{"slug": "hybrid-local-and-cloud-llm-stack-for-regulated-financial-document-processing", "title": "Hybrid local and cloud LLM stack for regulated financial document processing?", "summary": "A consultant is designing a hybrid AI pipeline for a regulated financial client that processes sensitive documents like bank statements and tax returns, requiring local LLMs for OCR and PII tokenization before any cloud API calls for reasoning. The architecture uses a local model for first-pass extraction, a PII scrubber to tokenize identifiers, and a cloud LLM under enterprise terms for the reasoning layer, with de-tokenization and template population occurring locally. The consultant is seeking production-tested stack recommendations for financial document processing under GLBA and NPI compliance constraints.", "body_md": "I'm scoping a hybrid AI pipeline for a consulting client in a regulated industry (GLBA-covered, NPI involved). Trying to validate the architecture before bringing on an engineer to build it.\n\nThe workflow: ingest financial PDFs (bank, brokerage, retirement statements, tax returns), classify by asset type, extract data, apply domain-specific business logic, populate Excel templates and fillable PDF forms. Compliance constraint: no NPI can hit a cloud API without ZDR-style controls.\n\nCurrent architecture sketch: - Local LLM (Ollama or LM Studio) on dedicated hardware for OCR and first-pass extraction - Local PII scrubber/tokenizer (Presidio or Skyflow) replaces identifiers with tokens before any cloud call - Cloud LLM under enterprise terms (Claude API with ZDR, or Bedrock equivalent) for the reasoning layer - Local de-tokenization and template population\n\nQuestions for anyone who's actually shipped this pattern: 1. What stack did you land on, and what would you do differently? 2. Local model for financial document OCR + structured extraction - is Qwen2.5-VL still the move, or has something better landed? 3. Tokenization layer: roll your own with Presidio, or pay for Skyflow / Private AI? 4. Orchestration: LangGraph, n8n, or custom Python? 5. Is an M4 Max Mac realistic for a single-user workflow at 50-200 PDFs per case, or do I need to plan for proper inference hardware?\n\nAlready evaluated turnkey hybrid platforms (LLM.co, PremAI, Petronella) - leaning toward an assembled stack for cost and control reasons, but open to being talked out of it if someone's had a great experience with one of these.\n\nNot looking for \"just go fully local\" (reasoning quality is important for this build) or \"just use the API\" (data constraints are real). Production-tested stacks only.\n\nComments URL: [https://news.ycombinator.com/item?id=48327218](https://news.ycombinator.com/item?id=48327218)\n\nPoints: 2\n\n# Comments: 1", "url": "https://wpnews.pro/news/hybrid-local-and-cloud-llm-stack-for-regulated-financial-document-processing", "canonical_source": "https://news.ycombinator.com/item?id=48327218", "published_at": "2026-05-29 18:22:54+00:00", "updated_at": "2026-05-29 18:47:31.140329+00:00", "lang": "en", "topics": ["large-language-models", "artificial-intelligence", "ai-infrastructure", "ai-safety", "natural-language-processing"], "entities": ["Ollama", "LM Studio", "Presidio", "Skyflow", "Claude", "Bedrock", "Qwen2.5-VL", "LangGraph"], "alternates": {"html": "https://wpnews.pro/news/hybrid-local-and-cloud-llm-stack-for-regulated-financial-document-processing", "markdown": "https://wpnews.pro/news/hybrid-local-and-cloud-llm-stack-for-regulated-financial-document-processing.md", "text": "https://wpnews.pro/news/hybrid-local-and-cloud-llm-stack-for-regulated-financial-document-processing.txt", "jsonld": "https://wpnews.pro/news/hybrid-local-and-cloud-llm-stack-for-regulated-financial-document-processing.jsonld"}}