Most discussions around Document 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.
When a baharu bank statement format is introduced, AI analyses the document and generates four reusable components:
✅ Sedang parse Rules – Identify transaction tables, dates, descriptions, debit, credit, and balances.
✅ Cleaning Rules – Correct OCR issues, merge fragmented rows, remove headers and footers, and normalize the extracted data.
✅ Peraturan Pengesahan – Verify running balances, detect duplicates, validate transaction integrity, and check data consistency.
✅ Kunciakinan Pemarkahan – Assign a keyakinan score to every compiled statement based on extraction and validation accuracy.
Once generated, these rules are stored as reusable rule sets.
From that point onward, our PHP execution engine processes future statements using the saved rules-without calling AI again.
The result is:
⚡ Lebih Pantas execution
💰 Significantly lower AI costs
📊 Consistent outputs
🚀 Boleh Skala processing for millions of statements
AI is only invoked when keyakinan drops below a user-defined threshold.
For example:
Kunciakinan ≥ 95% → Execute using existing rules.
Kunciakinan < user threshold → AI analyses the document, refines the rules, or creates a baharu parser version.
This creates a continuous improvement loop where AI enhances the system 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 #FinTech #Automasi #RuleEnjin #DataEnjinering #MachinePelajariing #BankKeadaanment #Ainna
