I’m building an end-to-end intake and analysis system for my clients’ financial statements, contracts and tax documents. The flow starts with a secure web form where each client can upload files; as soon as they hit “submit” every document must be encrypted at rest and neatly tagged in an AWS bucket (or equivalent service you propose inside the same account). From there the platform needs to watch what has and hasn’t arrived. Whenever a file is missing, incorrectly formatted, or a client simply forgets to update personal details, an automated email reminder should go out. I want to trigger three distinct nudges—one for missing documents, one for corrections, and one for profile updates—without me lifting a finger. Once the dataset is complete, a fully private LLM+RAG pipeline will run inside my own AWS environment: containerised, isolated, no calls to public AI APIs. I’ll provide the proprietary scoring logic and example prompts; you wire up the retrieval, fine-tuning loop and GPU orchestration so I can ask questions and get structured outputs in real time. Acceptance criteria: • Users can upload, re-upload and see status in a simple dashboard. • Every email reminder fires under the right conditions and is logged. • All data remains inside my VPC with audit-ready encryption and access controls. • The LLM answers queries using only the uploaded corpus and my custom rules, matching sample outputs I’ll share. Tell me which AWS services or frameworks you prefer for each layer and how quickly you can have a demo running.