The data risk landscape — what PE acquirers face
The Problem
Most PE-backed companies carry years of technical marketing debt — disconnected systems, unmeasured spend, and data blind spots that depress multiples at exit.
The Solution
We embed alongside your deal team to identify, quantify, and eliminate marketing debt — before LOI and through the 100-day plan.
The ML & AI Edge
We deploy proprietary ML models and AI compliance frameworks — quantifying marketing efficiency gaps and validating whether data assets can actually be used.
Acquisitions increasingly price in the value of proprietary data and AI capabilities — but most due diligence stops at "how much data?" We validate whether that data can actually be used: PII exposure, consent gaps, licensing restrictions, GDPR/CCPA compliance, and EU AI Act readiness. Data that can't be used for AI training is worth zero.
Probabilistic multi-touch attribution that surfaces the true cost-per-acquisition across every channel — even in cookie-less environments.
Early-warning ML models trained on EdTech and SaaS cohort data. Identify at-risk customers 90 days before they show standard churn signals.
We assess whether data assets are clean, consented, and compliant enough to support AI models — covering GDPR, CCPA, and EU AI Act obligations before they become deal-breakers.
Bottom-up TAM/SAM/SOM models built from first-party data signals, not industry reports. Credible numbers that hold up in IC presentations.
Process
A structured engagement model designed for the pace of PE transactions.
Confidential intake. We map your portfolio company, deal stage, and specific technical risk areas.
Intensive 2-week technical assessment. Full MarTech stack review, data infrastructure analysis, team capability audit.
A redacted audit report with quantified risks, EBITDA impact estimates, and integration flags.
100-Day blueprint execution. We embed with the portfolio team, drive delivery against EBITDA milestones, and ensure AI and data compliance is built in from day one.
Perspectives
The Data Debt Hidden in Your Next Acquisition
Most PE due diligence processes overlook the compounding cost of inherited data infrastructure — CRM rot, broken attribution, and ungoverned PII that create liability, not value.
Why AI Valuations Need a Data Validation Layer
Acquirers are pricing in proprietary data assets and AI capabilities — but rarely asking whether that data is legally usable for model training. GDPR, CCPA, and the EU AI Act create overlapping constraints that can invalidate an acquisition thesis overnight.
What We Find in Every MarTech Audit
Across PE-backed companies of every size and sector, the same five failure patterns appear: attribution collapse, stack redundancy, data silos, PII sprawl, and misaligned org design. Here's what to look for.