No—our layout-aware extraction preserves reading order across multi-column pages, headers/footers, and dense application packets. That means merchant applications, KYC forms, and underwriting questionnaires come through in the right sequence with far less cleanup. Your ops team spends less time fixing data and more time moving merchants to approval.

We extract tables into clean, structured data—including nested rows—so pricing, fees, settlement details, and bank grids don’t get flattened into unusable text. This reduces reconciliation work and prevents costly onboarding errors caused by misread rows or shifted columns. You can trust the output for downstream calculations and approvals.

Yes—output is delivered as structured JSON, making it easy to map fields into your merchant profile, CRM, or underwriting system. You can standardize across document types while keeping the flexibility to add new fields over time. This speeds integration and shortens time-to-value.

Every extracted field can include citations with page-level metadata like coordinates and source references. That gives reviewers a quick “show me where this came from” path for QA, compliance, and audit requests. Exceptions become faster to resolve because the evidence is attached to the data.

Our auto-routed agentic parsing applies heavier vision reasoning only where needed, and lighter parsing when pages are straightforward. That keeps throughput high and costs predictable while still handling real-world onboarding documents like scanned forms, stamps, and varying templates. You get reliability without paying a premium on every page.

Most teams start by sending a sample set of onboarding documents and receiving structured JSON that matches their target schema. From there, it’s straightforward to plug into your current review queues, rules, and downstream systems with clear citations for validation. We’ll help you validate accuracy early so you can go live confidently.