Conversational A.I for Smart Exploration (C.A.S.E)

Conversational A.I for Smart Exploration (C.A.S.E) | 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 Conversational A.I for Smart Exploration (C.A.S.E) Kareem Abouelseoud, Fouad Soliman, Mohamed Harb, Haidy Fekry, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7802332/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 In today's data-driven world, the ability to extract meaningful insights remains a significant bottleneck, often confined to technical specialists. This paper introduces Conversational A.I. for Smart Exploration (C.A.S.E.) a revolutionary platform designed to dismantle this barrier. C.A.S.E. transforms the complex, code-heavy process of data analysis into a simple, interactive dialogue, empowering any user—from business executives to domain experts—to converse directly with their datasets. By simply asking questions in natural language, users can automatically uncover insights, generate dynamic visualizations, and even build predictive models, turning raw data into a decisive, strategic advantage without writing a single line of code.At its core, C.A.S.E. operates on a sophisticated multi-agent framework where specialized agents collaborate to orchestrate the entire workflow. The Insight Generation Module, inspired by the QUIS framework, automates the discovery of statistically significant patterns through an iterative question-generation and subspace search pipeline. For complex tasks like preprocessing and visualization, C.A.S.E. employs a hybrid "caller-or-coder" design, balancing the reliability of predefined tools with the flexibility of Large Language Model (LLM)-generated code. This architectural prowess culminates in the AutoML module, which introduces a novel supervisor-agent architecture, moving beyond rigid, static pipelines to enable adaptive, iterative optimization of model development. Our results demonstrate consistent performance gains, with C.A.S.E. achieving the highest or matching F1-score on all classification benchmarks and the lowest or matching RMSE on every regression dataset tested, including a 0.6 F1-score improvement on the Banana Quality dataset and a 5 RMSE reduction on the NYC Airbnb dataset. By integrating these components into a seamless workflow, C.A.S.E. delivers a holistic solution that truly democratizes data science. Data Analysis Visualizations Insight Generation Large Language Model AutoML Agents Data Science 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-7802332","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":549488292,"identity":"545e769a-3b42-4c55-8474-3c22880d973e","order_by":0,"name":"Kareem Abouelseoud","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Kareem","middleName":"","lastName":"Abouelseoud","suffix":""},{"id":549488293,"identity":"c107f424-ee9d-49a8-8a17-89597fd1b287","order_by":1,"name":"Fouad Soliman","email":"","orcid":"","institution":"Ain Shams 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This paper introduces Conversational A.I. for Smart Exploration (C.A.S.E.) a revolutionary platform designed to dismantle this barrier. C.A.S.E. transforms the complex, code-heavy process of data analysis into a simple, interactive dialogue, empowering any user\u0026mdash;from business executives to domain experts\u0026mdash;to converse directly with their datasets. By simply asking questions in natural language, users can automatically uncover insights, generate dynamic visualizations, and even build predictive models, turning raw data into a decisive, strategic advantage without writing a single line of code.At its core, C.A.S.E. operates on a sophisticated multi-agent framework where specialized agents collaborate to orchestrate the entire workflow. The Insight Generation Module, inspired by the QUIS framework, automates the discovery of statistically significant patterns through an iterative question-generation and subspace search pipeline. For complex tasks like preprocessing and visualization, C.A.S.E. employs a hybrid \"caller-or-coder\" design, balancing the reliability of predefined tools with the flexibility of Large Language Model (LLM)-generated code. This architectural prowess culminates in the AutoML module, which introduces a novel supervisor-agent architecture, moving beyond rigid, static pipelines to enable adaptive, iterative optimization of model development. Our results demonstrate consistent performance gains, with C.A.S.E. achieving the highest or matching F1-score on all classification benchmarks and the lowest or matching RMSE on every regression dataset tested, including a 0.6 F1-score improvement on the Banana Quality dataset and a 5 RMSE reduction on the NYC Airbnb dataset. 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