1.24M √ tie-out p. 1 23.1% GL (108,000) sch. 4.1 $56M adj. +2.3% net debt 9,900,800 $4.7M 1,024 9.4% √ net debt √ 1,024 =B1-B2 6.2× 1.9× TB 2,640 EBITDA 2.9M 8.7% 12,480,900 $3,429.5k (312,000) — 1.24M √ tie-out p. 1 23.1% GL (108,000) sch. 4.1 $56M adj. +2.3% net debt 9,900,800 $4.7M 1,024 9.4% √ net debt +2.3% net debt 9,900,800 $4.7M 1,024 9.4% √ net debt √ 1,024 =B1-B2 6.2× 1.9× TB 2,640 EBITDA 2.9M 8.7% 12,480,900 $3,429.5k (312,000) — 1.24M √ tie-out p. 1 23.1% GL (108,000) sch. 4.1 $56M adj. +2.3% net debt 9,900,800 $4.7M 1,024 9.4% √ net debt √ 1,024 =B1-B2 6.2× 1.9× TB 2,640 EBITDA 2.9M =B1-B2 6.2× 1.9× TB 2,640 EBITDA 2.9M 8.7% 12,480,900 $3,429.5k (312,000) — 1.24M √ tie-out p. 1 23.1% GL (108,000) sch. 4.1 $56M adj. +2.3% net debt 9,900,800 $4.7M 1,024 9.4% √ net debt √ 1,024 =B1-B2 6.2× 1.9× TB 2,640 EBITDA 2.9M 8.7% 12,480,900 $3,429.5k (312,000) — 1.24M √ tie-out p. 1 23.1% $3,429.5k (312,000) — 1.24M √ tie-out p. 1 23.1% GL (108,000) sch. 4.1 $56M adj. +2.3% net debt 9,900,800 $4.7M 1,024 9.4% √ net debt √ 1,024 =B1-B2 6.2× 1.9× TB 2,640 EBITDA 2.9M 8.7% 12,480,900 $3,429.5k (312,000) — 1.24M √ tie-out p. 1 23.1% GL (108,000) sch. 4.1 $56M adj. +2.3% net debt 9,900,800 $4.7M sch. 4.1 $56M adj. +2.3% net debt 9,900,800 $4.7M 1,024 9.4% √ net debt √ 1,024 =B1-B2 6.2× 1.9× TB 2,640 EBITDA 2.9M 8.7% 12,480,900 $3,429.5k (312,000) — 1.24M √ tie-out p. 1 23.1% GL (108,000) sch. 4.1 $56M adj. +2.3% net debt 9,900,800 $4.7M 1,024 9.4% √ net debt √ 1,024 =B1-B2 6.2× 1.9× TB √ net debt √ 1,024 =B1-B2 6.2× 1.9× TB 2,640 EBITDA 2.9M 8.7% 12,480,900 $3,429.5k (312,000) — 1.24M √ tie-out p. 1 23.1% GL (108,000) sch. 4.1 $56M adj. +2.3% net debt 9,900,800 $4.7M 1,024 9.4% √ net debt √ 1,024 =B1-B2 6.2× 1.9× TB 2,640 EBITDA 2.9M 8.7% 12,480,900 $3,429.5k (312,000) — 1.24M

The AI platform for Quality of Earnings.

The AI platform for Quality of Earnings.

The AI platform for Quality of Earnings.

Socratics.ai goes from raw data room to defended, fully sourced databook in hours instead of weeks.

Socratics.ai goes from raw data room to defended, fully sourced databook in hours instead of weeks.

Socratics.ai goes from raw data room to defended, fully sourced databook in hours instead of weeks.

Book a demo

Book a demo

Platform

The complete stack for financial execution.

Every layer is engineered to bring agentic work up to the standard your review already demands.

Frontier Models
Deliverable
A databook your partners can sign.
Create a live-formula Excel databook with every figure traced to the source. Formatting, structure, and schedules follow the way your firm already builds them.
Databook
LIVE FORMULAS
fx
= EBITDA_BRIDGE (Income_statement, D&A_schedule, Mgmt_adjustments)= PROOF_OF_CASH (Bank_statements, GL_cash, Bank_reconciliations)= REVENUE_QUALITY (Sales_register, Revenue_by_customer, Customer_contracts)= NWC_PEG (AR_AP_Inventory, Prepaids, Balance_sheet_24mo)= NET_DEBT (Debt_schedules, Consolidating_BS, Loan_agreements)= OWNER_COMP (Payroll_register, Employee_census, Comp_benchmarks)= RELATED_PARTY (Consolidating_BS, Intercompany, RP_agreements)
EBITDA bridge
Rebuilds EBITDA bottom-up from operating income plus D&A, controls it against the income statement, then walks reported to adjusted EBITDA through defined categories, each with its own supporting schedule.
✓traced to source
Proof of cash
Reconciles reported receipts and disbursements to actual bank activity. An independent check on earnings quality that does not rely on the company's own books.
✓traced to source
Revenue quality
Breaks revenue out by product and customer to test concentration and whether growth is recurring and durable under new ownership, not dependent on a few relationships or one-time events.
✓traced to source
NWC detail & peg
Calculates net working capital cash-free, debt-free and trends it to separate normal from abnormal swings and support a peg, with the cash conversion cycle alongside.
✓traced to source
Net debt
Breaks out interest-bearing debt by entity and instrument, netted against cash, to determine the debt-like obligations a buyer assumes at close.
✓traced to source
Owner comp
Compares actual owner and executive pay to market-rate benchmarks to flag above- or below-market compensation to normalize out of sustainable earnings.
✓traced to source
Related party
Identifies transactions with owners, affiliates, and related entities and compares terms to arm's length, flagging anything that would not survive a change of ownership.
✓traced to source
EBITDA bridgeProof of cashRevenue qualityNWC detail & pegNet debtOwner compRelated party

Product

Everything you need to defend the number.

Know what's missing on day one.

Run your PBC checklist against the data room up front. Gaps surface day one, not week three.

Project
Search file names
Recents
Data Room111
IRL
Information Request ListAUTO-TRACKED
6 of 8 documents received
Trial balanceReceived
General ledgerReceived
Bank statementsReceived
Bank reconciliationsRequested
Balance sheetReceived
Income statementReceived
Vendor informationOutstanding
Accounts receivableRequested
Ask a questionAtlas ▾
See the full lineage behind any number.

Trace any number from cell to formula to source, every tie-out logged.

Annual Summary
12Gross Sales
13Discounts
14Returns & Allowance
15Total Net Sales
Formula trace
C12Gross SalesSales_Summary.docx · Table 1DOCX
C13DiscountsDiscounts_Schedule.pdf · p.3, Table 2PDF
C14Returns & Allowance='Returns'!D12CSV
C15Total Net Sales=C12−C13−C14XLSX
Decide every judgment call yourself.

See the reasoning and evidence, then decide. Nothing counts without your sign-off.

Annual Summary
Net Sales — FY2020E base$69.3M
Net Sales — FY2020E base

This ×12/10 gross-up to $69.3M looks aggressive. How should I build the base?

Seasonalize on FY2017–2019 pattern
Check monthly data for seasonality first
Keep the straight ×12/10
Use FY2019 actual as the base
Ask a follow-up…
Run the full QoE in hours.

Your whole QoE as a live plan you can watch or pause. Computed, not guessed.

Project
Search file names
Recents
Data Room111
Execution Plan
Execution PlanStatus
Cover, Scope, Indexready
GL-TB Tie-Outready
FSLI Mappingready
Income Statementready
Balance Sheetready
Proof of Cashblocked
Revenue Analysisready
Gross Margin / COGSblocked
Opex & Payrollready
Seasonality / Run-rateblocked
EBITDA Bridgeready
Net Working Capitalblocked
Net Debt scheduleblocked
Forecast reconciliationready
DCF hooksready
Checksready
Findings summaryready
Ask a questionAtlas ▾
Run your firm's own QoE, on every deal.

Apply your firm's methodology and format to every engagement, and generate the reporting materials your partners already expect.

Proof of CashRun 41×
Reconciles cash to receipts
GL–TB Tie-OutRun 63×
Ties GL to the trial balance
Revenue by CustomerRun 47×
Revenue, concentration & churn
Adjustments AnalyzerRun 22×
Classifies EBITDA add-backs
Working Capital PegRun 18×
12-month net working capital
Quality of RevenueRun 9×
Cut-off & rebate testing
Net Debt ScheduleRun 31×
Debt-like items at close
EBITDA BridgeRun 28×
Reported to adjusted walk
AR AgingRun 37×
Receivables aging & reserves
AP & AccrualsRun 24×
Payables and accrued liabilities
Inventory RollforwardRun 15×
Cost and obsolescence
Deferred RevenueRun 12×
Contract liability rollforward
Net Working Capital TrendRun 7×
Monthly NWC trend
Revenue CohortsRun 11×
New vs returning revenue
Expense ReclassRun 13×
Below-the-line reclasses
Capex RollforwardRun 8×
Additions and disposals

Why it's different

A QoE runs on proof.

Most AI hands you its best guess. Socratics.ai proves the number instead.

fx= B4 − B5

Check the math yourself.

Inspect and recalculate any number. Every calculation lives in the workbook as a deterministic formula, not a guess.

BUILDING QoE00:42
IngestEBITDA bridgeNet debtOwner comp

Watch it happen, stop it any time.

Every action runs in view, from a single edit to a full engagement build. Stop it whenever you need to, and Socratics.ai keeps the record.

Private
Private
Private

Sharpen the process, keep your data private.

Improve with every engagement you run. Your documents stay private and never train shared models.

Financial due diligence is being rebuilt around AI. The teams that adopt it early are already running faster, better-defended engagements.

These are the results firms see when they run Socratics.ai on live deals.

0+ hrssaved per engagement
0%traced to source
0%less manual work
Security

Held to the standard your review demands.

Client documents never train shared models.

Encrypted in transit and at rest. Isolated, US-hosted workspaces per engagement.

Per-engagement roles and permissions. Full audit trail of every change.

Document contents are data, never instructions.

Export everything, any time, as standard Excel, Word, and PDF.

SOC 2 Type II underway. Ask us for detail under NDA.

Use cases

Built for

The teams who live in Quality of Earnings.

Buy-side & sell-sideCarve-outs & roll-upsConfirmatory QoELender- and IC-ready

Transaction advisory

A full QoE in hours. Every adjustment traces back to its source document.

CPA & accounting firms

Your methodology, at your standard, on every engagement — without new headcount.

In-house deal teams

Diligence your IC or lender can check line by line.

Frequently asked questions

Everything you need to know before getting started.

A Quality of Earnings platform built specifically for transaction advisory and financial due diligence practices, not a general AI assistant pointed at finance. Socratics.ai executes the QoE process against a data room, guided by your firm's own methodology.

Where most AI gives you its best guess, Socratics.ai gives you proof. Every number runs as a deterministic calculation, traced through every transformation, all the way back to the source.

Transaction advisory and financial due diligence practices running buy-side and sell-side QoE engagements. Funds and corporate development teams use the same platform for screen-level QoE at LOI speed.

Your team makes every judgment call. Socratics.ai runs your firm's own methodology and format, so it fits into the process you already run.

Yes. Each engagement runs in its own isolated workspace, and your documents are never used to train shared models. Everything in your data room is encrypted in transit and at rest, and hosted in the US.