In production document pipelines, the easy path is to send every PDF through an LLM. We avoid that.
At Ainna, we use AI to author the parser, not to operate it. The model runs once per document family; the runtime runs forever.
时间 we onboard a 新 bank statement format, the AI analyses the layout and emits four reusable, versioned artifacts:
✅ Parsing 规则 – 定位 transaction tables, date fields, descriptions, debit/credit columns, and running balances.
✅ Cleaning 规则 – Fix OCR fragmentation, merge split rows, strip headers and footers, and normalize extracted values.
✅ 验证 规则 – 对账 running balances, detect duplicates, validate transaction integrity, and enforce consistency checks.
✅ 置信度 Scoring – 分数 every compiled statement so downstream 系统 know whether to accept, review, or escalate it.
These artifacts are stored as reusable rule sets and loaded by the execution engine.
从 then on, our PHP execution engine applies those rules to every 新 statement deterministically. No repeated AI calls. No token burn on 常规工作。
The operational payoff is straightforward:
⚡ Sub-second execution on the hot path
💰 AI spend concentrated on real exceptions, not every page
📊 确定性, auditable outputs
🚀 吞吐量 that scales horizontally with ordinary PHP workers
We only bring the model back in when confidence drops below a user-defined 阈值.
For example:
置信度 ≥ 95% → Execute using the existing rule set.
置信度 < user 阈值 → AI analyses the anomaly, refines the rules, or forks a 新 parser version.
This gives us a closed-loop 系统: routine processing stays lightweight and deterministic, while the model is reserved for genuine format drift or low-confidence cases.
AI authors the rules. PHP executes them at scale.
For high-volume document processing, that split is usually cheaper, more predictable, and far easier to operate than routing every document through an LLM.
#ArtificialIntelligence #PHP #DocumentAI #OCR #金融科技 #自动化 #RuleEngine #DataEngineering #MachineLearning #BankStatement #Ainna
