Overview
Data-driven due diligence is the difference between a successful acquisition and a costly mistake. Traditional due diligence relies on manual document review and surface-level financial analysis, missing the hidden risks buried in operational data, customer behavior, and technology debt.
Root Cause Analysis
Why Traditional Due Diligence Falls Short
- Information Overload: Virtual data rooms contain thousands of documents with no automated extraction or cross-referencing.
- Financial Focus Only: Teams analyze financials but miss operational red flags in supply chain, customer concentration, and tech stack.
- Time Pressure: Compressed deal timelines force teams to cut corners on data analysis.
- Integration Blind Spots: Pre-deal analysis rarely evaluates data platform compatibility for post-merger integration.
Use Cases
1. AI-Powered Data Room Analysis
Challenge: A PE firm needed to evaluate 15,000+ documents in a data room for a $500M acquisition within 3 weeks. Solution: Deployed an Azure OpenAI-powered document intelligence pipeline that extracted key financial metrics, contract terms, and risk indicators automatically. Result: Document review time reduced by 70%. Identified a $12M revenue recognition issue that renegotiated the deal price.
2. Customer Revenue Concentration Analysis
Challenge: An acquiring company couldn't assess the target's customer concentration risk from summary financials alone. Solution: Built a Databricks analytics pipeline ingesting raw transaction data to calculate customer-level revenue dependency, churn risk, and LTV. Result: Discovered that 45% of revenue came from 3 customers with expiring contracts — critical information for deal structuring.
3. Technology Debt Assessment
Challenge: A strategic acquirer needed to evaluate the target's data platform maturity and integration complexity. Solution: Created a proprietary assessment framework analyzing data architecture, pipeline reliability, infrastructure costs, and technical debt indicators using Snowflake and dbt. Result: Identified $8M in required infrastructure investments, properly accounted for in the valuation model.
Cloud Technologies We Use
- Azure OpenAI — Document intelligence and automated extraction
- Databricks — Large-scale data analysis and pattern detection
- Snowflake — Centralized analytics for cross-source correlation
- dbt — Data transformation and quality validation
- Power BI — Executive-ready due diligence dashboards
- Alteryx — Rapid data blending for ad-hoc analysis