{"componentChunkName":"component---src-templates-case-study-js","path":"/case_studies/leading-provider-of-biopharma-precision-medicine-intelligence","result":{"data":{"caseStudy":{"title":"Leading Provider of Biopharma Precision Medicine Intelligence","slug":"leading-provider-of-biopharma-precision-medicine-intelligence","tags":[{"id":"77526ebd-0f00-50a0-bab0-f1f5af85243b"}],"acf":{"the_problem":"The current platform was not scaling well, was costly to run, and hard to operate.","the_problem_sub_description":"<ul>\n<li>Inaccurate Ideal Customer Profiles (ICPs) and the need for frequent user modifications strained development resources.</li>\n<li>While users could filter ICPs to refine results, the process of defining and applying these filters was resource-intensive, causing cost spikes, slow performance, and user frustration.</li>\n<li>This was compounded by the daily rebuilding of large data sets and an app architecture not designed for high transaction volumes.</li>\n</ul>\n","the_result":"The delivered solution significantly increased client satisfaction and performance, leading to increased usage and revenue.","the_result_sub_description":"<ul>\n<li>Immediately following deployment, the new architecture garnered widespread user praise for its enhanced ease of use, performance, and result quality.</li>\n<li>This success translated into higher client satisfaction, increased system adoption, and greater revenue.</li>\n<li>Furthermore, strategic use of autoscaling capabilities optimized costs, reducing average CPU and memory usage by half.</li>\n</ul>\n","the_solution":"Our team set out to rework the entire architecture to improve performance, reduce costs, and overhaul critical functionality.","the_solution_sub_description":"<ul>\n<li><span style=\"font-weight: 400;\">The team optimized Ideal Customer Profile (ICP) management by shifting from customized data sets to regional ICPs (e.g., global, North America, Europe). </span></li>\n<li><span style=\"font-weight: 400;\">This change enabled faster filter creation for users and allowed the infrastructure to efficiently rebuild and cache daily results after data ingestion, significantly improving display speed.</span></li>\n<li><span style=\"font-weight: 400;\">The core of the architecture was reworked, migrating logic from Google Kubernetes Engine (GKE) to Dataflow, a Google Cloud Platform (GCP) serverless service. This resulted in significantly faster, less costly, and more resource-efficient processing with native autoscaling.</span></li>\n</ul>\n","featured_image":{"sizes":{"thumbnail":"https://cms.mangochango.com/app/uploads/2025/07/ClientSuccessStory_SMG_image-150x150.jpg","medium":"https://cms.mangochango.com/app/uploads/2025/07/ClientSuccessStory_SMG_image-300x167.jpg"}}}}},"pageContext":{"id":"3d533ee2-ee7b-5660-8fa3-9965e3cc0016"}}}