I've been wiring AI assistants into things that have to be correct , and financial data is where the wheels come off fastest. "Roughly right" is fine for a chatbot; it is not fine for a number someone files a tax return on. Here are three problems I actually hit, in order, and what fixed each.
We all know the failure mode: ask an LLM for a tax rate and it'll hand you a confident number with zero provenance. Fine for a demo, useless for anything you'd ship. The interesting question is how you ground the model in a source of truth — and MCP has quietly made that a one-liner.
Every major assistant now ships some version of scheduled prompts. You write "every Monday at 9am, analyze last week's signup funnel and tell me what changed," pick a cadence, and it runs. For a news briefing, this is great.
Short answer: make every indexing and retrieval decision attributable to a tenant before the Node. js service sends text to an embedding model or an LLM. Count with the model's tokenizer, reject unbudgeted work, and record actual usage after each call.
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