SmartLLM: A Secure, Private, and Cost-Effective On- Premise Large Language Model System for EnterpriseAI Deployment

SmartLLM: A Secure, Private, and Cost-Effective On- Premise Large Language Model System for EnterpriseAI Deployment | 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 Article SmartLLM: A Secure, Private, and Cost-Effective On- Premise Large Language Model System for EnterpriseAI Deployment Hrushikesh Kanhaiya Pardeshi, Ahmed Ismail Ebada This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8777215/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Large language models (LLMs) are increasingly adopted in enterprise environments to support document intelligence and decision assistance. However, cloud-based deployments introduce governance and accountability challenges, including data exposure, limited auditability, and reduced control over policy enforcement. This paper presents SmartLLM, a fully on-premise enterprise LLM system that integrates retrieval-augmented generation with governance constraints, verification functions, and safety-preserving refusal and escalation behavior. We formalize SmartLLM as a governance-aware system and introduce a composite evaluation metric, the Governance-Aware Reliability Score (GARS), which jointly captures hallucination rate, policy violation rate, and controlled refusal behavior. SmartLLM is evaluated on consumer-grade hardware using 45 enterprise documents and 200 document-centric queries across multiple domains. Across tasks, SmartLLM achieves up to 87% semantic answer accuracy relative to a GPT-4 baseline under identical retrieved contexts, with response latencies of 2–5 seconds and memory footprint below 4 GB. A controlled ablation study demonstrates that reliability improvements emerge from system architecture (governance + verification + routing) rather than model scale alone. These results show that governance-aware enterprise LLM capabilities are achievable through fully local deployment, providing a practical alternative to cloud-centric architectures for regulated, document-grounded use cases. Physical sciences/Engineering Business and commerce/Information systems and information technology Physical sciences/Mathematics and computing Large Language Models On-Premise AI Governance Retrieval-Augmented Generation Hallucination Reliability Enterprise Compliance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 24 Feb, 2026 Reviews received at journal 20 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviews received at journal 18 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers invited by journal 18 Feb, 2026 Editor assigned by journal 18 Feb, 2026 Editor invited by journal 13 Feb, 2026 Submission checks completed at journal 10 Feb, 2026 First submitted to journal 10 Feb, 2026 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. 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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-8777215","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":594479012,"identity":"7533f5c5-9526-4caa-8edb-0bcd66c4b18e","order_by":0,"name":"Hrushikesh Kanhaiya Pardeshi","email":"","orcid":"","institution":"HOPn","correspondingAuthor":false,"prefix":"","firstName":"Hrushikesh","middleName":"Kanhaiya","lastName":"Pardeshi","suffix":""},{"id":594479013,"identity":"d9554722-364f-452c-93ff-158663d5446f","order_by":1,"name":"Ahmed Ismail Ebada","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACNiiSYzgA5PEQo4UfqsWYeC2SDRAtiQ1EazE4wHzswY8yu/S+GwmMD962McjJOxDUwpZu2HMuOXfmjQRmw7ltDMaGBwhq4TGT4G1jzt1wI4FNmreNIXFjAwEt9gf4v0n+batPN7iRwP4bqKWeoBagLSDDDycAtbAxA7UkyBPQwWBwmM3cWObcccOZZx42S845J2G4gaCW483PHr4pq5bnO5588MObMht5eUIOY2CGsxhBaiWATiWkBQMQtmUUjIJRMApGGgAAkpc/66reZrAAAAAASUVORK5CYII=","orcid":"","institution":"Faculty of Computers and Artificial Intelligence, Damietta University","correspondingAuthor":true,"prefix":"","firstName":"Ahmed","middleName":"Ismail","lastName":"Ebada","suffix":""}],"badges":[],"createdAt":"2026-02-03 14:39:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8777215/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8777215/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103205362,"identity":"4fbc08c0-19cd-43f1-97e7-2cff5c618a62","added_by":"auto","created_at":"2026-02-23 07:12:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1021111,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchpaperSmartLLMupdated1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8777215/v1_covered_956fad73-607b-4ebd-9347-57461834d9e4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SmartLLM: A Secure, Private, and Cost-Effective On- Premise Large Language Model System for EnterpriseAI Deployment","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Large Language Models, On-Premise AI, Governance, Retrieval-Augmented Generation, Hallucination, Reliability, Enterprise Compliance","lastPublishedDoi":"10.21203/rs.3.rs-8777215/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8777215/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLarge language models (LLMs) are increasingly adopted in enterprise environments to support document intelligence and decision assistance. However, cloud-based deployments introduce governance and accountability challenges, including data exposure, limited auditability, and reduced control over policy enforcement. This paper presents SmartLLM, a fully on-premise enterprise LLM system that integrates retrieval-augmented generation with governance constraints, verification functions, and safety-preserving refusal and escalation behavior. We formalize SmartLLM as a governance-aware system and introduce a composite evaluation metric, the Governance-Aware Reliability Score (GARS), which jointly captures hallucination rate, policy violation rate, and controlled refusal behavior.\u003c/p\u003e \u003cp\u003eSmartLLM is evaluated on consumer-grade hardware using 45 enterprise documents and 200 document-centric queries across multiple domains. 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