How It Works

Operator-led technical diligence replaces the conventional model—where a consulting team delivers a report and moves on—with a single principal who carries context from first look through post-close execution. The process is designed for PE deal timelines and structured around five phases.

1. Engagement

Introduced by the deal team. Receives the CIM, management presentations, and data room access. Scope and timeline are defined around the deal's exclusivity clock and the specific technical questions the investment thesis depends on.

2. Assessment

Hands-on, keyboard-level analysis. I sit in management presentations, review the codebase, access production infrastructure (with permission), evaluate CI/CD pipelines, cloud cost structure, security posture, and team composition. This is not a checklist exercise—I read code, query databases, inspect deployment configurations, and trace request flows end to end.

3. Findings

A risk-quantified assessment where every finding is tied to dollar impact or timeline risk. Not a 200-page report with generic scorecards—a focused document the deal team can underwrite from. Clear enough to price, honest about what remains unknown until Day One.

4. Remediation Roadmap

A prioritized plan mapping diligence findings to a post-close execution timeline. Each item is sequenced by risk severity and value creation potential, with cost estimates and resource requirements. This roadmap is written by the person who will execute it—not translated by a separate team.

5. Post-Close Execution

Optional but typical. The same principal executes the remediation roadmap. First 30 days: triage and quick wins—the highest-impact, lowest-risk items that demonstrate momentum and build trust with the portfolio company team. Days 30–90: structural remediation—architecture changes, security hardening, cost optimization, and the foundational work that compounds over the hold period.

Who It's For

Operator-led diligence is built for PE firms evaluating software acquisitions where the technical dimension is material to the deal thesis—not a box to check, but a lever that determines whether the investment creates or destroys value.

  • The deal thesis depends on technical transformation. Modernization, cloud migration, AI integration, or platform consolidation post-close. The person who assesses the current state should be the person who designs and executes the target state.
  • The CTO is a key-person risk or flight risk. When the technical leader may not survive the transition, you need an operator who can evaluate the team's true capability independent of its current leader—and step in if needed.
  • Cloud and infrastructure costs are a material COGS line item. When hosting costs are a significant percentage of revenue, diligence needs to quantify the optimization opportunity with enough precision to model it into the deal.
  • Previous diligence reports failed to translate into post-close action. The assessment was accurate. The execution plan derived from it was not—because the people who wrote it were already staffed on the next deal.
  • The acquisition is a platform deal in a roll-up strategy. Technical integration across multiple acquisitions requires an operator who understands the full portfolio context, not a fresh team re-learning the landscape with each bolt-on.

What You Get

The deliverable is not a report. It is a complete technical risk assessment with an execution plan attached—and optionally, the operator who wrote both.

Large-Firm Model

A scored report benchmarked against proprietary databases. A rotating team of consultants delivers the assessment. A different team—internal or external—translates findings into a 100-day plan. Context degrades at the handoff. The diligence team moves to the next engagement.

Operator-Led Model

A risk-quantified assessment with underwriting-grade inputs—not a generic scorecard. A remediation roadmap with timeline and cost estimates. The same principal available to execute post-close. Diligence insights become execution inputs without translation loss.

Selected Engagements

Outcomes delivered across PE-backed software portfolio companies. All engagements originated as buy-side diligence and extended into post-close execution.

AI document ingestion engine: Built an AI-native system processing 100K+ legacy documents—including damaged and handwritten scans—with automated classification and extraction. Compressed what was previously 45 days of manual processing into one business day, accelerating customer onboarding and enabling competitive displacement at scale.
Cloud cost optimization: Identified anomalous CDN cost patterns during a 48-hour rapid diligence. Post-close, reduced CDN costs by 60% and compute costs by 40%—directly expanding EBITDA. Delivered within 30 days of close.
Security remediation: Deployed enterprise-grade web application firewalls across portfolio companies, closing security gaps that had persisted since inception. Identified and neutralized active SQL injection, XSS, and RCE attack vectors within weeks of engagement.
Authentication modernization: Diligence uncovered a hard-coded password shared across millions of user accounts. Post-close, rebuilt the entire authentication system—modern Firebase auth powering a Flutter-based mobile app for 2.5M users. Deployed within weeks.
Zero-downtime region migration: Executed a full production migration from us-west-1 to us-east-1 with zero downtime, including cross-account multi-region disaster recovery implementation.

For questions about timelines, pricing, and engagement structure, see the FAQ.