Natural Language-Driven Data Visualization Using Kestrel AI: A Novel Algorithm for Intelligent Analytics

Natural Language-Driven Data Visualization Using Kestrel AI: A Novel Algorithm for Intelligent Analytics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Natural Language-Driven Data Visualization Using Kestrel AI: A Novel Algorithm for Intelligent Analytics Veerababu Reddy, N. Veeranjaneyulu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6375485/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Generative AI in financial analytics is increasingly vital for interpreting vast, complex datasets to guide strategic decisions in dynamic, real-time markets, yet its technical complexity often limits non-expert engagement. Traditional methods, rooted in structured query language (SQL), depend heavily on specialized expertise, restricting access for business analysts and decision-makers lacking programming skills. These conventional systems achieve 75 percent to 80 percent accuracy for simple queries but falter significantly with complex, nested conditions or multi-operator logic, diminishing their utility in today’s fast-paced financial landscape. We present Kestrel AI, a pioneering algorithm harnessing advanced Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG) to convert natural language queries into precise SQL commands and insightful visualizations. With a modular, scalable architecture and HighChart integration, Kestrel AI delivers an outstanding 92 percent average accuracy, substantially outperforming traditional approaches across diverse query types, from basic to intricate. Experimental validation shows query execution times averaging 0.3 seconds, highlighting its speed and efficiency, while user studies reveal 85 percent of participants, spanning seasoned financial analysts to complete novices, commend its intuitive interface and rapid insight delivery. The system adeptly processes structured and unstructured data, allowing users to blend diverse datasets for thorough analysis, and adapts seamlessly to real-time market shifts. By simplifying complex data interactions and enhancing access, Kestrel AI fosters inclusive, data-driven decision-making in financial contexts. This work sets a new standard for generative AI-driven analytics, offering a transformative tool for investment research, market analysis, and organizational collaboration that bridges technical barriers and user-friendly design. Generative AI Financial Analytics NLP RAG SQL Queries User Experience Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6375485","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":438409676,"identity":"c2aa306d-9125-437c-97bc-aecd049eb015","order_by":0,"name":"Veerababu Reddy","email":"data:image/png;base64,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","orcid":"","institution":"Vignan's Foundation for Science, Technology \u0026 Research","correspondingAuthor":true,"prefix":"","firstName":"Veerababu","middleName":"","lastName":"Reddy","suffix":""},{"id":438409677,"identity":"387d6a55-bed9-4a27-91cf-ab099fa56ef0","order_by":1,"name":"N. Veeranjaneyulu","email":"","orcid":"","institution":"Vignan's Foundation for Science, Technology \u0026 Research","correspondingAuthor":false,"prefix":"","firstName":"N.","middleName":"","lastName":"Veeranjaneyulu","suffix":""}],"badges":[],"createdAt":"2025-04-04 10:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6375485/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6375485/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80178264,"identity":"0bf290ab-ebad-4200-96fb-f1128434317f","added_by":"auto","created_at":"2025-04-08 21:46:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":710461,"visible":true,"origin":"","legend":"","description":"","filename":"NaturalLanguageDrivenDataVisualizationUsingKestrelAIANovelAlgorithmforIntelligentAnalytics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6375485/v1_covered_e101482a-c4f3-4739-8311-f4940c5c0a85.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Natural Language-Driven Data Visualization Using Kestrel AI: A Novel Algorithm for Intelligent Analytics","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Generative AI, Financial Analytics, NLP, RAG, SQL Queries, User Experience","lastPublishedDoi":"10.21203/rs.3.rs-6375485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6375485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenerative AI in financial analytics is increasingly vital for interpreting vast, complex datasets to guide strategic decisions in dynamic, real-time markets, yet its technical complexity often limits non-expert engagement. Traditional methods, rooted in structured query language (SQL), depend heavily on specialized expertise, restricting access for business analysts and decision-makers lacking programming skills. These conventional systems achieve 75 percent to 80 percent accuracy for simple queries but falter significantly with complex, nested conditions or multi-operator logic, diminishing their utility in today\u0026rsquo;s fast-paced financial landscape. We present Kestrel AI, a pioneering algorithm harnessing advanced Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG) to convert natural language queries into precise SQL commands and insightful visualizations. With a modular, scalable architecture and HighChart integration, Kestrel AI delivers an outstanding 92 percent average accuracy, substantially outperforming traditional approaches across diverse query types, from basic to intricate. Experimental validation shows query execution times averaging 0.3 seconds, highlighting its speed and efficiency, while user studies reveal 85 percent of participants, spanning seasoned financial analysts to complete novices, commend its intuitive interface and rapid insight delivery. The system adeptly processes structured and unstructured data, allowing users to blend diverse datasets for thorough analysis, and adapts seamlessly to real-time market shifts. By simplifying complex data interactions and enhancing access, Kestrel AI fosters inclusive, data-driven decision-making in financial contexts. This work sets a new standard for generative AI-driven analytics, offering a transformative tool for investment research, market analysis, and organizational collaboration that bridges technical barriers and user-friendly design.\u003c/p\u003e","manuscriptTitle":"Natural Language-Driven Data Visualization Using Kestrel AI: A Novel Algorithm for Intelligent Analytics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-08 03:45:04","doi":"10.21203/rs.3.rs-6375485/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b3988f64-c7aa-4c86-bd18-1685f20b5173","owner":[],"postedDate":"April 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-09T11:51:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-08 03:45:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6375485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6375485","identity":"rs-6375485","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}