medical cognitive biases (anchoring, availability bias, confirmation bias, \u2026).<\/div>\n<div class=\"red-wrap\">\n  <figure class=\"lt-img\"><img decoding=\"async\" src=\"..\/wp-content\/uploads\/2025\/11\/red_1.webp\" alt=\"\" loading=\"lazy\"><\/figure>\n  <figure class=\"lt-img\"><img decoding=\"async\" src=\"..\/wp-content\/uploads\/2025\/11\/red_2.webp\" alt=\"\" loading=\"lazy\"><\/figure>\n  <figure class=\"lt-img\"><img decoding=\"async\" src=\"..\/wp-content\/uploads\/2025\/11\/red_3.webp\" alt=\"\" loading=\"lazy\"><\/figure>\n  <figure class=\"lt-img\"><img decoding=\"async\" src=\"..\/wp-content\/uploads\/2025\/11\/red_4.webp\" alt=\"\" loading=\"lazy\"><\/figure>\n  <figure class=\"lt-img\"><img decoding=\"async\" src=\"..\/wp-content\/uploads\/2025\/11\/red_5.webp\" alt=\"\" loading=\"lazy\"><\/figure>\n  <figure class=\"lt-img\"><img decoding=\"async\" src=\"..\/wp-content\/uploads\/2025\/11\/red_6.webp\" alt=\"\" loading=\"lazy\"><\/figure>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-group container video-post-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-columns row is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column col-12 is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"row video-post-wrap\"><div class=\"col-12 col-md-12 col-lg-12\"><div class=\"video-post-item video-two-speakers\" style=\"background-image: url('https:\/\/pacific.ai\/staging\/3667\/wp-content\/uploads\/2025\/04\/bg_users.svg');\"><a target=\"_blank\" class=\"df\" href=\"https:\/\/pacific.ai\/staging\/3667\/identifying-and-mitigating-bias-in-ai-models-for-recruiting\/\"><div class=\"video-left\"><div class=\"video-post-title\">Identifying and Mitigating Bias in AI Models for Recruiting<\/div><\/div><div class=\"video-right\"><div class=\"video-speaker df\"><figure class=\"video-post-image\"><img decoding=\"async\" src=\"https:\/\/pacific.ai\/staging\/3667\/wp-content\/uploads\/2025\/04\/Kat.webp\" alt=\"Identifying and Mitigating Bias in AI Models for Recruiting\" loading=\"lazy\"><\/figure><div class=\"video-right-inner\"><div class=\"video-speaker-name\">Katie Bakewell<\/div><div class=\"video-speaker-position\">  Data Science Solutions Architect at NLP Logix<\/div><\/div><\/div><div class=\"video-speaker df\"><figure class=\"video-post-image\"><img decoding=\"async\" src=\"https:\/\/pacific.ai\/staging\/3667\/wp-content\/uploads\/2025\/04\/Jason.webp\" alt=\"Identifying and Mitigating Bias in AI Models for Recruiting\" loading=\"lazy\"><\/figure><div class=\"video-right-inner\"><div class=\"video-speaker-name\">Jason Safley<\/div><div class=\"video-speaker-position\">  Chief Technology Officer at Opptly<\/div><\/div><\/div><\/div><figure class=\"play-video-item\"><img decoding=\"async\" src=\"https:\/\/pacific.ai\/staging\/3667\/wp-content\/uploads\/2025\/04\/play.svg\" alt=\"\" loading=\"lazy\"><\/figure><\/a><\/div><\/div><\/div>\n<\/div>\n<\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-group testimonial-section no-bg\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-columns row is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column col-12 is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"testimonial-wrap tw-green\">\n  <figure><svg width=\"57\" height=\"53\" viewBox=\"0 0 57 53\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M17.8252 0.875C5.68889 12.632 0 25.1476 0 36.3357C0 45.438 6.82667 52.075 13.6533 52.075C19.7215 52.075 24.6519 47.1446 24.6519 41.0765C24.6519 33.3017 18.963 29.3194 10.6193 29.3194C10.6193 20.0276 13.4637 14.5283 22.3763 5.42611L17.8252 0.875ZM49.4933 0.875C37.357 12.632 31.6681 25.1476 31.6681 36.3357C31.6681 45.438 38.4948 52.075 45.3215 52.075C51.3896 52.075 56.32 47.1446 56.32 41.0765C56.32 33.3017 50.6311 29.3194 42.2874 29.3194C42.2874 20.0276 45.1319 14.5283 54.0444 5.42611L49.4933 0.875Z\" fill=\"#219780\" \/><\/svg><\/figure>\n  <p>We apply LangTest in two stages: during training, and every time we generate a match list in production. It gives us real-time fairness validation.<\/p>\n  <div class=\"testim-author\">Katie Bakewell, Data Science Solutions Architect at NLP Logix<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-group conatiner bottomcontact-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-group container\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-columns row is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column col-12 is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"bc-wrapper bc-gate tac\">\n    <h4 class=\"tac\">Partnership for the AI Era<\/h4>\n    <p>Whether you are evaluating your first high-impact AI system or scaling AI governance across the enterprise, Pacific AI provides the infrastructure, oversight, and expertise to move forward with confidence.<\/p>\n    <div class=\"tac\">\n        <a href=\"https:\/\/pacific.ai\/contact-us\/\" class=\"btn1 big-btn yell\">Schedule a Strategy Call<\/a>\n    <\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n<\/div><\/section>\n","protected":false},"excerpt":{"rendered":"<p>Gatekeeper: Automated LLM, ML, and Agentic AI Testing Run test suites and build CI\/CD release gates on real-world medical AI tasks, social and cognitive bias, red teaming, and regulatory compliance. You can\u2019t assume fairness. You have to test for it \u2014 by swapping genders, names, or cultural cues and tracking how the model\u2019s response shifts. [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"nf_dc_page":"","content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-2300","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Gatekeeper - Pacific AI<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Gatekeeper - Pacific AI\" \/>\n<meta property=\"og:description\" content=\"Gatekeeper: Automated LLM, ML, and Agentic AI Testing Run test suites and build CI\/CD release gates on real-world medical AI tasks, social and cognitive bias, red teaming, and regulatory compliance. You can\u2019t assume fairness. You have to test for it \u2014 by swapping genders, names, or cultural cues and tracking how the model\u2019s response shifts. 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