Internal teams model what should happen (Financial Modeling). We audit what is happening (Behavioral Physics).
We do not rely on summary reports. We ingest system-of-record exports to build a Challenger Model based on observed physics: conversion, slippage, and cohort decay.
We normalize disparate VDR exports (Salesforce, HubSpot, NetSuite) into a standardized behavioral dataset. This enables repeatable stress tests across deals—regardless of target hygiene.
We take standard CSV exports from systems of record—directly from the Data Room. No IT access, no integration, no system credentials required.
We mathematically model the "Time-to-Close" curve. We identify stalled deals ("Ghost Pipeline") that have aged beyond the point of statistical probability.
We segment retention by vintage. Often, legacy cohorts are stable while recent cohorts churn 3x faster. Blended NRR hides this structural rot.
We detect "Duration Stuffing"—when a target pulls forward future renewals via discounting to hit a quarterly target, hollowing out the future.
If the dataset is insufficient, we say so immediately. We do not guess.
Key fields complete. Joins succeed. Reconciliations pass. Result: High-confidence conclusions with narrow risk bands.
Some missingness or partial history. Reconciliations mixed. Result: Conclusions delivered with explicit limitations and wider scenario bands.
Missing critical fields. Unreliable joins. Result: Screen Only (Red Flags) + Required Data Addendum. No underwriting conclusion.
Transparency builds trust. We are explicit about what our model can and cannot detect.
Zero IT footprint. Zero retention. We operate within your existing VDR environment.
We operate within Datasite, Intralinks, etc., or ingest via encrypted upload. No system integration required.
Upon delivery of the Final Memo, all client data is cryptographically wiped. We retain no residual IP from the target.
Start with a 48-hour Red Flag Screen. We'll show you the data quality grade and top 5 risks before you commit to deep diligence.
Book a Deal Screen