95% faster month-end spreading for SME Capital
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Intelligent Document Processing

Every document in the pack, extracted and cross-checked.

Bank statements, accounts, tax returns, ID, leases and valuation reports. Agents classify each document, extract the fields your policy needs, and reconcile them across the file, with every value cited back to its source page.

Citations on every value · Confidence scoring · Feedback loops

Not another IDP tool. Not another workflow layer.

Intelligent document processing vendors stop at extraction. Decisioning platforms ask underwriters to work in a new layer. Sea.dev embeds directly into the core with AI agents for document extraction, cross-checks, spreading, bank statement assessment, valuation and comparable review, and underwriter-ready credit reports.

See document integrity signals
IDP vendors

Stop at extraction

  • Fields out, often template-bound
  • No cross-checks across the pack
  • Values without a link to the source page
  • Spreading and assessment still done by hand
Decisioning platforms

Require a new workflow layer

  • Underwriters work in another system
  • Integration project before any value
  • Policy logic locked in the vendor layer
  • Documents still prepared manually
Sea.dev

Embedded in your existing core

  • Agents for document extraction, cross-checks and spreading
  • Bank statement assessment, valuation and comparable review
  • Underwriter-ready credit reports, every finding cited
  • Runs inside your LOS or banking core

Every document in a lending file

From the routine to the complex. Schemas are versioned and shaped to your policy, so the fields you extract are the fields your underwriters use.

Bank statements

PDF, CSV, scans and screenshots. Transactions, balances, returned items, conduct and cash-flow patterns, categorised for assessment.

Financial statements & management accounts

Audited accounts, management packs and Excel models. P&L, balance sheet and cash-flow line items across periods and entities.

Tax returns

Personal and corporate returns, forms and computations. Income, deductions and schedules, reconciled to the accounts.

Valuation reports & comparables

Valuation basis, condition, rental figures, tenancy schedules and comparable evidence, including multi-unit and multi-property schedules.

Leases, titles & legal

Leases, tenancy agreements, title documents and contracts. Parties, terms, dates, rents and obligations.

ID, KYC & supporting documents

Identity documents, proof of address, application forms, invoices and correspondence, matched to the borrower on file.

How it works

Four steps from broker pack to reviewed, cited data. The agents do the first pass, your team keeps the decision.

01

Classify

Every file in the pack is identified and split, whatever the format or scan quality. No templates to maintain.

02

Extract

The fields your policy needs are pulled into your schema, with a confidence score on each value so reviewers know where to look.

03

Cross-check

Values are reconciled across the pack: rent on the valuation against the lease, deposits against statements, names and addresses across every document.

04

Verify & cite

Integrity signals catch edited or synthetic documents. Every extracted value links back to the page it came from, ready for one-click review.

Case 4821 · Broker pack
1 to review
Extracted fields
0 of 38

Select a field to see where it came from.

Source
PAGE 3Lease_Unit_1.pdf
Rent: $7,000 per calendar month, payable in advance
Cross-check. Reconciles with valuation gross rent of $84,000 p.a.

Accuracy you can audit, and improve

AI cannot be a black box in lending. Two mechanisms keep extraction trustworthy on day one and better every month after.

Citations on every value

Each extracted figure links to the document, page and region it came from. Reviewers verify in one click instead of hunting through the pack, and the approval is logged with who checked what and when.

  • Page and region reference on every field
  • Confidence score to prioritise review
  • Full audit trail of approvals and edits

Feedback loops for continuous learning

Every correction a reviewer makes feeds back into extraction and schemas. Before go-live, accuracy is grounded against your historical files and cleared to an agreed threshold. After it, the system keeps learning your documents, your policy and your edge cases.

  • Corrections captured in review, not lost in email
  • Versioned schemas so changes stay controlled
  • Accuracy measured against your own history
Read: building fast feedback loops