Sea.dev x RNHB: intelligent document processing
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Financial spreading without the spreadsheet grind

Automate extraction, validation, and approvals while keeping analysts in control across private equity, venture debt, and commercial lending deals.

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Spread, cite, approve

Every figure links back to its source page. Ask the assistant about the numbers, then approve the spread.

Income Statement Spreading

Line ItemFY 2025FY 2026 (E)
Total Revenue$12,450,000$14,200,000
Cost of Goods Sold($6,225,000)($6,816,000)
Gross Profit
$6,225,000$7,384,000
Operating Expenses($4,120,000)($4,500,000)
EBITDA
$2,105,000$2,884,000
Net Interest Expense($150,000)($180,000)
Pre-tax Income$1,955,000$2,704,000

AI Assistant

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Where does the $7,384,000 Gross Profit come from?
The Gross Profit of $7,384,000 is calculated from Total Revenue ($14,200,000) minus Cost of Goods Sold ($6,816,000). This value is extracted from Page 12 .
Fetching submission data
Analyzing citations

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Explore how Sea automates statement extraction, variance reviews, and approvals end-to-end.

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Frequently asked questions

Automate financial spreading with human oversight.

Financial Spreading automatically extracts values from PDFs, Excel models, and data rooms, then routes proposed spreads through analyst checklists and approval steps. Your team controls every adjustment before data syncs to downstream systems.

The workflow is used by private equity, venture debt, commercial lending, and corporate development teams. Templates flex to multi-entity consolidations, subscription metrics, and bespoke KPIs so each mandate stays on brand.

Teams regularly cut first-pass spreading from hours to minutes. AI produces a draft in moments, while human-in-the-loop validation ensures accuracy so high-cost junior analysts stay focused on insights and deal execution.

No. Sea.dev embeds into the core you already run. Documents arrive through your existing intake, agents do the first pass, and the outputs (extracted data, cross-check results and credit reports) land back in your system with a full audit trail. There is no new workflow layer for underwriters to learn.

Yes. Valuation reports and comparables are among the more complex documents in a pack. The agents extract the valuation basis, condition, rental figures and comparable evidence, including multi-unit and multi-property schedules, then cross-check them against the lease, tenancy schedule and application before the file reaches an underwriter.

We've built a better way to capture and structure data. You can then download this data as an excel or word doc and/or send it straight to your internal databases or CRM as a JSON via our API. This increases the quality of data sent to your CRM and reduces the time it takes from first contact to closing.


Popular use-cases this has enabled:

  • Automate data collection from unstructured data received from leads via PDFs or Excel to create a first version of a completed application that you can refine with your lead
  • Automate collection of follow up data from your leads by sending automated follow up emails with a link to complete the data submission using our Onboarding Assistant
  • Automate the generation of summary reports from the captured data
  • Our AI data extraction platform supports comprehensive financial document processing across all major formats used in lending and financial services:


  • Traditional documents: PDFs, Word documents, Excel spreadsheets, and PowerPoint presentations
  • Financial statements: Bank statements, profit & loss statements, balance sheets, and cash flow reports
  • Loan documentation: Tax returns, pay stubs, credit reports, and loan applications
  • Digital sources: Website URLs, online banking portals, and API data feeds
  • Audio processing: Customer calls, voice memos, and recorded interviews for KYC compliance
  • Image formats: Scanned documents, photos of financial documents, and mobile-captured images

  • Our OCR and natural language processing capabilities ensure accurate data extraction regardless of document quality, handwriting, or format variations commonly found in financial documentation.