DDE / 001 — Diligence Infrastructure
Vol. 01 MMXXVI
Due Diligence Engine

DDengine

Preempt the Deal.
Flash QOE Standalone Cost Value Creation Dis-Synergies
Plate VI — From Trial Balance to Thesis DDE/IMG/001 · 2400 × 1029
01 — How it works § 01.00 / 04
INPUT

Source files in

VDR files ingested as-is. No template wrangling, no analyst pre-formatting, no data-room scraping.

Trial Balance P&L Census QoE + Other VDR Files
PIPELINE

Filter, classify, benchmark, analyze

A deterministic rulebook identifies cost-, asset-, and revenue-like items — AI assists where needed; every decision is recorded with applicable rationale.

Non-operating expense items identified
Balance sheet, revenue, non-recurring, allocations
Operating expense items classified
Function · subfunction
Cost type — labor, non-labor, outsourced
Cost behavior — fixed, semi-variable, variable
Output assembly & management questions
OUTPUT

Diligence report

A fully auditable Excel deliverable: benchmarked cost summaries, standalone cost estimate, value-creation and dis-synergy analysis, a data-integrity scorecard, and targeted management questions.

Re-run in minutes as new data emerges or management provides feedback — the model updates instantly.

02 — What you get § 02.00 / 04
01
Trial Balance
Every operating dollar classified with a full audit trail.
02
Benchmark Quality
Investment grade proprietary and commercial benchmarks available.
03
Integrity Scorecard
Quickly understand gaps in data and items needed to complete the diligence.
04
Management Questions
Auto-generated and targeted — not a generic, repetitive or redundant questionnaire.
Iterative Input and Report Update
05
Entanglement Analysis
Identifies functional and subfunctional labor and non-labor cost dependencies on the Parent.
Iterative Input and Report Update
06
Value Creation
Above-benchmark costs are quantified; trial-balance-level cost items are targeted for potential post-close reduction.
07
Dis-Synergy & Standalone
Incremental carveout cost and non-labor entanglement, modeled at the subfunction level.

⟲  Management Questions and Entanglement Analysis are iterative inputs — analyst responses and confirmed entanglement levels feed back into the model and update the report, sharpening the cost read with each round.

03 Why DDengine

Sharpen your thesis.

DDengine compresses the analytical work that usually decides whether a deal is worth pursuing into a same-day model.

i.

Speed at the front

From raw VDR files to a benchmarked cost model in hours. Screen more targets, kill the bad ones early, and reserve senior time for the deals that survive.

Same-day turnaround
No data prep
Transparent reporting
ii.

Multi-modal analysis

Every cost function is benchmarked and analyzed two ways — against commercially procured industry databases and proprietary, industry-specific AI benchmarks.

Institutional-grade benchmarks
Multi-model AI consensus
Bottom-up analysis
iii.

Not a static report

Every classification is rule-traced and analyst-confirmable. As management responds, new files arrive, and the team confirms classifications, the model updates — structured files shuttle to management and back. The model sharpens with the deal.

Rule-traced taxonomy
Round-trip override system
Data-integrity scorecard
04 — Engage § 04.00 / 04

Run your next target.

Founding-cohort slots are limited and going to active PE firms and corp dev teams. Send a target and we'll run it.

Join the founding cohort → See a sample deliverable →
Client Portal
Run type Select the stage of analysis for this submission
Run 1 — Always Initial Baseline Run Full pipeline — all 9 stages from raw VDR files
Run 2+ — Optional Update Run Upload an updated deliverable and/or individual tab files — Census, Management Questions, Entanglement, TB Overrides
01 Source Files Upload VDR files
02 Configuration Run settings
03 Review & Submit Confirm and run

Source files.

Step 01 / 03 — Required: Trial Balance
Required TB

Trial Balance

The primary source file. DDengine ingests the TB as-is — no pre-formatting needed. Multi-period tabs are supported; the engine will detect column headers and select the relevant period.

Export directly from your accounting system or as-provided from the VDR. Excel (.xlsx) or CSV preferred.
↑
Drop file or click to browse
.xlsx · .xls · .csv
Optional P&L

P&L / Income Statement

If provided, DDengine uses the P&L to anchor revenue for cross-checks and to enable TB-to-P&L period alignment (TB_PERIOD_ALIGN). Without it, benchmarks run on the TB period.

Monthly or quarterly P&L with a clearly labeled header row. Management accounts format is fine.
↑
Drop file or click to browse
.xlsx · .xls · .csv
Optional Census

Employee Census

Headcount roster used to drive labor attribution (tabs 14–18) and the Census Function Summary. Without it, census tabs are suppressed and standalone labor estimates rely on TB classification alone.

Include columns for name/ID, department, title, location, and compensation. Format flexibility — DDengine normalizes.
↑
Drop file or click to browse
.xlsx · .xls · .csv
Optional QoE

Quality of Earnings

Provides a pre-built non-recurring and normalizing adjustment schedule. When present, DDengine cross-references Stage 3 (non-recurring exclusions) against QoE-identified adjustments.

Sell-side or buy-side QoE in any format. DDengine extracts the adjustment schedule — full workbook is fine.
↑
Drop file or click to browse
.xlsx · .xls · .csv · .pdf
Optional VDR

Other VDR Files

Additional data room materials — management presentations, board decks, contracts, or supplemental schedules. Surfaced during Stage 9 (management questions) and stored for analyst review.

Multiple files accepted. Any format is fine for supplemental documents — they are indexed, not parsed by the pipeline.
↑
Drop files or click to browse
Any format · Multiple files
* Trial Balance is the only required file — all others are optional but improve output quality.

Run configuration.

Step 02 / 03 — Settings that govern the pipeline

Every setting is documented.

Settings map 1:1 to engine/settings.py. Defaults are correct for most initial runs — adjust for carveouts, specific periods, or benchmark requirements.

Deal
Identifying information for this run.
Used to label the deliverable workbook and for run history. Internal codename or target company name.
Operating context
OPERATING_CONTEXT · IS_STANDALONE · IS_CARVEOUT — controls which output tabs and analyses are active.
Standalone: target modeled as a going concern. Dis-synergy, entanglement, and carveout tabs are suppressed. Carveout: activates Tab 12 (Entanglement Analysis), dis-synergy modeling, and standalone cost estimation for costs currently shared with the parent.
Industry & NAICS
INDUSTRY_CONTEXT · NAICS_CODE — drives benchmark peer group and NAICS resolution.
Sets the primary industry bucket for APQC and AI benchmark peer group selection. If the target spans multiple sectors, choose the dominant revenue segment. Used for auto-NAICS resolution when no code is provided.
When provided, this code is used directly for L2 APQC and AI benchmark lookups — bypassing the auto-resolution that derives NAICS from INDUSTRY_CONTEXT. Leave blank to let the engine resolve. For benchmarking accuracy, a 6-digit code is preferred over a higher-level selection.
6 digits · leave blank for auto-resolve
Period alignment
TB_PERIOD_ALIGN — controls which TB quarters are selected to construct the LTM window.
Trailing 12M (default): selects the most recent 4 quarters of TB data on its own reporting cadence. Tab 1 period label reflects the TB's own LTM. Match P&L: forces TB quarters to align with the P&L LTM window — required when the TB trails the P&L by one quarter and you want an apples-to-apples comparison. Requires a P&L file.
Units
UNIT_MULTIPLIER · REVENUE_UNIT_MULTIPLIER — scale TB and P&L values to actuals.
The denomination of values in the Trial Balance. Actuals means values are in whole dollars; Thousands and Millions scale accordingly before pipeline processing. Benchmark comparisons are always done in actuals regardless of TB denomination.
The denomination of values in the P&L / income statement. Often matches the TB, but set independently when the VDR provides documents with different scale conventions.
Classification
AI_TB_CLASSIFICATION — controls whether AI assists deterministic classification on low-confidence rows.
The deterministic rulebook (Stages 5–7) always runs first. When On, AI assists only on TB rows where rule confidence is below threshold — every AI decision is still traceable and overridable. Recommended On for all production runs; Off only for speed testing or rulebook-only verification.
AI benchmarks
BENCHMARK_TYPE · FORCE_REFRESH_BENCHMARKS — multi-model AI consensus benchmarking (Tab 7).
DDengine queries three independent AI models and requires convergence before accepting a benchmark estimate. Cached: re-uses the last fresh result for this industry — fast, no additional cost. Fresh: forces a new multi-model run. Auto: uses cache if <30 days old, otherwise runs fresh. Off: suppresses Tab 7 entirely — APQC only.
When Yes, discards any cached AI benchmark result for this industry and forces a full fresh multi-model run — regardless of BENCHMARK_TYPE. Use after a NAICS or industry change, or when the cache is suspected stale. Has no effect when BENCHMARK_TYPE is Off.
Commercially procured benchmarks
APQC_BENCHMARKS · APQC_PASS_THROUGH — institutional industry benchmark database (Tab 6). Billed at cost.
When On, DDengine queries commercially procured APQC Process Classification Framework data for cost-as-% benchmarks at the function level. This is the institutional-grade source that feeds Tab 6 (APQC Benchmark Scorecard). Note: APQC data is a pass-through cost billed at cost — confirm availability for the target's NAICS before enabling on cost-sensitive runs.
Marks APQC data cost as a client pass-through in the engagement summary. Set to Client when APQC cost is billed directly to the deal; Absorbed when included in the run fee.
Override source
OVERRIDE_SOURCE — controls whether the pipeline ingests analyst classification overrides before Stages 5–9.
None: pipeline runs clean, no prior overrides applied — standard for the initial run. Updated file(s): reads confirmed changes from the deliverable and/or individual tab files you upload on an Update Run — auto-set, no need to pick this manually. Override file: reads a standalone override CSV/Excel — for programmatic or bulk corrections outside the standard tab-based flow.
Notes
Analyst context passed to the engine — surfaced in management questions, the integrity scorecard, and Tab 24 (Methodology).
Deal context useful to the engine — known data quality issues, specific cost concerns, seller representations, TB basis (cash vs. accrual), known census gaps, or areas to probe in management questions.

Review & submit.

Step 03 / 03 — Confirm before running
Source files
Run configuration
What happens next
DDengine will run all 9 pipeline stages against your uploaded files. You'll receive an email when the deliverable is ready — typically within the hour. The structured Excel workbook (DDengine Deliverable.xlsx) will appear in your Runs history for download.
Trial Balance required to submit.
✓
Run submitted

DDengine is running your analysis. You'll receive an email when the deliverable is ready — typically within the hour.

Runs

Deal Submitted Config Status Deliverable