Allocator means M&A technical due diligence, portfolio construction, deciding which programs to fund and which to kill
Your PE firm just signed a letter of intent to acquire a $40M SaaS company. The Financial Statements look clean, EBITDA margins are 28%, and the seller's pitch deck shows a Data Moat in their ML pipeline. You have 21 days to figure out whether the technology actually works, whether the team can survive without the two founders, and whether the $6M in 'platform investment' on the Balance Sheet is a Capital Asset or a Wasting Asset. Your findings will directly change the Valuation - or kill the deal. This is M&A Technical Due Diligence.
M&A Technical Due Diligence is the systematic evaluation of a target company's technology, Knowledge Capital, and engineering Operations during an acquisition - turning qualitative claims about systems and teams into quantitative inputs for your Valuation model and post-close Operating Value plan.
You already know that M&A due diligence stress-tests the seller's Financial Statements and operational reality against your Valuation model. M&A Technical Due Diligence is the layer of that process focused specifically on technology and Knowledge Capital.
It answers three questions:
The output isn't a report that says "technology looks good." It's a dollar-denominated adjustment to your Valuation model: write-downs for technical debt, Implementation Cost estimates for post-close fixes, and a realistic Time Horizon for capturing the Operating Value you're underwriting.
If you're a PE Operator or working inside PE portfolio companies, technical due diligence directly determines three things that hit your P&L:
1. Purchase price adjustments. Every defect you find is negotiating leverage. If the platform needs $1.2M in rework to reach the seller's claimed capacity, that comes off the Enterprise Value or gets structured into Closing Adjustments. Miss it, and you've overpaid - a permanent drag on your Returns.
2. Post-close Capital Budgeting accuracy. The moment the deal closes, you own the Cost Structure. If due diligence missed that the infrastructure costs $380K/year more than the seller represented because they were running on a founder's personal cloud account with startup credits, that's an Income Shortfall you didn't underwrite. Your Hurdle Rate math breaks.
3. Integration risk and Time to Value. You're buying this company because it fits into a Portfolio thesis - maybe it adds a product line to a Multi-Brand Portfolio, or its technology enables Cost Reduction across existing Operations. If the technology can't integrate without a 14-month rewrite, your Investment Horizon assumptions are wrong and the NPV of the deal collapses.
The Operator who understands technology has an Informational Advantage over purely financial buyers. You can see through claims that a traditional M&A due diligence team would accept at face value.
Technical due diligence follows a structured process. Think of it as Triage applied to a codebase, team, and infrastructure.
You request access to the [UNDEFINED: data room] and start with artifacts:
You're building a Scoring Model across dimensions: code quality, infrastructure maturity, team risk, security posture, and scalability. Each dimension gets rated and translated into dollar impact.
This is where software expertise becomes a Competitive Advantage:
Every finding becomes a line item with a dollar value:
You produce a Sensitivity Analysis: best case, base case, and worst case adjustments to Enterprise Value based on your findings.
Always when technology is a material part of the Valuation thesis. If you're acquiring a SaaS company, a technology-enabled services business, or any company where the Data Moat or Knowledge Capital is a significant portion of what you're paying for, you need dedicated technical due diligence.
Scale the depth to the deal size and risk:
| Deal Size | Approach | Time Horizon | Typical Cost |
|---|---|---|---|
| Under $5M | Operator does it personally | 5-7 days | Your time |
| $5M-$50M | Operator + 1-2 senior engineers | 14-21 days | $30K-$80K |
| $50M+ | Dedicated technical due diligence firm + internal team | 21-30 days | $100K-$300K |
Skip or reduce scope when:
The decision rule: If more than 20% of the Enterprise Value you're paying is attributable to technology, Knowledge Capital, or Data Moat, full technical due diligence is mandatory. The Error Cost of skipping it dwarfs the Implementation Cost of doing it.
Your PE firm is acquiring a B2B SaaS company for $36M (6x trailing EBITDA of $6M). ARR is $14.4M, growing 18% year-over-year. The seller claims $2.1M in annual infrastructure costs and a 3-person platform team. The Valuation thesis assumes you can improve EBITDA margins from 42% to 55% within 24 months through EBITDA Optimization.
Infrastructure audit reveals true costs. You pull actual cloud invoices. The seller reported $2.1M but excluded $340K in data transfer costs categorized as 'cost of Revenue' and $180K in third-party API fees buried in 'general expenses.' True infrastructure cost: $2.62M. That's $520K/year the seller hid through creative Chart of Accounts classification.
Team risk assessment. The '3-person platform team' is technically accurate, but 1 of those 3 is the CTO-founder who holds all Tribal Knowledge about the ML pipeline - the claimed Data Moat. She has no Equity Compensation vesting post-close and told you in her interview she's 'excited to take some time off.' If she leaves, you need to hire 2 senior engineers at $200K each to replace her institutional knowledge. That's $400K in year-one risk you didn't underwrite.
Code quality findings. The ML pipeline has zero Quality Gates - no automated tests, no monitoring, no documentation. Estimated Implementation Cost to bring it to production grade: $600K over 12 months (2 engineers for a year). Without this investment, the defect rate will increase and the Data Moat erodes - it becomes a Wasting Asset.
Valuation adjustment. Hidden costs: -$520K/year ongoing (reduces EBITDA to $5.48M). CTO retention risk: -$400K year-one contingency. ML pipeline stabilization: -$600K Capital Investment. At 6x EBITDA on the corrected figure, fair Enterprise Value is $32.9M, not $36M. Plus $1M in post-close Capital Budgeting the seller didn't disclose. Your adjusted bid: $31M with a retention package for the CTO structured into Closing Adjustments.
Insight: Technical due diligence found $4.1M in value the seller's Financial Statements obscured. Without it, you'd have overpaid by 11% and missed critical Execution Risk in your EBITDA Optimization thesis. The Informational Advantage of being an Operator who reads code paid for itself 40x over the cost of the diligence process.
You operate a Multi-Brand Portfolio of three PE portfolio companies in retail. Brand A needs an inventory management system. Brand B built one internally 2 years ago for $800K. Brand C uses a $240K/year vendor. A fourth company with a mature Inventory Control platform is available for acquisition at $4.2M (3.5x Revenue of $1.2M). You need to decide: Build, Buy (acquire), or Hire (vendor).
Technical due diligence on the acquisition target (5 days). You review the $4.2M target's codebase. It's solid - 78% test coverage, documented APIs, Quality Systems in place. But the team is 6 people, and 4 of them built the product. Without retention, the Knowledge Asset degrades fast. Infrastructure Cost Per Unit: $0.12 per transaction. The platform handles 800K transactions/month.
Assess Brand B's internal system. You audit what was built for $800K. It works for Brand B's scale but has no Quality Gates, zero documentation, and one engineer maintains it. Extending it to Brand A would cost an estimated $400K and 9 months. Cost Per Unit: $0.31 per transaction at Brand B's volume of 200K/month. It's a Knowledge Asset with high Tribal Knowledge concentration risk.
NPV comparison over 5-year Investment Horizon. Acquire: $4.2M upfront + $720K/year (team) + $180K/year (infra) = NPV of -$7.8M at 12% Discount Rate. Serves all 3 brands. Build (extend Brand B): $400K build + $280K/year (team + infra) = NPV of -$1.5M. Serves only Brand A and B, not C. Vendor for all 3: $720K/year ($240K x 3) = NPV of -$2.6M. No Capital Asset, pure expense.
Risk-Adjusted Value calculation. The acquisition NPV is worst on paper but creates a Capital Asset you control across the entire Portfolio. If you add 2 more brands over 5 years (the PE thesis), the acquisition Cost Per Unit drops to $0.06 - half the vendor rate. Expected Value of the acquisition, weighted by Portfolio Construction plans: NPV improves to -$5.1M vs. -$3.9M for vendor (scaling to 5 brands). The crossover point is brand #4.
Insight: Technical due diligence on both the acquisition target AND your own Portfolio's existing systems is what makes a Build, Buy, or Hire decision rigorous. Without it, you'd compare sticker prices instead of Risk-Adjusted Return across your actual Investment Horizon. The decision flips depending on how many brands you plan to operate - which is a Capital Allocation question, not a technology question.
Technical due diligence converts qualitative technology claims into dollar-denominated Valuation adjustments - every finding should map to a line item in your Enterprise Value model or post-close Capital Budgeting plan.
The highest-value findings are usually in three places: hidden Cost Structure the seller reclassified, Tribal Knowledge concentration that creates Contingent Liabilities, and claimed Knowledge Assets or Data Moats that are actually Wasting Assets without continued investment.
Your Informational Advantage as an Operator who understands technology is the single biggest edge in PE acquisitions - it lets you see risk that purely financial buyers accept at face value, which means you either pay less or avoid bad deals entirely.
Treating technical due diligence as a pass/fail gate instead of a Valuation input. Every technology has problems. The question isn't 'is the code good?' - it's 'what does it cost to fix, and does the deal still work at the adjusted price?' Killing a deal over fixable technical debt is leaving money on the table. Quantify the Implementation Cost and adjust your bid.
Ignoring the team and focusing only on the code. Code is a Depreciating Asset without the people who maintain it. If 80% of the Knowledge Capital walks out after close because there's no retention incentive, the technology you bought is worth a fraction of what you paid. Map where Tribal Knowledge lives and price the retention risk into your Closing Adjustments.
You're evaluating a $20M acquisition target (5x EBITDA of $4M). The seller claims their proprietary recommendation engine is their core Competitive Advantage and Data Moat. During technical due diligence, you discover: (a) the 'engine' uses an open-source library with a thin configuration layer, (b) no tests or Quality Gates exist, (c) one engineer maintains it and has no post-close retention package, and (d) rebuilding equivalent functionality from the open-source library would take 3 months and cost $150K. How do you adjust your Valuation?
Hint: Think about what portion of the $20M Enterprise Value was attributable to the Data Moat claim. If the moat doesn't exist, what's the asset actually worth? Consider the Sensitivity Analysis: what's the base case EBITDA if the engineer leaves vs. stays?
The claimed Data Moat is not a Data Moat - it's a thin wrapper on commodity technology. The $150K rebuild cost tells you the proprietary technology has near-zero standalone value as a Capital Asset. Key adjustments: (1) Remove any premium attributed to the 'proprietary engine' from Enterprise Value. If the seller justified the 5x multiple partly on technology differentiation, argue for 4x-4.5x ($16M-$18M). (2) Price in the engineer retention risk: if she leaves, $150K rebuild + $200K opportunity cost during 3-month gap = $350K contingency. (3) If EBITDA depends on the recommendation engine working (it drives Upsell or Churn reduction), run a Sensitivity Analysis: what happens to Revenue if it goes down for 3 months? A 5% Revenue hit on $12M trailing = $600K. Risk-Adjusted Value of the deal drops to roughly $15M-$17M. Your adjusted bid should be $16M with a retention package for the engineer as a condition of close.
You operate two PE portfolio companies. Company A spends $1.8M/year on a SaaS vendor for its core workflow system. Company B is considering the same vendor at $1.1M/year. A competitor to that vendor is for sale at $5M with $2.4M ARR, 60% margins, and a 4-person engineering team. Walk through how you'd structure the technical due diligence and what findings would change your Buy vs. continue-with-vendor decision.
Hint: Think about the Portfolio Construction angle - you're not just buying for one company. Calculate the break-even: at what point does owning the technology beat renting it? And what technical findings would invalidate the thesis?
Structure: 14-day technical due diligence covering code quality, infrastructure costs, team retention risk, and integration feasibility with both Company A and B. Key findings that SUPPORT buying: (a) Clean codebase with Quality Systems, suggesting low post-close Capital Investment needed. (b) Infrastructure Cost Per Unit is lower than the vendor's implied pricing. (c) Team has documented processes (Knowledge Asset, not just Tribal Knowledge). Combined vendor spend: $2.9M/year. Acquisition at $5M pays back in under 2 years (Payback Period) before considering operational savings. NPV at 12% Discount Rate over 5 years: vendor = -$10.5M, acquisition = -$5M - $1.2M/year ops = -$9.3M, slight advantage to acquisition plus you own the Asset. Key findings that KILL the deal: (a) The product only handles Company A's workflow - Company B would need $1M+ in customization. (b) 3 of 4 engineers are unvested and likely to leave. (c) Infrastructure is on Obsolescence-risk technology requiring migration. Any of these breaks the Portfolio Construction thesis and you're better off staying with the vendor.
M&A Technical Due Diligence builds directly on your understanding of M&A due diligence - the general framework for investigating an acquisition target. Where general due diligence focuses on Financial Statements, legal risk, and market position, technical due diligence zooms into the technology and Knowledge Capital layer specifically. It also depends on understanding Knowledge Asset - you learned that knowledge compounds and appreciates when maintained. Technical due diligence is where you assess whether a target's knowledge is actually encoded in systems (a durable Knowledge Asset) or trapped in people's heads (Tribal Knowledge that creates Contingent Liability risk). Downstream, this skill feeds directly into Capital Allocation decisions across a Portfolio - once you can accurately value technology, you can make rigorous Build, Buy, or Hire decisions, set realistic Hurdle Rates for post-close Capital Investment, and run Portfolio Construction that accounts for technology synergies across PE portfolio companies. It also connects to EBITDA Optimization: the findings from technical due diligence become your post-close Operations roadmap, telling you exactly where to invest for Cost Reduction and where the Wasting Assets need immediate Capital Budgeting attention.
Disclaimer: This content is for educational and informational purposes only and does not constitute financial, investment, tax, or legal advice. It is not a recommendation to buy, sell, or hold any security or financial product. You should consult a qualified financial advisor, tax professional, or attorney before making financial decisions. Past performance is not indicative of future results. The author is not a registered investment advisor, broker-dealer, or financial planner.