Technical Due Diligence in 2026: Scope, Checklist, and the Lender-Grade Report

Last updated: July 2026

Quick answer: Technical due diligence has split into two products. One is the acquirer's conviction review — does the code, architecture, and engineering team justify the price and the plan? The other is the independent report your lender relies on before it funds the deal. They cover the same workstreams but are written for different readers, and on a leveraged deal you often need both. The bottleneck has moved: the hard part is no longer reading code, it is verifying claims — scalability, uptime, code ownership, license cleanliness — with evidence instead of interviews. On a $40M SaaS target, plan for a phased review inside the exclusivity window: a few days pre-LOI, two to four weeks post-LOI, a confirmatory pass before close. Run it in-house when you have senior engineers to spare and no external report requirement; hire a firm when you lack bandwidth, the domain is outside your team's expertise, or a lender wants independence. Scope it like a lender: evidence over interviews.

I'm Deqian Jia, co-founder of Peony, a data room company serving 6,800+ customers whose deals hold $26.3B in client assets. Over the last two years I have watched deal teams stand up technical due diligence rooms for everything from a $12M dev-tools acquisition run entirely by two in-house engineers to a $180M carve-out where the lender's advisor and the buyer's engineering team worked the same room from opposite ends. The pattern that keeps repeating is the one this post is built on: technical due diligence is no longer one deliverable. It is two — the buyer's conviction review and the independent report the lender relies on — and the work that decides both has shifted from reading source code to verifying the claims around it.

That shift is happening inside a market that is doing more with fewer, larger deals. Global M&A announcements reached $2.8 trillion in H1 2026, a 48% increase over the same period in 2025 (per LSEG data), even as the number of transactions fell — around 24,000 deals, down 9% year over year. Fewer, bigger, more scrutinized deals mean the technical review carries more weight per transaction, and more of it is now done with tooling rather than headcount. Per Bain's 2026 M&A Report, AI adoption among M&A practitioners more than doubled, to 45%, in a survey of 303 senior dealmakers — and one in five strategic dealmakers walked away from a deal because of the anticipated impact of AI on the target's business. The tooling has changed; the questions have not. Below is how to scope, run, and staff a technical review in 2026 — and how to structure the room so both the conviction review and the lender's report run off the same evidence without contradicting each other.

I run Peony, a data room company, and I will be straight about where we fit: Peony organizes, permissions, and evidences the documents a technical review runs on. It does not read the target's code, benchmark its architecture, or write the report — that is the reviewer's work product. What the room does is make the exchange governed and auditable, so the buyer's engineers and the lender's advisor can both see what they need, and no more. More on that in the data-room section; the rest of this post is the review itself.

What is technical due diligence — and how is it different from IT, cybersecurity, and AI due diligence?

Technical due diligence examines the code, architecture, engineering team, and scalability of a software target — it is the review you run when the product itself is the asset you are buying. That is the clean line that separates it from the three neighbours it gets confused with, and getting the partition right matters, because scoping the wrong review wastes weeks and misses the risk that actually kills the deal.

Here is the partition I use, and it holds across every deal I see:

• Technical due diligence is code, architecture, engineering team, and scalability of a software target. The question is: does the product hold up under the hood, and can it scale under new ownership?
• IT due diligence is broader and applies to any target, software or not: infrastructure, applications, the vendor and license stack, cloud cost, and engineering practices. A logistics company or a dental group has an IT stack worth diligencing even though it ships no software. IT DD is the systems-and-infrastructure review; technical DD is the code-and-architecture review.
• Cybersecurity due diligence is a security-posture deep dive — breach history, attack surface, ransomware exposure, third-party cyber risk, and regulatory compliance. It is narrower than IT DD but far deeper on security specifically.
• AI due diligence diligences an AI company, or AI features inside a broader target: model provenance, training-data rights, model risk, and regulatory exposure such as the EU AI Act.

The overlap that trips people up is security. Technical due diligence has its own security workstream, but it is a narrow one: secure-development practices and code-level review — does the target build software securely, and does the code contain obvious vulnerabilities? It maps that workstream against a recognized standard; the canonical federal reference is NIST Special Publication 800-218, the Secure Software Development Framework (SSDF) Version 1.1, published February 2022. What technical DD's security workstream deliberately does not do is the full breach-posture and attack-surface work — that belongs to cybersecurity due diligence, and on any deal where security is a live concern you run both, with the technical review handing the deep security questions across.

If your target ships AI features, the same handoff applies: technical DD flags that AI is present and assesses how it is built, then defers the model-provenance, training-data, and EU AI Act exposure to AI due diligence. And if the target is a non-software operating company that happens to run a lot of systems, you are probably scoping IT due diligence, not technical DD. Get the partition right first; everything downstream depends on it.

What does technical due diligence cover on a software acquisition?

Technical due diligence on a software acquisition covers eight workstreams. Each one answers a specific question a buyer must be able to defend to an investment committee or a lender, and each produces evidence, not just an opinion. Independent providers organize their frameworks slightly differently — AKF Partners evaluates across five areas it lists as "Technology," "AI / ML," "Organization," "Process," and "Security"; Crosslake Technologies covers "infrastructure, SDLC, information security, organization, business continuity, software architecture, customer support, product management and enterprise systems" — but the underlying scope maps to the same eight workstreams below.

Two of these deserve emphasis because they are where I see the most money left on the table. The security posture workstream is narrow by design — it is about how the target builds software, not its full breach history — and anchoring it to NIST SP 800-218 gives you a defensible standard to grade against instead of a gut feel; the deeper attack-surface and breach work belongs to cybersecurity due diligence. The data and IP / OSS licensing workstream is the one non-engineers underweight: a single copyleft license entangled in the proprietary codebase can force the buyer to disclose its own source or fund an expensive re-architecture, and it is invisible unless someone runs the open-source inventory. Scope all eight, then weight the depth to the deal — a dev-tools company lives or dies on code quality and OSS licensing; a high-traffic consumer app lives or dies on architecture and scalability.

How do you run technical due diligence step by step?

You run technical due diligence in three phases mapped to the deal stages, not as a single sprint the week before close. That is the framing I use across every deal, and it mirrors the phased cadence in our IT due diligence framework — a short pre-LOI screen, a deep post-LOI review, and a confirmatory pass — because the same logic applies: you spend a little to decide whether to sign the LOI, spend the bulk to negotiate the price, and spend a little more to confirm before you fund.

Phase 1 — Pre-LOI screen (a few days). The goal is to surface deal-killers before you commit to exclusivity. This is deliberately light: one architecture walkthrough with the target's lead engineer, a scan of the top contracts and the open-source inventory for obvious contamination, a security-posture check, and a first read on key-person risk. You are not reading the whole codebase here — you are looking for the one finding that means "do not sign." Output is a short memo flagging any workstream that looks red.

Phase 2 — Post-LOI deep dive (two to four weeks). This is where the real work happens, during exclusivity when you have access and leverage. It covers the full eight-workstream review: a code review or supervised read, architecture and scalability validation, the full open-source and IP sweep, cloud cost-to-serve analysis, and the security workstream mapped to the SSDF. Critically, it includes interviews — management interviews with the CTO and head of platform, and separately, technical interviews with senior engineers without the executive filter in the room, because the engineers tell you where the bodies are buried. Output is the findings register that drives price negotiation, indemnities, and the retention plan.

Phase 3 — Confirmatory pass (a few days). Before close, you re-test anything that was pending, confirm that scalability or remediation claims made during Phase 2 actually held, and finalize the day-one plan. Output is a green/yellow/red status for the deal committee.

Who is in the room. On the buyer's side: a technical lead (in-house or an independent firm), the corp-dev owner who runs the process, and counsel for the IP and OSS findings. On the target's side: the CTO or VP Engineering, one or two senior engineers for the technical interviews, and whoever controls source-code access. If a lender is financing the deal, its independent advisor is a distinct participant with its own scope — more on that below.

On timeline: in our experience across deals run in our data rooms, the whole arc fits inside a typical exclusivity window, and the constraint is almost never how fast someone can read code. It is two operational things — getting source-code access actually granted, and getting time on the calendars of the target's senior engineers. Both are slow if you start them late. Begin the access conversation and the interview scheduling on day one of exclusivity, not week three, and the review lands on time. Let them slip, and no amount of reviewer speed recovers the calendar. (For where cost-timeline tradeoffs bite the whole deal, our due diligence cost breakdown covers the budget side.)

Should you run it in-house or hire a firm?

The honest answer is that it depends on three things — your team's bandwidth, the target's domain, and whether anyone external needs to rely on the result — and on most $40M deals the real answer is some of both. Let me split it cleanly.

Run it in-house when you have senior engineers who can be pulled off roadmap work for the two-to-four-week deep dive, the target's stack is close enough to your own that your engineers know what good looks like, and there is no external report requirement. In-house review is cheaper, and there is a quality your own engineers bring that a firm cannot: they know which architectural choices will actually hurt your integration, because they will be the ones doing it. The cost is real, though — it is not free, it is your best engineers not shipping your roadmap for a month, and it carries key-person risk on your side if one person does the whole review.

Hire a firm when you lack that bandwidth, the target's domain is outside your team's expertise, the deal is large or contested enough that you want benchmarking data you do not have, or — the decisive one — a lender or investment committee wants an independent opinion they can rely on. An internal review, however good, is not independent by definition.

On who the firms are, using their own positioning: Crosslake Technologies describes itself as "Your Private Equity Tech Due Diligence Advisor" and benchmarks targets using what it calls "proprietary TechIndicators®, based on data from more than 6,000 prior technology M&A transactions," with the report ordered by the deal partner, operating partner, and investment committee. AKF Partners evaluates across five areas it lists as "Technology," "AI / ML," "Organization," "Process," and "Security," and states it has "performed technical diligence for seed rounds, A-series and beyond, carve-outs, strategic investments and taking public companies private. From $5 million invested to over $1 billion." West Monroe runs a technology transaction practice, predominantly for private-equity clients, whose diligence diagnostic surfaces technical debt, cyber vulnerabilities, and operational inefficiencies and delivers a roadmap to protect and grow equity across the hold period. Alvarez & Marsal also runs a software product and technology diligence practice serving private-equity buy-side and sell-side work.

Two cautions. First, notice the common thread in how these firms position: private equity, investors, strategic acquirers — buy-side and sell-side. None of them frames itself as a lender's firm, so do not assume any given firm is the one your lender will rely on; the lender chooses and instructs its own advisor, which is exactly the subject of the next section. Second, the build-versus-buy choice is not just in-house-versus-firm — it is also automated-versus-manual. Static analysis and automated scanning (the SonarQube-style tools) are cheap and fast and belong in every review, but they find code smells, not architectural fragility or key-person risk. They are an input to a consultant-led or engineer-led review, not a replacement for one. Use the scanners to cover breadth and the humans to find the findings that actually move price.

What does the lender-grade independent report cover?

On leveraged software deals, lenders commonly commission an independent technical assessment before they fund, and the reason is straightforward: on a software company, the code and the engineering org are the collateral behind the loan. A lender extending debt against a SaaS business is underwriting the durability of the product and the team that maintains it, and it wants an opinion from someone who does not work for the buyer. That is the lender-grade report — my term for the rigor and independence the report carries, not a label any firm puts on itself.

What the report contains. Substantively, it covers the same eight workstreams as the buyer's conviction review — architecture and scalability, code quality and technical debt, the SDLC security workstream, infrastructure and cost-to-serve, data and IP/OSS licensing, engineering team and key-person risk, and roadmap. The difference is not the subject matter; it is the reader. A lender-grade report is written for a credit committee that was not in any management meeting, so every material finding is evidenced rather than asserted, the risks are rated and quantified where possible, and the document carries an independent opinion the lender can put in its file. It reads less like internal deal notes and more like an assessment that stands on its own.

Who provides it. The independent firms that run technology diligence practices — Crosslake, AKF Partners, West Monroe, Alvarez & Marsal — are the usual providers, but here is the important caveat, and I want to be precise because a lot of content gets it wrong: do not describe any firm as "lender-accepted." These firms position around private equity and acquirers, not lenders, and no firm is universally accepted by lenders. The lender chooses and instructs its own advisor, or tells you which firm's report it is willing to rely on. So the first move is not to pick a firm — it is to ask your lender who they will rely on, and let that answer drive the engagement.

Can one report serve both audiences? This is the question every corp-dev lead asks, and the honest answer is: sometimes, but only if you engineer it that way from the start. The tension is genuine. Your conviction review is written for people who sat in the room and can read between the lines; the lender's report is written for a third party who needs everything spelled out and independently opined. A report built purely for internal conviction usually will not carry the independence a credit committee wants, and a report scoped for the lender may skip the integration and roadmap questions you care about most. There are two workable paths. One: agree the scope with the lender up front, decide who instructs the firm, and commission a single report both sides accept. Two: run two coordinated workstreams — your team and the lender's advisor — off the same data room, so the findings are drawn from identical evidence and cannot contradict each other. The failure mode to avoid is two reviews running blind to each other and landing on the deal committee's desk with conflicting conclusions. The next section is how the room prevents exactly that.

What are the technical red flags that kill deals?

A handful of technical findings recur across dead deals, and the useful thing to know is that almost none of them are fatal on their own — they are fatal when they are discovered late, cannot be verified, or cannot be priced. Here are the ones that actually kill software deals, in rough order of how often I see them do it.

1. Key-person risk. One or two senior engineers hold the architecture in their heads, nothing is documented, and the whole thing walks out the door if they leave post-close. This is the single most common serious finding in software DD. It is survivable with retention packages, earn-outs, and a documented knowledge-transfer plan — but only if you find it in time to structure around it. Discovered at the confirmatory stage, it is a scramble; discovered at close, it is a write-down waiting to happen.

2. A total source-code access standoff. A target that permits no meaningful technical review — no supervised read, no third-party escrow review, no scan — is a red flag, because you cannot underwrite what you cannot verify. Note the word "total," though: a scoped access process is normal, and the next paragraph covers what standard looks like. It is the blackout, not the caution, that kills the deal.

3. Unvalidated scalability claims. The CIM says the platform handles 10x current load. The reality is no load tests, no capacity headroom, and an architecture that will not get there without a rewrite. Scalability is the claim most often asserted and least often evidenced, and validating it independently — load-test artifacts, capacity math, an architecture that actually supports the claim — is where a technical review earns its fee.

4. Open-source license contamination. A copyleft license such as GPL entangled in the proprietary codebase can force the buyer to disclose its own source code or fund an expensive re-architecture. It is invisible without an open-source inventory and SBOM, and it is a legal finding as much as a technical one — loop in counsel the moment the inventory flags it.

5. Runaway technical debt with no register. Debt itself is normal and priceable; debt that nobody has quantified is the problem, because it means the cost of the roadmap the deal thesis depends on is unknown.

6. Undocumented single points of failure. A critical system with one instance, no failover, and no disaster-recovery plan is an availability risk the buyer inherits on day one.

7. No secure-development discipline. A codebase built with no secure-development practices — measured against a standard like NIST SSDF SP 800-218 — signals latent vulnerability, and it hands work to cybersecurity due diligence to size the full exposure.

On the access standoff specifically, because it is the one that causes the most needless deal friction: a target refusing to hand over a full copy of its source code is not a red flag — it is normal, and expecting a competing party to take a complete copy of your crown jewels is naive. The standard alternatives are well established and you should propose them rather than escalate. Supervised read-only review lets your reviewer or an independent firm read the code on the target's machine or in a controlled environment without downloading it. Third-party escrow or a neutral firm can review the full codebase and report findings without the code ever reaching the buyer. Automated scans can run against the repository with only the results shared. Any of these preserves the target's protection while giving you real verification. Reserve your alarm for the target that will not permit even a supervised read — that is the blackout that should end the conversation.

How do you structure the data room for technical due diligence?

Structure the room two ways at once: by workstream so reviewers find evidence instead of hunting for it, and by audience so the buyer's engineers and the lender's advisor each see only what they should. This is the part of the deal where a data room company has a genuine, honest wedge — and also a clear limit I will name up front, because it matters for setting expectations.

Start with a request-list-driven room: one folder per workstream, seeded with the request list before the reviewers arrive, so day one is analysis and not a scavenger hunt. The structure maps directly to the eight workstreams above.

Then layer the access controls that make a multi-party technical review safe:

• Visitor groups per audience. Your buyer engineering team, the lender's independent advisor, and counsel each get their own scoped view — the lender's firm sees the security and architecture folders it was instructed to opine on, counsel sees the IP and OSS folders, and nobody sees folders outside their remit. This is exactly the mechanism that lets the conviction review and the lender's report run off the same evidence without either party rummaging through the other's scope.
• Watermarks on sensitive design docs. Dynamic watermarks stamp each viewer's identity onto every rendered page of the architecture and design documentation, so if a diagram leaks it carries a forensic trail back to whoever exported it.
• Page analytics on the reviewer's actual reading. Page-level analytics show which sections the lender's advisor actually read — genuinely useful when their report lands and you want to know whether they went deep on the scalability folder or skimmed the cover page, and equally useful for spotting which workstream a reviewer is stuck on so you can get them what they need.
• Smart Q&A for the technical question queue. A software technical review generates a hundred-plus technical questions — route them through a structured Q&A workflow with assignment and audit trail instead of a sprawling email thread, so every question and answer is logged and nothing falls through.

The honest concession, stated plainly. The source code itself does not live in the data room. A data room governs the documents around the code — the diagrams, the scan outputs, the license inventories, the registers, the reports — not the git history. The code itself typically stays in read-only repository access, a supervised review environment, or an escrow arrangement, exactly as the red-flags section described. Anyone who tells you a data room replaces a git host for source-code review is selling you something. What the room does is make everything except the raw code governed, permissioned, watermarked, and auditable — and coordinate the two audiences so their reports agree. That is the wedge, and it is a real one: Peony is used for due diligence by 6,800+ customers, and the platform's SOC 2 Type II controls, staged access, and per-audience visitor groups are built for exactly this multi-party pattern. It will not read the code for you. It will make sure the buyer's engineers and the lender's advisor are working from the same, controlled set of everything else.

How much does technical due diligence cost?

Technical due diligence scales with deal size and scope, and rather than quote figures here that could drift out of sync with the rest of the site, I will hand you to the page that owns them: our due diligence cost breakdown carries the current bands by workstream and deal size and is the canonical cost reference for this cluster. The practice frame is simple — a technical review is a mid-sized line in the diligence budget, larger than a basic cybersecurity questionnaire and usually smaller than a full quality-of-earnings engagement, and it rises with codebase complexity, the number of services and integrations, whether the target ships AI features, and whether a lender requires an independent report on top of your internal review. A lightweight pre-LOI screen costs far less than a full post-LOI deep dive with an outside firm plus a confirmatory pass. And the single most controllable lever on the bill is the same one it is for every workstream: a clean, well-structured data room on day one means reviewers spend their hours analyzing evidence instead of chasing it.

Frequently Asked Questions

We're acquiring a $40M SaaS company — should we run technical due diligence in-house or hire a third-party firm?

It depends on whether your team can staff it and whether a lender is involved. If you have senior engineers who can be pulled off roadmap work for two to four weeks and there is no external report requirement, an in-house review is credible and cheaper — your engineers know what good looks like in your stack. Hire a firm when you lack the bandwidth, when the target's domain is outside your team's expertise, when key-person risk on your own side is high, or when a lender or investment committee wants an independent opinion they can rely on. Independent providers such as Crosslake, AKF Partners, and West Monroe position around private-equity and strategic acquirers and bring benchmarking data across hundreds or thousands of prior deals. The common answer on a $40M deal with debt financing is both: an in-house conviction review plus an independent report for the lender.

Our lender requires an independent technical due diligence report — what does it cover, and who provides one lenders accept?

On leveraged software deals, lenders commonly commission an independent technical assessment before funding, because the code and engineering org are the collateral behind the loan. The report typically covers the same workstreams as a buy-side review — architecture and scalability, code quality and technical debt, security posture, infrastructure and cost-to-serve, IP and open-source licensing, and key-person risk — but written for a third party who was not in the room, with evidence and an independent opinion rather than the acquirer's internal notes. On who provides it: independent firms like Crosslake, AKF Partners, West Monroe, and Alvarez & Marsal run technology diligence practices for investors and acquirers. Be careful with the phrase "lenders accept" — no firm should be described as universally lender-accepted; the lender chooses and instructs its own advisor, so confirm with your lender who they will rely on before you commission anything.

Can one technical due diligence report satisfy both us and our lender?

Sometimes, but do not assume it. One report serving two audiences is possible only if it is scoped that way from the start and the lender agrees in advance to rely on it. The tension is real: your conviction review is written for people who were in management meetings and can read between the lines, while the lender's advisor writes for a third party who needs every finding evidenced and an independent opinion attached. A report built purely for internal conviction usually will not carry the independence a lender's credit committee wants, and a lender-commissioned report may not dig into the roadmap and integration questions you care about. The practical path is to agree scope with the lender early, decide who instructs the firm, and either commission one report both sides accept or run two coordinated workstreams off the same data room so findings do not contradict.

How long does technical due diligence take on a $40M SaaS deal?

In our experience across deals run in our data rooms, a $40M SaaS technical review runs in three phases mapped to deal stages, and the whole arc typically fits inside the exclusivity window rather than running as one sprint. A pre-LOI screen is a few days — one architecture walkthrough, a top-contract and open-source scan, a security posture check. The post-LOI deep dive is the bulk of the work, usually two to four weeks, covering full code and architecture review, management and engineer interviews, and scalability validation. A confirmatory pass before close is a few more days to re-test open items and finalize findings. The bottleneck is rarely reading code — it is getting source-code access granted and scheduling time with the target's senior engineers. Start access negotiation on day one of exclusivity, not day thirty, and the calendar takes care of itself.

What's the difference between technical due diligence, IT due diligence, cybersecurity due diligence, and AI due diligence?

They overlap but scope different targets and questions. Technical due diligence examines the code, architecture, engineering team, and scalability of a software target — it is the review you run when the product is the asset. IT due diligence is broader and applies to any target: infrastructure, applications, the vendor and license stack, and engineering practices, whether the company sells software or shoes. Cybersecurity due diligence is a security-posture deep dive — breach history, attack surface, ransomware exposure, third-party cyber risk — narrower than IT DD but deeper on security. AI due diligence diligences an AI company or AI features specifically: model provenance, training-data rights, and regulatory exposure such as the EU AI Act. Technical DD's own security workstream is narrow — secure development and code-level review — and hands the full breach-posture and attack-surface work to cybersecurity due diligence.

The target won't grant full source-code access — is that a red flag, and what's the standard alternative?

It is not automatically a red flag — most sellers refuse to hand a competing party a full copy of their source code, and that caution is normal. It becomes a red flag only when the target blocks any meaningful technical review at all. The standard alternatives are well established. Supervised read-only review lets your reviewer or an independent firm read the code on the target's machine without downloading it. A third-party escrow or a neutral firm can review the full codebase and report findings without the code ever reaching the buyer. Automated scans can run against the repository with results shared, not the source. What should worry you is not a scoped access process — it is a target that will not permit even a supervised read, refuses any scan, or cannot produce the code at all. Scope the access; do not accept a total blackout.

How much does technical due diligence cost for a software acquisition?

It scales with deal size, scope, and whether you bring an outside firm, and the honest answer is that our cost breakdown owns the numbers rather than this page. As a frame, technical review is a mid-sized line in the diligence budget — larger than a basic cybersecurity questionnaire, usually smaller than a full quality-of-earnings engagement — and it rises with codebase complexity, the number of services and integrations, whether the target ships AI features, and whether a lender requires an independent report on top of your internal review. A lightweight pre-LOI screen costs far less than a full post-LOI deep dive with an independent firm and a confirmatory pass. Rather than repeat figures that could drift out of sync, see our due diligence cost breakdown for the current bands by workstream and deal size; it is the canonical cost reference for this cluster and stays consistent across the site.

What technical red flags actually kill software deals?

A handful recur. Key-person risk is the most common — one or two senior engineers hold the architecture in their heads and nothing is documented, so the knowledge walks out if they leave post-close. A total source-code access standoff, where the target permits no supervised read and no scan, kills deals because you cannot underwrite what you cannot verify. Unvalidated scalability claims are another: the target asserts it can handle ten times the load but has no load tests, no capacity headroom, and an architecture that will not get there without a rewrite. Open-source license contamination — a copyleft license like GPL entangled in proprietary code — can force disclosure of the buyer's own source or an expensive re-architecture. Runaway technical debt, undocumented single points of failure, and a security posture with no secure-development discipline round out the list. Each is survivable if priced; each is fatal if discovered late.

One senior engineer owns the whole codebase — how do we assess key-person risk?

Treat it as one of the highest-severity findings in a software deal. Start by mapping the actual dependency: which systems only this person understands, how much is documented versus in their head, and what breaks the day they leave. Test the documentation directly — ask for architecture docs, runbooks, and onboarding materials, then check whether a second engineer can operate from them. Interview the rest of the team without the key person present to gauge how deep the bus factor goes. Then structure around it: retention packages and earn-outs tied to the key engineer staying through a transition, a knowledge-transfer plan with documentation milestones, and a realistic post-close hiring plan to build redundancy. A single owner is not a deal-killer by itself — an undocumented single owner with no retention hook and no succession plan is. The fix is contractual and operational, and it should be priced into the deal.

How should we structure the data room and code access for technical due diligence?

Structure it by workstream and by audience. Build a request-list-driven room with a folder per workstream — architecture, code quality, security, infrastructure and cost, IP and open source, team and key-person, roadmap — so reviewers find evidence instead of hunting for it. Use visitor groups to give each audience its own scoped view: your engineering team, the lender's independent advisor, and counsel each see only what they need. Put dynamic watermarks on sensitive architecture and design documents so any leak carries a forensic trail. Use page-level analytics to see which sections the lender's reviewer actually read — useful when their report lands. Route the hundred-plus technical questions through a structured Q&A workflow instead of email. The honest limit: the source code itself typically stays in read-only repository access or a supervised environment — a data room governs the documents around the code, not the git history itself.

Related Resources

• IT Due Diligence (2026) — the systems-and-infrastructure review for any target, and the 6-axis framework technical DD partitions against
• Cybersecurity Due Diligence — where technical DD's narrow SDLC security workstream hands off the full breach-posture and attack-surface work
• AI Due Diligence — model provenance, training-data rights, and EU AI Act exposure when the target ships AI features
• Due Diligence Cost Breakdown (2026) — the canonical cost page: technical review cost by workstream and deal size
• Due Diligence Data Room Checklist — the request-list structure your technical reviewers will work from
• M&A Due Diligence Process Guide — where the technical review sits in the full deal workflow
• Quality of Earnings — the financial-DD counterpart that sets the number your deal prices on
• Best Tech M&A Advisors — advisors who run technology-sector deals end to end
• Best Software M&A Advisors — sell-side and buy-side advisors specialized in software transactions
• Due Diligence Solutions — how Peony structures a multi-party diligence room
• M&A Solutions — the full deal-room pattern behind the buyer-and-lender workflow

Sources

• Bain & Company — M&A Report 2026 (AI adoption among M&A practitioners more than doubled, to 45%, n=303 senior dealmakers; one in five walked away from a deal over anticipated AI impact): bain.com
• NIST — Secure Software Development Framework (SSDF) Version 1.1, SP 800-218, February 2022 (the canonical secure-software-development reference the security workstream maps against): csrc.nist.gov
• Crosslake Technologies — Technology Due Diligence ("Your Private Equity Tech Due Diligence Advisor"; "proprietary TechIndicators®, based on data from more than 6,000 prior technology M&A transactions"): crosslaketech.com
• AKF Partners — Technical Due Diligence (five evaluation areas "Technology," "AI / ML," "Organization," "Process," "Security"; "From $5 million invested to over $1 billion"): akfpartners.com
• West Monroe — Mergers & Acquisitions (technology transaction practice, predominantly PE; diagnostic surfaces technical debt, cyber vulnerabilities, and operational inefficiencies): westmonroe.com
• Alvarez & Marsal — Software Product and Technology Diligence (buy-side and sell-side software and technology diligence practice for private-equity funds): alvarezandmarsal.com

Last updated: July 2026