Scale invoice extraction to a million documents a month without burning the budget

At a million documents a month, the system has to do two things at once: keep accuracy high enough that finance trusts the books, and keep the cost low enough that the contract is profitable. The single biggest lever is routing. Most invoices come from a repeat set of vendors with predictable layouts. A small, cheap multimodal model handles the bulk; only documents that fail validation rules or come back with low confidence are escalated to the expensive frontier model. The second lever is structural cost reduction on every call: render pages at the resolution where line items are legible, not the maximum supported; use batch APIs where end-to-end latency does not matter; cache extractions by content hash so reruns are free; and lean on the fact that the same vendor templates repeat month after month. Accuracy comes from layered validation, not from picking the smartest model. A strict JSON schema constrains the output shape. Deterministic rules like totals reconciling and dates parsing catch most arithmetic and formatting errors before anything else looks at the document.

The system has four pillars: cost structure, accuracy assurance, human review, and drift control. **Cost structure** starts with model routing. A cheap default model, often a smaller open VLM or a provider's mini tier, handles the high-volume clean majority. Documents that fail deterministic validation, score below a confidence threshold, or come from never-before-seen vendor templates escalate to a frontier model like Claude Sonnet 4.6 or Gemini 3.1 Pro. The frontier model sees the tail, not the traffic. On top of routing, three orthogonal levers stack: tune rasterization resolution to the legibility floor rather than the model maximum; use batch APIs for non-urgent volume since they price at 30 to 50 percent of synchronous; and cache extractions by page-image content hash, with template-aware prompts that exploit the long tail of repeat vendors who send near-identical layouts every cycle. **Accuracy assurance** is layered. Structured output against a strict JSON schema constrains the response shape. Deterministic business rules catch errors that no model self-confidence score can match: line items sum to subtotal, subtotal plus tax equals total, dates parse, currency is consistent, tax IDs match country patterns. Rule failures are the primary escalation signal. **Human review** is a budgeted queue sized from finance's error tolerance. Confidence thresholds are calibrated so the daily queue is humanly processable; dollar value adds an override so a misread ten-thousand-dollar invoice always gets eyes regardless of confidence. Reviewer corrections are captured as labeled data for the next fine-tune round.