{"id":27396,"date":"2026-05-24T17:16:54","date_gmt":"2026-05-24T08:16:54","guid":{"rendered":"https:\/\/aireviewirush.com\/?p=27396"},"modified":"2026-05-24T17:16:54","modified_gmt":"2026-05-24T08:16:54","slug":"accelerating-enterprise-scale-ai-improvement-experimentation","status":"publish","type":"post","link":"https:\/\/aireviewirush.com\/?p=27396","title":{"rendered":"Accelerating Enterprise-Scale AI Improvement &#038; Experimentation"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div>\n<p data-ttstextid=\"4\"><em>With particular due to Arkaprabho Ghosh and David Reed.\u00a0<\/em><\/p>\n<p>As AI continues to remodel the enterprise panorama, the problem for giant organizations isn\u2019t simply adopting the know-how\u2014it\u2019s scaling it successfully. At Cisco, we acknowledged that whereas our groups had been keen to construct Retrieval-Augmented Technology (RAG) purposes, the method was typically fragmented. Builders had been spending months stitching collectively completely different elements of a RAG pipeline\u2014similar to loaders, splitters, embedding fashions, and vector databases. Every part <a name=\"_Int_dhZLITA4\"\/>carried its personal studying curve and operational overhead. The burden of evaluating an amazing variety of open-source instruments and endlessly experimenting with numerous configurations to seek out the correct match for particular use circumstances in the end led to inconsistent requirements, technical debt, and widespread \u201cknow-how fatigue\u201d.<\/p>\n<p>To unravel this, Cisco IT created <strong>DRIFT (Doc Retrieval and Ingestion Framework Toolkit)<\/strong>, a standardized, scalable platform that helps speedy improvement and experimentation in RAG workflows with the power to scale to satisfy enterprise-standard workloads.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_53 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title \" >Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\" role=\"button\"><label for=\"item-6a24e66736ad3\" ><span class=\"\"><span style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input aria-label=\"Toggle\" aria-label=\"item-6a24e66736ad3\"  type=\"checkbox\" id=\"item-6a24e66736ad3\"><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/aireviewirush.com\/?p=27396\/#Simplifying_the_AI_Journey\" title=\"Simplifying the AI Journey\">Simplifying the AI Journey<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/aireviewirush.com\/?p=27396\/#The_Cisco-on-Cisco_Benefit_Constructed_for_Scale_Safety\" title=\"The Cisco-on-Cisco Benefit: Constructed for Scale &amp; Safety\">The Cisco-on-Cisco Benefit: Constructed for Scale &amp; Safety<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/aireviewirush.com\/?p=27396\/#The_DRIFT_Methodology_Powering_Safe_RAG\" title=\"The DRIFT Methodology: Powering Safe RAG\">The DRIFT Methodology: Powering Safe RAG<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/aireviewirush.com\/?p=27396\/#Why_is_DRIFT_a_Sport-Changer\" title=\"Why is DRIFT a Sport-Changer:\">Why is DRIFT a Sport-Changer:<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/aireviewirush.com\/?p=27396\/#Driving_Actual-World_Affect\" title=\"Driving Actual-World Affect\">Driving Actual-World Affect<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/aireviewirush.com\/?p=27396\/#Wanting_Forward\" title=\"Wanting Forward\">Wanting Forward<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Simplifying_the_AI_Journey\"><\/span>Simplifying the AI Journey<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>DRIFT was constructed with a easy premise: utility groups ought to give attention to constructing AI-first experiences and enterprise logic, not on the heavy lifting of infrastructure. We&#8217;re eradicating the obstacles to entry by offering a platform that handles the complexity of information pipeline orchestration, permitting groups to fast-track their AI journey with out the necessity for in depth ramp-up time on underlying, advanced applied sciences.<\/p>\n<p>Whether or not you&#8217;re a hard-core developer requiring deep API-level management or a enterprise consumer on the lookout for an intuitive interface, DRIFT gives a real end-to-end improvement and experimentation atmosphere.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_Cisco-on-Cisco_Benefit_Constructed_for_Scale_Safety\"><\/span>The Cisco-on-Cisco Benefit: Constructed for Scale &amp; Safety<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>DRIFT is a robust instance of the Cisco-on-Cisco benefit\u2014the place Cisco software program is constructed to run on Cisco\u2019s personal AI infrastructure. Constructed on a cloud-native microservices structure and deployed on Kubernetes, DRIFT is engineered for agility, resilience, and enterprise-scale efficiency. Its asynchronous ingestion and file add structure is designed to deal with giant volumes of enterprise information effectively, enabling high-throughput pipelines with out sacrificing reliability.<\/p>\n<p>On the coronary heart of this basis are <b>Cisco AI <\/b><strong>PODs<\/strong> powered by <b>Cisco UCS-C885A<\/b> {hardware}. This offers DRIFT the high-performance compute spine wanted for demanding AI workloads similar to inferencing, embeddings, and reranking. By working <a name=\"_Int_j10zSdrv\"\/>on-premise throughout a number of <b>Cisco Knowledge Facilities<\/b>, DRIFT combines scale, robust safety, excessive availability, and operational management in a method that meets the wants of enterprise AI.<\/p>\n<p>The result&#8217;s greater than only a trendy AI platform\u2014it&#8217;s a clear demonstration of how Cisco AI software program and Cisco AI infrastructure come collectively to ship production-ready efficiency at scale. With DRIFT working on Cisco AI PODs constructed on UCS-C885A, Cisco is showcasing an end-to-end AI stack that&#8217;s scalable, safe, and purpose-built for enterprise innovation.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"lazy lazy-hidden aligncenter wp-image-491919\" data-lazy-type=\"image\" src=\"https:\/\/blogs.cisco.com\/gcs\/ciscoblogs\/1\/2026\/05\/DRIFT_Platform_Architecture.png\" alt=\"\" width=\"1061\" height=\"707\"><noscript><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-491919\" src=\"https:\/\/blogs.cisco.com\/gcs\/ciscoblogs\/1\/2026\/05\/DRIFT_Platform_Architecture.png\" alt=\"\" width=\"1061\" height=\"707\"><\/noscript><\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_DRIFT_Methodology_Powering_Safe_RAG\"><\/span>The DRIFT Methodology: Powering Safe RAG<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>DRIFT streamlines the trail from uncooked doc to clever assistant via a strong, modular pipeline structure:<\/p>\n<ul>\n<li><strong>Doc Preprocessing:<\/strong> We assist numerous doc sources and codecs, standardizing numerous enterprise information right into a constant, model-ready format. We even leverage Imaginative and prescient Language Fashions (VLM) to transform photos inside paperwork into textual content representations.<\/li>\n<li><strong>Clever Splitting and Hybrid Processing:<\/strong>\u00a0DRIFT helps quite a lot of splitting algorithms, together with the power to protect a doc\u2019s structural formatting throughout the splitting course of. For paperwork with blended content material, it additionally permits a hybrid method that selectively processes photos\u2014serving as a extremely efficient value optimization method.<\/li>\n<li><strong>Embedding <\/strong><strong>and Ingestion:<\/strong> Groups can select from a set of ordinary embedding fashions or carry their very own. We provide seamless integration with each shared multi-tenant in addition to devoted Vector databases to go well with quite a lot of enterprise use circumstances. Our platform helps each key phrase and semantic search algorithms, making certain environment friendly ingestion and retrieval that meet enterprise SLAs.<\/li>\n<li><strong>Retrieval and Reranking:<\/strong> DRIFT permits for configurable hybrid search and metadata filtering, making certain that retrieved information is exact. Our reranking capabilities additional refine outcomes based mostly on relevance, considerably rising accuracy.<\/li>\n<li><strong>Adaptive Structure:<\/strong> Designed for the longer term, DRIFT helps evolving use circumstances, together with Agentic RAG and Graph RAG, making certain enterprise purposes can scale as AI architectures advance.<\/li>\n<li><strong>Constructed-in Testing and Analysis:<\/strong> Builders can check retrievers towards pattern queries and work together with LLMs immediately inside the platform to validate generative summaries earlier than deployment.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Why_is_DRIFT_a_Sport-Changer\"><\/span>Why is DRIFT a Sport-Changer:<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>API-First Structure:<\/strong>\u00a0DRIFT was constructed from the bottom up with an API-first method. We offer complete, ready-to-use APIs for each step of the lifecycle\u2014together with doc add, ingestion, retrieval, and configuration\u2014enabling seamless integration into current enterprise purposes and workflows.<\/li>\n<li><strong>Full Transparency and Experimentation:<\/strong> We now have moved away from the \u201cblack-box\u201d method to a real end-to-end improvement and experimentation platform that empowers builders with full visibility. Groups have full management over configuration selections for all elements of their pipelines, permitting them to fine-tune, check, and optimize for optimum accuracy.<\/li>\n<li><strong>Curated, Accountable AI:<\/strong> We get rid of the guesswork of evaluating open-source libraries. DRIFT gives fashions which are already vetted and accepted by Cisco\u2019s Accountable AI (RAI) and governance groups.<\/li>\n<li><strong>Lowered Know-how Fatigue:<\/strong> By offering a curated suite of industry-standard elements, we save groups from \u201cevaluation paralysis.\u201d We deal with the combination to allow them to give attention to innovation.<\/li>\n<li><strong>Flexibility and Scalability:<\/strong> Whereas we offer normal, high-quality choices, DRIFT stays absolutely versatile. Groups can combine their very own customized Vector Databases or fine-tuned fashions\u2014similar to these specialised for Cisco-specific monetary or technical terminology.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Driving_Actual-World_Affect\"><\/span>Driving Actual-World Affect<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Since its MVP launch in January 2025, the adoption of DRIFT has been extraordinary. Inside the first 12 months, we&#8217;ve seen important adoption with over <strong>600 builders<\/strong> having constructed greater than <strong>1,500 pipelines<\/strong> throughout numerous enterprise models, together with Finance, Provide Chain, Engineering, Authorized, IT Operations, and Individuals and Communities.<\/p>\n<p>By decreasing the time required to construct an information pipeline from months to minutes, DRIFT has change into a important engine for Cisco\u2019s AI technique, enabling groups to experiment quickly and ship high-accuracy, AI-first options at scale.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Wanting_Forward\"><\/span>Wanting Forward<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The success of DRIFT is a testomony to the collaborative spirit at Cisco. By working throughout groups\u2014from\u00a0IT &amp; Operations\u00a0to our numerous enterprise models\u2014we&#8217;ve created a instrument that not solely powers inner AI assistants (like our company-wide HR assistant) but additionally gives a basis for future product integrations.<\/p>\n<p>As we proceed to iterate, DRIFT stays dedicated to serving to Cisco groups transfer sooner, experiment extra, and ship the following era of AI-powered options to our staff, prospects and companions.<\/p>\n<\/p><\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>With particular due to Arkaprabho Ghosh and David Reed.\u00a0 As AI continues to remodel the enterprise panorama, the problem for giant organizations isn\u2019t simply adopting the know-how\u2014it\u2019s scaling it successfully. At Cisco, we acknowledged that whereas our groups had been keen to construct Retrieval-Augmented Technology (RAG) purposes, the method was typically fragmented. Builders had been [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":27398,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-27396","post","type-post","status-publish","format-standard","has-post-thumbnail","category-cloud-computing"],"_links":{"self":[{"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/posts\/27396","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=27396"}],"version-history":[{"count":1,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/posts\/27396\/revisions"}],"predecessor-version":[{"id":27397,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/posts\/27396\/revisions\/27397"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/media\/27398"}],"wp:attachment":[{"href":"https:\/\/aireviewirush.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=27396"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=27396"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=27396"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}