A private equity data room holds the CIM, audited financials, the quality of earnings report, the cap table and material contracts. Deal teams check each figure on its source page.
LandingAI transforms documents into highly accurate, verifiable, structured data so teams can reliably automate document-intensive workflows.
A private target's data room averages 7,583 files, filed in folders the seller named. Financials, contracts, scans and slide decks sit side by side.1
Revenue and EBITDA appear in the CIM, the monthly profit and loss statement, the audited statements and the quality of earnings report, and each one lays them out differently.
Working capital adjustments appear in more than 90% of private-target deals. Values from aging reports and balance sheets set the peg that the post-close true-up is measured against.4
The same Parse call reads a confidential information memorandum, an audited financial statement and an employee census. Every document type uses one set of APIs, with zero templates to build and zero models to train or fine-tune.
A new form, issuer or document type works on its first call, with nothing to configure.
If an issuer redesigns a form or a new version arrives, no pipeline changes are needed.
Upload a sample in the Playground; agentic reasoning will immediately understand it.
Classify labels each file in a data room export, so CIMs, financial statements, contracts and certificates each go to the schema built for them.
Classify API →Parse reads slide decks, scanned agreements, financial tables and charts into structured Markdown, page by page.
Parse API →Section takes the Parse output of a long credit agreement or CIM and builds a table of contents, so definitions, schedules and financial sections can be found by heading.
Section API →Extract returns the values your model and diligence tracker read, such as adjusted EBITDA, share counts and lease terms, as JSON that matches your schema.
Extract API →Ground maps each extracted value to the block, page and bounding box it came from, so an associate checks a figure on the source document before it reaches the investment committee memo.
Ground docs →Trusted, high-volume processing of the documents involved in private equity due diligence requires handling diverse document types and layouts. A capitalization table is very different from a credit agreement, which is different from a certificate of insurance. The pipeline must provide high accuracy and consistent outputs across all of them.
Our agentic approach is able to reason over each document type and return structured hierarchical Markdown and JSON outputs that downstream systems can rely on.
Each document below links to a worked example. The documents and schemas are available in full on GitHub and you are welcome to use them for your own testing.
Audited Financial StatementAudited balance sheet, results and cash flow
Quality of Earnings ReportAdjusted EBITDA and working capital
Business Profit and Loss StatementMonthly and trailing results
AR and AP Aging ReportReceivables quality and the working capital peg
Financial ModelManagement projections and returns by scenario
Articles of IncorporationLegal name, state and authorized shares
Master Services AgreementTerm, renewal and assignment on a key contract
Customer Concentration ScheduleRevenue from the largest customers
Commercial LeaseRent, term and premises for each facility
Certificate of InsurancePolicies, limits and dates in force| Field | Source document | Used for |
|---|---|---|
| Revenue, gross margin and normalized EBITDA by period | Confidential Information Memorandum | Screening model and the indication of interest |
| Buyer, target, purchase price and exclusivity | Letter of Intent | Deal terms carried into the purchase agreement |
| Total assets and total liabilities | Audited Financial Statement | Balance sheet and debt-like items |
| Total revenues, total expenses and operating cash flow | Audited Financial Statement | Tie-out against the CIM and the quality of earnings report |
| Reported and adjusted EBITDA by period | Quality of Earnings Report | Valuation basis |
| Adjustment, category and amount | Quality of Earnings Report | Which add-backs the sponsor accepts |
| Average net working capital | Quality of Earnings Report | Working capital peg in the purchase agreement |
| Total balance and each aging bucket | AR and AP Aging Report | Working capital peg and bad debt reserve |
| Holder, total shares and ownership percent | Capitalization Table | Ownership, rollover and proceeds waterfall |
| Fully diluted shares | Capitalization Table | Price per share |
| Borrower, lenders and maximum commitment | Credit Agreement | Debt to repay or refinance at close |
| Debtor, secured party, filing date and collateral | UCC Lien Search | Liens to release at closing |
| Corporation name, state and authorized shares by class | Articles of Incorporation | Entity structure, checked against the cap table |
| Provider, initial term, renewal and assignment | Master Services Agreement | Change-of-control consents |
| Customer, revenue and share of total | Customer Concentration Schedule | Concentration risk |
| Tenant, premises, term and base rent | Commercial Lease | Occupancy cost and lease liabilities |
| Coverage, policy number, expiration and limits | Certificate of Insurance | Insurance program and tail coverage |
| Status, hire date, annual pay and headcount | Employee Census | Payroll cost and retention planning |
| Position, term end and base salary | Executive Employment Agreement | Management compensation and severance exposure |
| Seller, buyer, purchase price and governing law | Stock Purchase Agreement | Closing terms reconciled against the letter of intent |
Deal teams measure diligence by how many deals reach a close, what each review costs and how closely the closing numbers match the model.
Completing diligence ranks beside regulatory hurdles as a top obstacle to closing a deal. Values extracted from each data room export leave more of the exclusivity window for the questions that decide it.
Associates copy figures from CIMs, statements and schedules into the model by hand. Extracted, grounded values let them start from the numbers and open a file only where a value needs a closer look.
After closing, a true-up compares delivered working capital with the agreed peg. Every value behind the peg carries its page and location, so the deal team checks it against the source before signing.
The best accuracy on the difficult documents other tools get wrong, such as messy scans, complicated tables, checkboxes and handwriting.
Every answer cites its sources. Values come back with an audit trail (page, bounding box, character range) so a human can review them.
Applies multi-step reasoning, and plans before it acts. Handles documents it has never seen before and adapts to variations.
Composable APIs that drop into any tech stack and can be called from anywhere. Easy to build on and embed in your pipelines and products.
Cost-competitive rates with complete price transparency. Spend controls before you run, and every job reports its exact cost.
Built for production and scale from day one. Fully observable and monitored, running securely in our cloud or yours.
Available on Team and Enterprise plans. VPC and on-prem deployment require an Enterprise plan.
