编辑文章

Most discussions around 文档 AI focus on using AI to process every document.

We took a different approach.

At Ainna, AI is used to build the parser, not to run the parser.

时间 a 新 bank statement format is introduced, AI analyses the document and generates four reusable components:

✅ Parsing 规则 – Identify transaction tables, dates, descriptions, debit, credit, and balances.

✅ Cleaning 规则 – 正确 OCR issues, merge fragmented rows, remove headers and footers, and normalize the extracted data.

✅ 验证 规则 – 校验 running balances, detect duplicates, validate transaction integrity, and check data consistency.

✅ 置信度 Scoring – 分配 a confidence score to every compiled statement based on extraction and validation accuracy.

Once generated, these rules are stored as reusable rule sets.

从 that point onward, our PHP execution engine processes future statements using the saved rules—without calling AI again.

The result is:

• ⚡ 更快的执行

• 💰 Significantly lower AI costs

• 📊 Consistent outputs

• 🚀 可扩展 processing for millions of statements

AI is only invoked when confidence drops below a user-defined 阈值.

For example:

• 置信度 ≥ 95% → 执行 using existing rules.

• 置信度 < user 阈值 → AI analyses the document, refines the rules, or creates a 新 parser version.

This creates a continuous improvement loop where AI enhances the 系统 only when required, while routine processing remains deterministic, lightweight, and cost-efficient.

AI builds the intelligence. PHP executes it at scale.

For high-volume document processing, this architecture is often more economical, predictable, and easier to maintain than sending every document through an LLM.

#ArtificialIntelligence #PHP #DocumentAI #OCR #金融科技 #自动化 #RuleEngine #DataEngineering #MachineLearning #BankStatement #Ainna