{"id":2325,"date":"2026-03-16T12:18:11","date_gmt":"2026-03-16T11:18:11","guid":{"rendered":"https:\/\/cogita.ai\/?post_type=case-study&#038;p=2325"},"modified":"2026-04-02T12:19:10","modified_gmt":"2026-04-02T10:19:10","slug":"ai-powered-insurance-verification-from-manual-scraping-to-production-in-under-3-months","status":"publish","type":"case-study","link":"https:\/\/cogita.ai\/pl\/case-study\/ai-powered-insurance-verification-from-manual-scraping-to-production-in-under-3-months\/","title":{"rendered":"Weryfikacja ubezpiecze\u0144 oparta na sztucznej inteligencji - od r\u0119cznego skrobania do produkcji w mniej ni\u017c 3 miesi\u0105ce"},"content":{"rendered":"<p><strong>A US dental services company replaced fragile, hand-coded scraping scripts with an AI-driven automation that verifies patient insurance data across dozens of providers - with near-perfect accuracy.<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Problem<\/h3>\n\n\n\n<p>The company's daily operations depended on verifying insurance eligibility for every patient - pulling coverage details, plan specifics, and medical history from dozens of different insurer systems.<\/p>\n\n\n\n<p>The existing approach was brute force: engineers manually built and maintained custom scraping scripts for each insurance provider's portal. Every time a provider changed their UI, updated their login flow, or restructured their data - a script broke. Someone had to find the issue, rewrite the code, test it, and redeploy.<\/p>\n\n\n\n<p>It was a never-ending maintenance treadmill. Slow, fragile, expensive to maintain, and impossible to scale as the company added new insurance partners.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Wyzwanie<\/h3>\n\n\n\n<p>Insurance provider portals are not built for automation. They vary wildly in structure, authentication flows, and data formats. They change without warning. Traditional RPA and scraping tools demand rigid, rule-based scripts that shatter the moment a button moves or a form field changes its label.<\/p>\n\n\n\n<p>The company needed a system that could adapt - not one that required a developer every time a portal updated its CSS.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Rozwi\u0105zanie<\/h3>\n\n\n\n<p>We designed an intelligent automation pipeline combining AI-driven browser interaction with multi-stage data processing:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Playwright<\/strong> as the core automation layer - an AI agent that navigates insurer portals the way a human would: logging in, finding the right screens, interpreting page layouts, and extracting the relevant data.<\/li>\n\n\n\n<li><strong>Multi-stage LLM processing pipeline<\/strong> that takes raw extracted data - combining insurance plan details and patient medical history from multiple sources - and transforms it into clean, structured output.<\/li>\n\n\n\n<li><strong>Direct JSON integration<\/strong> with the client's core system, delivering verified insurance data ready for immediate downstream processing and analysis.<\/li>\n<\/ul>\n\n\n\n<p>The result is a system that handles the full verification workflow end-to-end: login, navigation, extraction, processing, and delivery - without human intervention.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Wyniki<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Metryczny<\/th><th>Wp\u0142yw<\/th><\/tr><\/thead><tbody><tr><td><strong>Accuracy<\/strong><\/td><td>Nearly 100% on processed insurance records<\/td><\/tr><tr><td><strong>Time to production<\/strong><\/td><td>~2\u20133 months from project start<\/td><\/tr><tr><td><strong>Manual script maintenance<\/strong><\/td><td>Eliminated - AI adapts to portal changes<\/td><\/tr><tr><td><strong>Integration<\/strong><\/td><td>Direct JSON output to client's core system<\/td><\/tr><tr><td><strong>Status<\/strong><\/td><td>In production, running daily since early 2025<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>What used to require a team of engineers maintaining a patchwork of fragile scripts is now handled by an AI system that runs autonomously, every day.<\/p>\n\n\n\n<p>As the client noted on Clutch: the project delivered a production version within approximately 2\u20133 months, with almost 100% accuracy. The COGITA team adapted quickly to the client's workflows and responded fast when issues arose (see the full review <a href=\"https:\/\/clutch.co\/go-to-review\/e1fd37e6-14d2-472a-b8e0-e11debd32929\/397183\" rel=\"nofollow noopener\" target=\"_blank\">here<\/a>).<\/p>","protected":false},"excerpt":{"rendered":"<p>A US dental services company replaced fragile, hand-coded scraping scripts with an AI-driven automation that verifies patient insurance data across dozens of providers - with near-perfect accuracy. Problem The company's daily operations depended on verifying insurance eligibility for every patient - pulling coverage details, plan specifics, and medical history from dozens of different insurer systems. [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":true},"class_list":["post-2325","case-study","type-case-study","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/cogita.ai\/pl\/wp-json\/wp\/v2\/case-study\/2325","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cogita.ai\/pl\/wp-json\/wp\/v2\/case-study"}],"about":[{"href":"https:\/\/cogita.ai\/pl\/wp-json\/wp\/v2\/types\/case-study"}],"wp:attachment":[{"href":"https:\/\/cogita.ai\/pl\/wp-json\/wp\/v2\/media?parent=2325"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}