Users of government portals, banking platforms, job boards, and educational institutions routinely re-enter identical identity information across unrelated web forms, a repetitive process that is time-consuming and error-prone. Existing tools address this only partially: password managers and browser-native autofill handle basic contact fields but cannot process identity documents, while repositories such as DigiLocker provide storage without autofill or query capability. This paper presents FormOS, a full-stack platform that lets a user upload identity documents once and subsequently access, query, and deploy that data across the web. FormOS combines an OCR pipeline (Tesseract.js) for structured field extraction, a schema-grounded Retrieval-Augmented Generation (RAG) assistant for natural-language querying, a Form Analyzer using synonym and fuzzy field-matching for autofill-readiness scoring, and a Chrome Man-ifest V3 extension for one-click form completion. Its contribution is the integration of these established components around a single, securely governed personal identity store, rather than novelty in any individual algorithm. The platform is built on Next.js 14, Node.js/Express.js, and MongoDB, with a multi-layered security model comprising JWT authentication, bcrypt-hashed PINs, TOTP-based MFA, and AES-256 field-level encryption. A feasibility analysis spanning technical, economic, operational, and legal dimensions supports real-world viability. As the system is at an early implementation stage, this paper reports a qualitative, architecture- and feasibility-based evaluation with module-level functional validation rather than fabricated performance bench-marks, and identifies empirical validation as future work.

Index Terms—Optical Character Recognition, Retrieval-Augmented Generation, Form Autofill, Identity Document Man-agement, Chrome Extension, Data Privacy, Multi-Factor Authen-tication, Web Automation, Systems Integration