Cut Anthropic API Costs for Invoice Extraction by Using a Parser-First Pipeline

说明与用途

通过先使用解析器并验证数据，再将异常账单升级到高级 LLM，降低账单提取成本并提高准确性。

问题

High costs and suboptimal accuracy when sending every invoice PDF to a premium multimodal model like Claude, even though most invoices are digital PDFs with selectable text.

结果

Costs dropped significantly by avoiding premium multimodal calls for routine invoices; accuracy improved for standard AP documents; only anomalous invoices trigger expensive LLM calls.

工作流

• 1. Extract text from PDF (OCR or built-in parser).
• 2. Map extracted fields to a predefined schema (invoice_id, invoice_date, vendor_name, subtotal, tax_amount, total_amount, due_date).
• 3. Validate required fields and perform deterministic checks (e.g., subtotal + tax ≈ total).
• 4. If validation passes, output JSON; if not, flag for escalation.
• 5. Escalate only flagged invoices to Claude or GPT‑4.1 for final extraction.

证据

If most of your invoices are digital PDFs with selectable text, sending every page through full visual reasoning is overkill.

来源

Azure Document Intelligence scored 94.2% for line‑item extraction; Google Document AI 93.8%; Claude Sonnet 4 91.5%.

来源