{"templateName":"blog-page","cssClassNames":"blog-page page basicpage summit-page","canonicalLink":"https://www.snowflake.com/en/blog/agentic-intelligence-contract-review-snowflake/","robotsTags":["index","follow"],"allowedRenditionsWidth":["320","480","640","768","960","1200","1440","1920"],"language":"en","description":"See how Snowflake used agentic AI to reduce contract review time by 70%, improve audit coverage and scale revenue-control workflows.","title":"Agentic Intelligence for Contract Review on Snowflake","analyticsPageType":"homepage","analyticsCategory":"general","analyticsSubCategory":"","excludeFromAnalytics":false,":mappedPath":"/en/blog/agentic-intelligence-contract-review-snowflake/",":type":"snowflake-site/components/structure/page",":items":{"root":{"columnCount":12,"columnClassNames":{"experiencefragment-banner":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-sub-header":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-pre-footer":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-header":"aem-GridColumn aem-GridColumn--default--12","markup_editor-table":"aem-GridColumn aem-GridColumn--default--12","responsivegrid":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-footer":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12","container_47873732":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12",":items":{"experiencefragment-banner":{"id":"experiencefragment-5e11d31c7a","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/pushdown-banner/pushdown-banner-blank/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment","xfModelPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/pushdown-banner/pushdown-banner-blank.xfmodel.json"},"experiencefragment-header":{"id":"experiencefragment-94fc3d0b52","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment","xfModelPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master.xfmodel.json","languageNavPath":"/content/snowflake-site/global/en/blog/agentic-intelligence-contract-review-snowflake.languagenav.json","appliedCssClassNames":"snowflake-sticky-nav-host"},"experiencefragment-sub-header":{"id":"experiencefragment-51db7891a6","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/sub-navigation/master/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment","xfModelPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/sub-navigation/master.xfmodel.json"},"responsivegrid":{"columnCount":12,"columnClassNames":{"container_breadcrumb":"aem-GridColumn aem-GridColumn--default--12","container_main_content":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12",":items":{"container_breadcrumb":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"breadcrumb":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"blog-page-breadcrumb-indentation",":type":"snowflake-site/components/container",":items":{"breadcrumb":{"id":"breadcrumb-979789106f","breadcrumbItems":[{"title":"Blog","path":"/en/blog/","active":false},{"title":"AI & ML","path":"/en/blog/ai-ml/","active":false},{"title":"How Snowflake Reduced Contract Review Time by 70% with Agentic Intelligence ","path":"/en/blog/agentic-intelligence-contract-review-snowflake/","active":false}],":type":"snowflake-site/components/blog/breadcrumb"}},":itemsOrder":["breadcrumb"],"appliedCssClassNames":"snowflake-container"},"container_main_content":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"flexible_column_container":"aem-GridColumn aem-GridColumn--default--12","related_content":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"main-content",":type":"snowflake-site/components/container",":items":{"flexible_column_container":{"id":"flexible-column-container-3d017fafcb","propertiesId":"snowflake-blog-template-main-container","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":true,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-0e1d4a108b",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container_hero":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"blog_hero":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-a1e5141efa",":type":"snowflake-site/components/container",":items":{"blog_hero":{"id":"blog-hero-45a5b3455d","showClaude":true,"showChatGpt":true,"authors":[{"authorImage":{"id":"image-1732599e11","height":"594","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--efc9b3d7-2d9d-493e-a3d3-205665b2fa7b/nikolai-profile.jpg?quality=85&preferwebp=true","alt":"Nikolai Scholz","lazyEnabled":true,"isLcpImage":false,"width":"594",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-8417318242","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/nikolai-scholz/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Nikolai Scholz"}},{"authorImage":{"id":"image-cbe23cd10a","height":"428","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a5b52f4f-e3a7-424f-b5a1-908d41158cf8/pooja-keshan.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"440",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-1fb20f95e3","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/pooja-keshan/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Pooja Keshan"}}],"image":{"id":"image-b0ea943ace","height":"720","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--12bd185f-91bd-4eed-8ef1-736affb6797e/03-general.jpg?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"1680",":type":"snowflake-site/components/image"},"timeToRead":"6","publicationDate":"AUG 05, 2026","tag":{"tagText":"AI & ML","tagColor":"#C54985"},"title":{"lines":["How Snowflake Reduced Contract Review Time by 70% with Agentic Intelligence "],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"]},"responsivegrid_content":{"columnCount":12,"columnClassNames":{"image":"aem-GridColumn aem-GridColumn--default--12","blog_text":"aem-GridColumn aem-GridColumn--default--12","image_1062066405":"aem-GridColumn aem-GridColumn--default--12","blog_text_1769981022":"aem-GridColumn aem-GridColumn--default--12","blog_text_2004631924":"aem-GridColumn aem-GridColumn--default--12","blog_text_971992418":"aem-GridColumn aem-GridColumn--default--12","quote_item":"aem-GridColumn aem-GridColumn--default--12","image_1994155271":"aem-GridColumn aem-GridColumn--default--12","blog_text_1935050943":"aem-GridColumn aem-GridColumn--default--12","blog_text_611322206":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-e556995a4d","text":"\u003Cp\u003E\u003Ci\u003EThis is an internal case study sharing how Snowflake's Forward Deployed Engineer team built an AI-powered contract review agent on top of the Snowflake platform. The tool is used internally at Snowflake and is shared here as an example of how frontier AI capabilities can transform high-stakes, regulated workflows.\u003C/i\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003ESnowflake Technology used: \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/data-integration/openflow/about\"\u003ESnowflake Openflow\u003C/a\u003E, \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/ai-documents\"\u003ESnowflake Cortex AI\u003C/a\u003E, \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents\"\u003ECortex Agent\u003C/a\u003E, \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/sql-reference/functions/data_agent_run-snowflake-cortex\"\u003EData Agent Run\u003C/a\u003E, \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_extract\"\u003ESnowflake AI Extract\u003C/a\u003E, \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EStreamlit in Snowflake\u003C/a\u003E and \u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-cowork\" target=\"_blank\"\u003ESnowflake CoWork\u003C/a\u003E.\u003C/i\u003E\u003C/p\u003E\r\n\u003Ch2\u003ETraditional review process\u003C/h2\u003E\r\n\u003Cp\u003EIn the high-stakes world of enterprise software, the quote-to-cash lifecycle hinges entirely on the speed and accuracy of contract review. Traditionally, scaling accounting operations means adding manual headcount to painstakingly review bespoke customer order forms, MSAs and amendments. This manual approach creates an operational bottleneck and can introduce human-error risks when identifying nonstandard clauses that impact revenue recognition.\u003C/p\u003E\r\n\u003Cp\u003EAt enterprise scale, Snowflake processes a high volume of customer contracts — ranging from structured capacity commitments to specialized marketplace agreements. Each of these contractual events carries unique commercial terms — such as capacity commitments, discount structures, billing frequencies and incentive clauses — that must be meticulously reviewed for revenue recognition compliance and internal audit controls.\u003C/p\u003E\r\n\u003Cp\u003EWhen it comes to testing internal controls over this process, auditors read each PDF line by line, check terms for standard/nonstandard implications and review the relevant revenue treatment for appropriateness. But as Snowflake's deal volume grows, this model doesn't scale. Thousands of order forms per quarter, each requiring careful human review …\u003C/p\u003E\r\n\u003Cp\u003E"The audit team was spending hours manually scanning PDFs to find nonstandard terms and yet only providing assurance on a sample of the entire population," says Amrita Kapoor, VP Internal Audit. "We needed to flip the model. By letting AI handle the exhaustive reading across the entire population, our auditors can focus their expertise on exception handling — ultimately providing significantly expanded assurance and eventually also passing these time savings back to process owners.”\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_971992418":{"id":"blog-text-df8222a9a3","text":"\u003Ch2\u003EBuilding an auditor's copilot, not a black box\u003C/h2\u003E\r\n\u003Cp\u003EThe team's design principle was straightforward: automate the detection layer, but keep auditors in complete control of what counts as "nonstandard." This wasn't about replacing judgment; it was about removing the tedious hours spent finding the needle in the haystack so experts could focus on deciding what to do about it.\u003C/p\u003E\r\n\u003Ch4\u003EThe Contract Review Agent works in three layers:\u003C/h4\u003E\r\n\u003Cp\u003E\u003Cb\u003EIngest and extract:\u003C/b\u003E Order form PDFs flow from source systems through Google Drive via Snowflake Openflow into a Snowflake stage. A Cortex Agent powered by Cortex AI Functions handles layout extraction and uses AI Extract to pull structured fields: customer name, capacity amount, discount terms, payment schedules and dozens of revenue-relevant data points.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EClassify against a playbook and flag novel terms:\u003C/b\u003E Rather than relying solely on the model's judgment, the agent evaluates every extracted term against a user-managed playbook — a living set of rules that define what "standard" and "nonstandard" look like. The playbook is a governed Snowflake table that the audit team owns and edits directly, no engineering ticket required. The agent scores every clause against it, producing a classification of standard or nonstandard — with confidence scores, clause-level excerpts, page references and a natural-language explanation of why a term was flagged.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ESurface, review and learn:\u003C/b\u003E Findings are presented in a reviewer-centric application with an admin dashboard for portfolio-level visibility and a contract detail view for deep-dive review. Auditors can approve, override or escalate every finding. Every correction — whether to an extracted value or a classification — is logged and persisted as extraction tips, similar to playbook rules, that are stored in long-term memory and fed into the agent on every subsequent run. Custom AI Extract questions for each schema field allow auditors to refine how the agent parses specific terms, tightening precision and recall with each batch. A parallel Snowflake CoWork agent gives stakeholders a natural-language interface to audit status and nonstandard rates without needing direct app access.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item":{"id":"quote-item-17439eb9b4","alignment":"left","layout":"card","quoteSourceName":"Charles Xu","quoteSourceTitle":"Engineering Manager of Applied AI","showQuoteIcon":false,"quote":"\"The agent is not just parsing but reasoning. It explains why a term is considered nonstandard, cites the playbook rule and presents the contract excerpt, letting the reviewer confirm or correct. Every decision is logged.\"","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_1769981022":{"id":"blog-text-62313d88a5","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-0cc14a08bb","height":"950","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--5ee6251e-d212-4b3a-a346-50414386e1b1/review-contracts.png?quality=85&preferwebp=true","alt":"review contracts","lazyEnabled":true,"isLcpImage":false,"width":"1934",":type":"snowflake-site/components/image"},"blog_text_1935050943":{"id":"blog-text-dda6b47904","text":"\u003Cp\u003E\u003Csub\u003E\u003Ci\u003EAI-generated illustrative example. Not based on actual customer or company data.\u003C/i\u003E\u003C/sub\u003E\u003C/p\u003E\r\n\u003Ch2\u003EThe playbook: auditors write the rules\u003C/h2\u003E\r\n\u003Cp\u003EMost AI contract tools ship with a fixed model of what's "normal." That's a problem in audit, where the definition of nonstandard evolves with business context, regulatory changes and deal structure.\u003C/p\u003E\r\n\u003Cp\u003EThe playbook manager puts control directly in the auditors' hands. It's an editable rules engine stored in Snowflake, accessible through the application UI:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003EEach rule defines a term, its detection criteria and its classification (standard vs. nonstandard).\u003C/li\u003E\r\n\u003Cli\u003EEvery change is logged to an immutable audit trail — action, rule ID, who changed it, when, old value, new value.\u003C/li\u003E\r\n\u003Cli\u003ERules take effect immediately on the next processing run, without code changes or redeployment.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EThis means the audit team can respond to a new contract pattern in minutes. When a novel discount structure shows up in Q4 deals, auditors add a rule and the agent starts flagging it across the entire corpus. The playbook evolves at the speed of business, not at the speed of engineering sprints.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1062066405":{"id":"image-925e59a0f8","height":"800","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b9aef977-7512-44a2-b9e3-029c40d5aa79/playbook.png?quality=85&preferwebp=true","alt":"playbook","lazyEnabled":true,"isLcpImage":false,"width":"1938",":type":"snowflake-site/components/image"},"blog_text_611322206":{"id":"blog-text-3c426d6284","text":"\u003Cp\u003E\u003Ci\u003E\u003Csub\u003EAI-generated illustrative example. Not based on actual customer or company data.\u003C/sub\u003E\u003C/i\u003E\u003C/p\u003E\r\n\u003Ch2\u003ECatching what you didn't know to look for\u003C/h2\u003E\r\n\u003Cp\u003ERule-based detection catches known patterns. But contracts are creative documents — new clauses appear that no existing rule anticipates. This is where novel term detection comes in.\u003C/p\u003E\r\n\u003Cp\u003EThe system uses a two-layer approach. First, a corpus of known-standard contract language is embedded and indexed. When the agent encounters a clause that is semantically distant from this corpus, it's flagged as potentially novel. Second, flagged terms are evaluated against existing playbook rules to separate genuinely novel language from known patterns.\u003C/p\u003E\r\n\u003Cp\u003ESurfaced novel terms land in a separate view where auditors label them: meaningful anomaly or routine noise. These labels flow back into the system, teaching it what to prioritize in future runs. Over time, the novel term detector gets sharper, tuned by the people who understand the contracts best.\u003C/p\u003E\r\n\u003Ch2\u003EResults\u003C/h2\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EReview time cut by 70%:\u003C/b\u003E What previously took multiple levels of prep is now handled by AI and instead of days is hours.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EFull coverage without linear headcount growth:\u003C/b\u003E Thousands of order forms reviewed for revenue implications per quarter without scaling the audit team in lockstep with deal volume.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAuditable by design:\u003C/b\u003E Every extraction, classification, rule change and correction is logged with full provenance — the kind of evidence trail external auditors expect.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EImproving accuracy over time:\u003C/b\u003E Each review cycle feeds corrections and labels back into the system, compounding accuracy with every batch.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1994155271":{"id":"image-a7496db9ce","height":"684","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--ccc8b38a-1a6d-4504-b8ae-73720c20da0c/novelty-analysis.png?quality=85&preferwebp=true","alt":"novelty","lazyEnabled":true,"isLcpImage":false,"width":"1934",":type":"snowflake-site/components/image"},"blog_text_2004631924":{"id":"blog-text-397a0d41e2","text":"\u003Cp\u003E\u003Ci\u003E\u003Csub\u003EAI-generated illustrative example. Not based on actual customer or company data.\u003C/sub\u003E\u003C/i\u003E\u003C/p\u003E\r\n\u003Ch2\u003EBeyond customer contracts\u003C/h2\u003E\r\n\u003Cp\u003EThe same architecture is being extended to other agreement types. The pattern generalizes: define an extraction schema, write playbook rules for what "standard" means in that document type and let the agent do the exhaustive reading while experts focus on judgment.\u003C/p\u003E\r\n\u003Cp\u003E"The only way contract review scales with the business is through this kind of system," says Nikolai Scholz, PM Unstructured Data Application. "Without it, you grow your operational team linearly. With it, you keep the team lean while expanding coverage — and the system gets smarter every quarter because the experts are teaching it."\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","blog_text_971992418","quote_item","blog_text_1769981022","image","blog_text_1935050943","image_1062066405","blog_text_611322206","image_1994155271","blog_text_2004631924"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"author_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-c9f4c9bcf5",":type":"snowflake-site/components/container",":items":{"author_chip":{"id":"author-chip-a528abc285","title":{"id":"title","type":"heading2","lines":["Learn more about the authors"],":type":"snowflake-site/components/title-v2"},"authors":[{"authorImage":{"id":"image-1732599e11","height":"594","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--efc9b3d7-2d9d-493e-a3d3-205665b2fa7b/nikolai-profile.jpg?quality=85&preferwebp=true","alt":"Nikolai Scholz","lazyEnabled":true,"isLcpImage":false,"width":"594",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-8417318242","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/nikolai-scholz/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Nikolai Scholz"},"authorTitle":"Staff Product Manager, Applied AI at Snowflake"},{"authorImage":{"id":"image-cbe23cd10a","height":"428","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a5b52f4f-e3a7-424f-b5a1-908d41158cf8/pooja-keshan.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"440",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-1fb20f95e3","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/pooja-keshan/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Pooja Keshan"},"authorTitle":"Internal Audit Manager at Snowflake "}],":type":"snowflake-site/components/blog/author-chip"}},":itemsOrder":["author_chip"],"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium"},"container_share_article":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"share_article":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-ccc15e28c6",":type":"snowflake-site/components/container",":items":{"share_article":{"id":"share-article-8d04c54c3c","linkedInShareUrl":"https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Fwww.snowflake.com%2Fcontent%2Fsnowflake-site%2Fglobal%2Fen%2Fblog%2Fagentic-intelligence-contract-review-snowflake&title=How+Snowflake+Reduced+Contract+Review+Time+by+70%25+with+Agentic+Intelligence+","twitterShareUrl":"https://x.com/intent/post?url=https%3A%2F%2Fwww.snowflake.com%2Fcontent%2Fsnowflake-site%2Fglobal%2Fen%2Fblog%2Fagentic-intelligence-contract-review-snowflake&text=How+Snowflake+Reduced+Contract+Review+Time+by+70%25+with+Agentic+Intelligence+","facebookShareUrl":"https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fwww.snowflake.com%2Fcontent%2Fsnowflake-site%2Fglobal%2Fen%2Fblog%2Fagentic-intelligence-contract-review-snowflake",":type":"snowflake-site/components/blog/share-article"}},":itemsOrder":["share_article"],"appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["container_hero","responsivegrid_content","responsivegrid_premium_content_banner","container_author_chip","container_share_article"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-73fcf44aec",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"blog_table_of_content":{"id":"blog-table-of-content-437e451b9b",":type":"snowflake-site/components/blog/blog-table-of-content","tableOfContents":[]}},":itemsOrder":["blog_table_of_content"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":true,"isActiveTOC":false},"related_content":{"id":"related-content-999fd9ce73","relatedContent":[],":type":"snowflake-site/components/blog/related-content","isBlogPage":true}},":itemsOrder":["flexible_column_container","related_content"],"appliedCssClassNames":"snowflake-container"}},":itemsOrder":["container_breadcrumb","container_main_content"],":type":"wcm/foundation/components/responsivegrid"},"container_47873732":{"additionalClasses":"section--blog-newsletter","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-7e46c18e04",":type":"snowflake-site/components/container",":items":{"flexible_column_cont":{"id":"flexible-column-container-438f4886a7","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"small","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"section--blog-newsletter","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-194178ffff",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"marketo_v2":{"id":"marketo-v2-0ac294ef57","marketoForm":{"hidden":null,"formId":"3320","edit":false,"successUrl":null,"script":null,"values":null},"title":{"id":"title","type":"heading3","lines":["Subscribe to our blog newsletter","Get the best, coolest and latest delivered to your inbox each week"],":type":"snowflake-site/components/title-v2"},"munchkinId":"252-RFO-227","serverInstance":"252-RFO-227.mktoweb.com","marketoConfigured":true,"formConfigured":true,":type":"snowflake-site/components/form/marketo-v2"},"text":{"id":"text-be5578627d","additionalClasses":"newsletter-disclaimer","text":"\u003Cp\u003EBy submitting this form, I understand Snowflake will process my personal information in accordance with their Privacy Notice.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"}},":itemsOrder":["marketo_v2","text"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":true,"isActiveTOC":false}},":itemsOrder":["flexible_column_cont"],"appliedCssClassNames":"snowflake-container"},"experiencefragment-pre-footer":{"id":"experiencefragment-fc7a0c91df","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/get-started-pre-footer/get-started-pre-footer/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment","xfModelPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/get-started-pre-footer/get-started-pre-footer.xfmodel.json"},"markup_editor":{"id":"markup-editor-7fa4311efd","title":"Page CSS","cssContent":"@media screen and (min-width:768px){.snowflake-blog-author-chip-wrapper{justify-content:flex-start}.snowflake-blog-related-content-on-blog-page{max-width:1408px;margin-left:auto;margin-right:auto}.snowflake-text{font-family:Lato,sans-serif;font-weight:400;font-size:16px;line-height:24px}}.section--blog-newsletter{max-width:none;width:100%;padding-left:0;padding-right:0;margin-left:0;margin-right:0;margin-bottom:0}.section--blog-newsletter .mktoField{background-color:transparent !important}.section--blog-newsletter\u003E.container{padding-left:0;padding-right:0}@media screen and (min-width:768px){.section--blog-newsletter\u003E.container{padding-left:0;padding-right:0}}.newsletter-disclaimer p{font-size:14px !important}.section--blog-newsletter .snowflake-marketo-form-container{margin-bottom:24px;background-color:#f6f9fa;gap:48px;box-shadow:none}.section--blog-newsletter .snowflake-title p.snowflake-title-line:first-child{font-family:Texta;font-size:24px;line-height:26px;font-weight:700;margin-bottom:4px}.section--blog-newsletter .snowflake-title p.snowflake-title-line{text-transform:none;font-family:\"Lato\",sans-serif;font-size:16px;line-height:24px;font-weight:normal}@media screen and (min-width:1024px){.section--blog-newsletter .snowflake-marketo-form-container{display:flex;justify-content:center}.section--blog-newsletter .snowflake-title .snowflake-title-line{text-align:left}.section--blog-newsletter .snowflake-marketo-form .mktoFormRow:has(\u003E input[type=\"hidden\"]){flex-grow:0}.section--blog-newsletter .snowflake-marketo-form{display:flex;width:50% !important}.section--blog-newsletter .snowflake-marketo-form .mktoButtonRow{flex-grow:0;width:auto !important;margin-left:0;margin-right:0}.section--blog-newsletter .snowflake-marketo-form .mktoFormRow{flex-grow:1}.section--blog-newsletter\u003E.container{padding-left:0;padding-right:0}.section--blog-newsletter .snowflake-marketo-form-title{width:50%;margin-bottom:0 !important}.section--blog-newsletter .center .snowflake-title{align-items:flex-start}}.snowflake-sub-navigation a.snowflake-sub-navigation-primary-link{width:auto !important}.snowflake-blog-hero{align-items:stretch !important}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"markup_editor-table":{"id":"markup-editor-3e2bc68ec5","title":"Table Styling CSS","cssContent":"#snowflake-blog-template-main-container table{width:100%;background-color:var(--ui-background-01);border-collapse:collapse;border:2px solid var(--ui-background-09);font-family:'Lato',sans-serif;color:var(--ui-background-09)}#snowflake-blog-template-main-container table thead{background-color:var(--ui-01)}#snowflake-blog-template-main-container table th,#snowflake-blog-template-main-container table td{border:2px solid var(--ui-background-09);padding:var(--spacing-01)}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"experiencefragment-footer":{"id":"experiencefragment-1c500be06b","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/footer/master/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment","xfModelPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/footer/master.xfmodel.json"}},":itemsOrder":["experiencefragment-banner","experiencefragment-header","experiencefragment-sub-header","responsivegrid","container_47873732","experiencefragment-pre-footer","markup_editor","markup_editor-table","experiencefragment-footer"],":type":"wcm/foundation/components/responsivegrid"}},":itemsOrder":["root"],":hierarchyType":"page",":path":"/content/snowflake-site/global/en/blog/agentic-intelligence-contract-review-snowflake","isPasswordProtected":false,"analyticsContentTags":["snowflake-site:taxonomy/blog/product-and-technology/ai-ml"],"analyticsEnabled":true,"coveoConfig":{"searchHub":"snowflake.com","pipeline":"snowflake.com","apiKey":"xx335921a6-2a0a-40f2-a167-e390b4766c3d","organizationId":"snowflakecomputingproduction8neljofn"},"analyticsDebugMode":false,"analyticsData":{"excludeFromAnalytics":false,"subCategory":"","pageType":"homepage","templateName":"blog-page","siteName":"snowflake","pageUrl":"/content/snowflake-site/global/en/blog/agentic-intelligence-contract-review-snowflake","language":"en","category":"general","pageName":"How Snowflake Reduced Contract Review Time by 70% with Agentic Intelligence ","contentTags":["snowflake-site:taxonomy/blog/product-and-technology/ai-ml"]},"locale":"en"}