TAS · TRANSACTION SERVICES
Technology M&A diligence that speaks EBITDA and DevOps
Most technology diligence misses the gap between financial logic and operating reality. We run quality-of-earnings, revenue durability, technical debt, IP ownership, and integration risk as one decision system.
BEST FIT
Who this service is for, and when to use it.
The mandate follows the constraint, not the menu. This service line solves a specific operating problem; the trigger below tells you when it is the right opening move.
- AUDIENCE
- Private equity sponsors, founder-sellers, and boards evaluating technology middle-market transactions
- TRIGGER
- Use this when the model depends on synergy capture, clean ARR, scalable delivery, or a technical platform that must survive buyer diligence.
- SERVICE CODE
- TAS
ENGAGEMENT TIMELINE
Transaction Advisory Services primarily lives in diagnostic assessment.
Each service line lives inside the four-phase operating journey. This phase is where this engagement spends most of its operating cadence.
PHASE 01
Diagnostic Assessment
Days 1–14
TAS lives in the diagnostic. Operating diligence runs alongside financial diligence so the buyer underwrites the system, not the slide.
- Operating constraint baseline against the value-creation thesis
- Technical debt and IP defensibility quantified in dollars
- Integration risk register tied to retention and synergy capture
OPERATOR RESULTS
What an operator tests before the model gets trusted
Transaction diligence is not just a report on historical numbers. We test whether the technical platform, delivery model, customer base, and integration path can support the value creation plan a buyer is underwriting.
ENGAGEMENT OUTCOMES
What the work produces.
Outcomes are what the engagement leaves behind for the executive team to operate with. They are not intermediate deliverables; they are operating moves.
- OUTCOME 01
- Diligence memo tied to value creation risk
- OUTCOME 02
- Technical debt quantified in dollars
- OUTCOME 03
- Integration thesis validated before close
Transaction diligence is not just a report on historical numbers. We test whether the technical platform, delivery model, customer base, and integration path can support the value creation plan a buyer is underwriting.
RELATED INTELLIGENCE
Field notes that support transaction advisory services.
Read insights
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The Margin That Wasn't There: Auditing AI Vendor Dependency Before You Sign
A SaaS target's 82% gross margin can hide a single-vendor API bill that quietly halves it. How to diligence AI dependency, model drift, and COGS before LOI.
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Your AI Model Is Worth $0 If You Can't Trace the Training Data
Acquirers discount AI IP up to 60% when data provenance is murky. How to prove lineage on your models and training sets before a PE deal team arrives.
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The Day Your Acquired ML Team Starts Interviewing: A Post-Close Retention Playbook
Acquired ML engineers don't quit over money. They quit the week IT puts a two-week ticket between them and a GPU. Here's the post-close playbook that keeps them.
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The MLOps Audit: How to Price an AI Target Before the Models Quietly Rot
AI targets don't fail in the codebase—they fail in the retraining pipeline. A buyer's field guide to auditing MLOps maturity, model drift, and registry gaps.
BRIEF · TAS
How to Diligence a GenAI Acquisition: Reading the CIM Against the Inference Bill
A PE diligence playbook for tech M&A: separate a real GenAI moat from a $25/month API wrapper, audit the IP chain, and price inference cost before you sign.
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The Brittle System Problem: When a Dashboard Tweak Takes Down Billing
A two-line change to a reporting page shouldn't crash your payment gateway. When it can, buyers cut the price. Here's how brittleness becomes a 22% discount.
DECISION GUIDES
When this service is the right move.
- AI Agent vs. Workflow Automation: Decision Guide A decision guide for choosing an AI agent, internal copilot, or workflow automation for a business process.
- AI Knowledge System vs. Chatbot: Decision Guide A decision guide for choosing an internal AI knowledge system, support copilot, or customer-facing chatbot.
- Asset Deal vs. Stock Deal: Technology M&A Decision Guide A board-level decision guide for choosing asset deal, stock deal, or hybrid structure in technology middle-market acquisitions.
- Carve-Out vs. Full Acquisition: Technology Integration Decision Guide A decision guide for choosing carve-out, full acquisition, or phased TSA structure when technology systems, teams, and customer operations must separate cleanly.
- Technical Diligence vs. Financial Diligence: Technology M&A Decision Guide A decision guide for choosing technical diligence, financial diligence, or integrated diligence when technology company value depends on both the numbers and the operating system.
- Transaction Advisory Services vs. Investment Banker: M&A Readiness Decision Guide A decision guide for choosing transaction advisory, investment banking, or integrated sell-side readiness support before a technology middle-market M&A process.
OPERATOR RESOURCES
Checklists and scorecards for this service line.
- Exit Readiness Scorecard A 12-18 month readiness scorecard for technology companies preparing for buyer diligence, investment banking preparation, or PE exit planning.
- Integration Risk Checklist A pre-close and Day 1 checklist for technology acquisitions where customer retention, staff retention, data migration, and synergy capture depend on execution quality.
- Technical Debt EBITDA Worksheet A finance-and-engineering worksheet for translating release drag, rework, incidents, and platform fragility into EBITDA and valuation exposure.
COMMON QUESTIONS
Operator-grade answers.
The questions that come up before the first call. Relevant outcomes are listed on the results page.
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What makes operator-led transaction advisory different?
We evaluate the operating system behind the numbers: code quality, delivery capacity, data integrity, sales process, customer concentration, and integration risk. That gives buyers a clearer view of what they can actually own after close.
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What deal size is a fit?
We focus on technology middle-market companies: typically 50-300 employees, $10M-$100M ARR, and $50M-$300M enterprise value.
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