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

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.