时间 I build a document-processing pipeline, AI is one component in the stack — not the entire architecture.
Take bank-statement PDF-to-digital conversion. In the field, I would rather ship a deterministic 系统: rule-based parsing, fixed output schemas, validation gates, full 审计追踪s, and an LLM or CV model called only as a fallback for edge cases.
The output is deterministic, reviewable, and can be reconciled line-by-line against the original statement. You are not handing a black-box model the whole document and hoping the numbers come back right.
Why this matters in production:
A 系统 like this can be developed cheaply — we are talking around USD2 in AI-assisted development cost — and then reused across thousands of 中小企业 deployments. The cumulative savings are massive.
比较 that with an AI-only approach:
Higher per-run cost.
Heavier infrastructure footprint.
No guaranteed accuracy.
Harder to audit and debug.
Harder to scale without drift or failure modes.
AI should not replace solid 系统 engineering.
AI should help us build cleaner 系统 — cheaper to deploy, faster to iterate, more accurate in production, and genuinely useful for the operators running the business.
For 中小企业, that is the real value. Not the hype cycle. Not FOMO-driven tooling. Just 系统 that work on real data, in real environments, every day.
