Technology Due Diligence in Mergers and Acquisitions: The 2026 Buyer Playbook

Technology due diligence in mergers and acquisitions re-trades 30 to 40 percent of software-heavy deals, according to the PitchBook 2025 Software M&A Report, with typical price reductions of 5 to 25 percent when buyers surface material findings in code quality, cybersecurity, IP ownership, or third-party license exposure. On a 50 million dollar SaaS acquisition, a single open security vulnerability cluster combined with an unaddressed open-source licensing risk routinely costs the seller 2 to 4 million dollars in adjusted purchase price, plus an indemnity escrow held for 18 to 36 months. The tech file has become the second-most-priced workstream after quality of earnings, and for software and AI-product companies it is now the first.

What Technology Due Diligence Actually Means in an M&A Deal

Technology due diligence is the workstream inside the broader operational diligence phase where the buyer (and a specialist tech DD firm, the buyer’s lender, and often the buyer’s cyber insurance carrier) verify that the target company’s software assets, code base, infrastructure, security posture, data practices, and intellectual property are what the seller represented in the confidential information memorandum. It runs in parallel with quality of earnings on the financial side and commercial diligence on the customer side, and it produces a written findings memo that drives three decisions: whether the deal closes at the agreed price, whether the purchase agreement needs new indemnities or escrows for technical exposure, and whether the buyer needs to budget remediation capital for year one.

For asset-light services businesses the workstream is light, often a one to two week pass covering the software inventory and the basics of cybersecurity hygiene. For software-product companies, SaaS platforms, fintech, healthtech, and any business with proprietary code as a primary revenue driver, the workstream runs 4 to 6 weeks with a named team of code reviewers, security testers, architecture reviewers, and IP counsel. Costs scale with scope: West Monroe Partners, Crosslake Technologies, OpenView Tech DD, Endava, and Code Climate quote 25,000 to 150,000 dollars for a standard mid-market tech DD, and 200,000 dollars and up for a complex software-product engagement. The output feeds the lender’s underwriting file, the cyber insurance binder at closing, and the disclosure schedules in the purchase agreement. The seller who has not prepared loses control of the conversation.

The Ten Workstreams Inside a Modern Tech Due Diligence

Software Inventory and License Position

Current state: Buyers request a complete inventory of every business-critical application in use, broken out by category (CRM, ERP, accounting, dev tooling, communications, security, HR), with vendor, contract term, renewal date, seat count, per-seat cost, and contractual exit terms. The inventory should match the general ledger software expense category line by line. Sellers often discover that 15 to 25 percent of their seat-based subscriptions are inactive.

Target state: Every application has a current contract, a single named owner inside the business, a documented renewal date with at least 60 days of lead time, and a documented exit cost. Vendor concentration risk is mapped, with no single SaaS vendor controlling a system that cannot be replaced inside 6 months without operational disruption.

Impact on outcome: A messy inventory with auto-renewing contracts at above-market rates becomes a one-time integration cost that the buyer prices into the offer as a year-one capital fix, typically 100,000 to 400,000 dollars for a 50 to 200 employee business.

Proprietary Code Repository and Code Quality

Current state: For any business with proprietary code, the buyer requests read access to the source control repository (GitHub, GitLab, Bitbucket, Azure DevOps) under NDA, typically through a virtual data room or a dedicated diligence GitHub organization. The reviewer runs static analysis with tools like SonarQube, Code Climate, Snyk Code, or Semgrep across the full repository and pulls metrics on cyclomatic complexity, code smells, duplicated lines, test coverage, and known vulnerability patterns. The reviewer also samples 5 to 10 percent of the code by hand to confirm the static analysis output matches the actual code style.

Target state: Test coverage above 70 percent on the core business logic, cyclomatic complexity averages below 10 per function on critical paths, technical debt ratio (as measured by SonarQube methodology) below 5 percent, documented commit history showing more than one active contributor in the last 12 months, and a clear branching and pull request workflow with mandatory code review on the main branch.

Impact on outcome: A 50 percent or higher test coverage gap on the core revenue-generating module is a standard 0.5x to 1.0x EBITDA multiple compression in software M&A pricing, per Crosslake Technologies 2025 tech DD benchmark data. A documented “bus factor” of one (only the founder or a single engineer can ship to production) is a deal-killer for institutional buyers because the buyer cannot underwrite the integration risk.

Architecture and Scalability Headroom

Current state: The buyer requests an architecture diagram showing every production service, the cloud provider for each (AWS, Azure, Google Cloud, on-premise, hybrid), the data flow between services, the authentication and authorization boundaries, and the single points of failure. The reviewer evaluates the current load against the documented scaling ceiling, the cost per transaction at current volume versus the cost per transaction at 3x and 10x volume, and the vendor concentration risk in cloud spend.

Target state: Documented scaling headroom of at least 5x current peak load without architectural rework, no single point of failure in the request path for the primary product, multi-region or multi-zone redundancy for any system with a recovery time objective under 4 hours, and cloud vendor concentration limited so the business is not commercially captive to a single hyperscaler beyond reasonable industry norms.

Impact on outcome: A buyer modeling a 3-year hold on a SaaS asset will price scalability headroom directly into the growth case. A platform that requires a full re-platform between year 1 and year 2 to support the buyer’s growth plan is priced as a 1.0 to 2.0 million dollar capital project plus 6 to 9 months of revenue delay, both of which compress the offer.

Cybersecurity Posture and Audit History

Current state: The buyer requests the most recent penetration test report (ideally external, third-party, with a remediation tracker showing all critical and high findings closed), the SOC 2 Type II report (the Type II covers the operating effectiveness of controls over a 6 to 12 month observation window, not just the design), the ISO 27001 certification if applicable, the cyber incident log for the last 36 months, the data breach notification history, and a current vulnerability scan output. For healthcare targets, HITRUST CSF is added. For payments targets, PCI DSS Attestation of Compliance is added.

Target state: A clean SOC 2 Type II from a top-100 audit firm covering the trailing 12 months, no open critical or high vulnerabilities older than 30 days, no unreported security incidents in the last 36 months, a documented incident response plan tested at least annually, and multi-factor authentication enforced on every production system and every administrative account.

Impact on outcome: The absence of a SOC 2 Type II report on a B2B SaaS company selling into mid-market or enterprise customers is now a standard 5 to 10 percent purchase price reduction, because the buyer has to fund the audit process post-close (typically 75,000 to 200,000 dollars and 12 to 18 months of clock) before the company can credibly close upmarket deals. A reported but unresolved data breach in the last 24 months can trigger a 10 to 25 percent purchase price holdback held in escrow for the duration of the applicable state attorney general notification windows.

Data Privacy and Regulatory Compliance

Current state: The buyer maps the company’s data processing activities against every regulatory regime that applies based on the customer footprint and the data categories collected. For EU customer data, GDPR applies and the buyer requests the Record of Processing Activities, the Data Protection Impact Assessments for high-risk processing, and the Standard Contractual Clauses for any cross-border transfer. For California customer data, CCPA and CPRA apply. For healthcare data, HIPAA applies and the buyer requests the Business Associate Agreements with every downstream vendor. For payment data, PCI DSS applies. For under-13 data, COPPA applies. For EU AI Act in-scope systems (high-risk AI deployments going into effect on a phased schedule through 2027), the buyer requests the conformity assessment documentation.

Target state: A current data map covering every category of personal data, every storage location, every downstream processor, and every cross-border transfer mechanism. A current retention policy with documented enforcement (data actually gets deleted on the schedule the policy says, not just on paper). A privacy program owner with documented training. A breach notification playbook with named outside counsel relationships in every jurisdiction where notification can be required.

Impact on outcome: A GDPR gap on a company with material EU revenue is priced as a remediation project (typically 150,000 to 500,000 dollars and 9 to 12 months) plus a regulatory risk reserve based on the maximum fine exposure (up to 4 percent of global annual revenue or 20 million euros, whichever is higher, per GDPR Article 83). A HIPAA gap on a healthcare data processor is a deal-blocking issue for many strategic buyers because the buyer’s existing covered entity contracts include flow-down compliance requirements.

Third-Party Dependencies and Open Source Hygiene

Current state: The buyer runs a software composition analysis (SCA) tool like Snyk, Black Duck, FOSSA, or Mend across the entire code base to enumerate every third-party open source dependency, the license attached to each, the known vulnerability status, and the upgrade path. The reviewer pays special attention to copyleft license exposure, where a GPL-licensed dependency linked into proprietary code can in theory require the company to release its proprietary source code under the same license. The reviewer also enumerates every commercial SaaS dependency in the production path and confirms whether escrow agreements exist for any vendor whose failure would cause a service outage.

Target state: Every dependency has a documented license, no GPL or AGPL dependencies linked into the proprietary code base in a way that triggers the copyleft obligation, no critical-severity vulnerabilities older than 30 days, no high-severity vulnerabilities older than 90 days, and source code escrow agreements in place for any single-vendor dependency that creates an existential risk to service delivery.

Impact on outcome: A confirmed AGPL contamination in a proprietary code base is a standard 5 to 15 percent purchase price reduction or, in severe cases, a deal-kill, because the remediation requires identifying every code path that touches the contaminated module and rewriting it under a compatible license. A single critical CVE in a widely-used dependency that has been open for more than 6 months is a competence signal that buyers price into the offer alongside other security findings.

Intellectual Property Ownership and Assignment

Current state: The buyer’s IP counsel requests every employee invention assignment agreement, every contractor master services agreement and statement of work, every consulting agreement, and every founder IP assignment from before the company was formed. The counsel verifies that every person who touched the code base assigned the work product to the company in writing, with sufficient specificity to survive challenge in the relevant jurisdiction. For open source contributions made by employees on company time using company resources, the counsel verifies that the contributions were authorized under a written open source contribution policy.

Target state: Every current and former employee has signed an invention assignment agreement covering the full period of employment. Every contractor has a written assignment in the master agreement, not just in individual statements of work. Every founder has a documented assignment of pre-incorporation IP. Every open source contribution by an employee is logged and authorized.

Impact on outcome: A missing employee IP assignment for a core engineer is one of the few findings that can stop a deal outright, because the buyer cannot verify clear title to the code base. The remediation requires reaching the former employee and obtaining a current assignment, which is sometimes impossible and almost always expensive when the former employee understands the control. A 2025 ISACA review of mid-market software acquisitions found IP ownership defects were cited in 11 percent of broken deals.

Technical Debt and Deprecated Technology

Current state: The reviewer pulls a full inventory of language versions, framework versions, runtime versions, and database versions in production. The reviewer flags any technology that is past end-of-life support (Python 2, jQuery 1.x, AngularJS 1.x, Node.js versions below current LTS, Java 8 in security-sensitive contexts, unsupported database major versions). The reviewer also pulls the outstanding security patch backlog and the deferred upgrade list from the engineering team’s own tracker.

Target state: No production system running an end-of-life major version of a language, framework, or runtime. No deferred security patches above the standard SLA (typically 7 days for critical, 30 days for high, 90 days for medium). Technical debt items prioritized in a written backlog with realistic effort estimates and target resolution dates.

Impact on outcome: A platform running on Python 2 or AngularJS 1.x in 2026 is priced as a re-platform project. For a mid-sized SaaS code base, the buyer typically budgets 500,000 to 2 million dollars and 12 to 24 months for the migration, and that number comes directly off the offer price. The seller who upgrades on schedule preserves both the multiple and the optionality.

DevOps and Operational Maturity

Current state: The reviewer requests the CI/CD configuration files, deployment frequency metrics, mean time to recovery from the last 12 months of production incidents, rollback capability documentation, the observability stack (Datadog, New Relic, Grafana, Splunk, Honeycomb, or equivalent), and the on-call rotation and incident response playbook. The reviewer aligns the metrics against the four DORA metrics (deployment frequency, lead time for changes, change failure rate, mean time to recovery) that the buyer’s portfolio operations team uses as a maturity yardstick.

Target state: Automated CI/CD pipeline with mandatory test gates, deployment frequency at least weekly for the primary product, change failure rate under 15 percent, mean time to recovery under 4 hours for production incidents, observability covering every production service with documented service-level objectives, and a tested incident response plan with named roles.

Impact on outcome: DevOps maturity is one of the workstreams where the buyer’s investment in remediation has the highest return, so a weak DevOps file is often priced as a year-one investment rather than a purchase price reduction. A strong DevOps file, by contrast, supports a premium multiple because it directly de-risks the buyer’s integration plan.

AI and Machine Learning Specific Diligence

Current state: For any company with material AI or machine learning in the product, the buyer adds a workstream covering model provenance, training data ownership, model bias and fairness testing (especially for systems making decisions about hiring, credit, housing, healthcare, or insurance), drift monitoring, and regulatory compliance with the EU AI Act for any in-scope high-risk system. The reviewer pays specific attention to the EU AI Act conformity assessment requirements coming into effect on a phased schedule through 2026 and 2027.

Target state: A model registry documenting every production model with version, training date, training data sources and licenses, intended use, performance metrics, and bias test results. A documented model governance process with named owners. A regulatory mapping for every applicable jurisdiction.

Impact on outcome: AI-specific findings are still a developing pricing category, but the early pattern from PitchBook 2025 software M&A data is that buyers will deduct 10 to 20 percent of enterprise value for material model provenance gaps on companies where AI is a primary revenue driver.

Worked Example: How One SaaS Deal Lost 4 Percent on Tech Findings

Consider a fictional but representative scenario. Northwind Analytics is a 120-employee B2B SaaS company selling a marketing analytics platform to mid-market e-commerce brands. Revenue is 22 million dollars, adjusted EBITDA is 5 million, and the owner takes the business to market expecting a 10x EBITDA multiple based on PitchBook 2025 Q4 software M&A data for vertical SaaS with greater than 30 percent revenue growth and greater than 110 percent net revenue retention.

The buyer is a software-focused private equity firm with 22 prior SaaS investments. The buyer signs an LOI at 50 million dollars enterprise value (10.0x). Confirmatory diligence opens. Crosslake Technologies is engaged for tech DD on a 110,000 dollar fixed-fee scope covering code review, security testing, IP and license audit, and architecture review. The findings memo lands in week 5.

Finding 1: A penetration test run by Crosslake’s security team identifies 14 high-severity vulnerabilities in the customer-facing application, including one authentication bypass and three SQL injection patterns in legacy reporting endpoints. Estimated remediation cost is 800,000 dollars over 4 months with a senior security engineer and two contractors.

Finding 2: Software composition analysis identifies an AGPL-licensed graph rendering library deeply integrated into the dashboard module. The license obligation is to release the calling code under AGPL, which would expose the proprietary dashboard code. Remediation is to replace the library with an MIT-licensed alternative and refactor the integration. Estimated cost is 400,000 dollars over 6 months.

Finding 3: No SOC 2 Type II report. The company has a SOC 2 Type I from 18 months ago but has not completed an observation period for a Type II. Two of the buyer’s planned post-close enterprise customer expansions require a current Type II as a contractual condition.

Finding 4: Three of the original engineering team did not sign invention assignment agreements until 6 months after they started writing code. Two have left the company. IP counsel recommends obtaining current assignments from the two former employees as a closing condition, with a contingent indemnity if either refuses.

The buyer’s repricing letter does four things. It deducts 1.2 million dollars from purchase price to cover the security remediation and the open-source license refactor. It requires the seller to fund a 500,000 dollar escrow against the SOC 2 timeline, released when the Type II report is issued. It requires the seller to obtain the missing IP assignments before close, with a 300,000 dollar indemnity holdback if either former employee will not sign. And it preserves the 10.0x multiple but applies the deductions, bringing the gross enterprise value to 48 million dollars, with the seller funding the remediation escrow.

The seller takes home 48 million dollars at close instead of 50 million, with 800,000 dollars in escrows that are released over 18 months on remediation milestones. The 2 million dollar gap is the cost of a tech file the seller never audited before going to market. The deal closes because the findings were priced cleanly and addressed in the agreement structure, not because they were avoided.

Common Mistakes Sellers Make Before and During Tech Diligence

Treating the Code Repository as an Engineering Tool Instead of a Diligence Artifact

The git history is one of the first things a tech DD reviewer reads, and a code base with no meaningful commit messages, no branching discipline, no pull request reviews, and a “bus factor” of one tells the buyer that the engineering organization is fragile. The fix is to enforce minimum standards on the main branch (mandatory pull requests, mandatory review, mandatory CI pass) at least 12 months before going to market, so the recent history of the repository tells the story the seller wants the buyer to read.

Assuming the Penetration Test From Three Years Ago Still Counts

A penetration test report ages quickly. A 36-month-old pen test on a code base that has shipped 200 deployments since the test is functionally worthless to a buyer, because none of the tested code paths are guaranteed to still exist. The standard buyer expectation is a current pen test (within 12 months) from a recognized third-party firm, with a remediation tracker showing every critical and high finding closed. The fix is to schedule an external pen test 6 to 9 months before going to market and clear the findings before the data room opens.

Confusing SOC 2 Type I With Type II

A SOC 2 Type I report is a point-in-time attestation that controls are designed correctly. A SOC 2 Type II report is an observation-period attestation (typically 6 to 12 months) that the controls actually operated effectively. Enterprise buyers and enterprise customers want Type II. Sellers who show up to diligence with only a Type I are effectively telling the buyer that the audit clock has not started. The fix is to begin the Type II observation period 18 to 24 months before going to market.

Letting Open Source Compliance Drift

Open source dependencies enter a code base through every developer’s package manager call, and most companies have no central oversight of what gets pulled in. By the time the buyer’s SCA tool runs, the code base typically contains hundreds of dependencies that the engineering team did not consciously choose. The fix is to run an SCA tool internally on a weekly schedule, block new copyleft dependencies in CI, and maintain a current Software Bill of Materials for every production release.

Letting Founder Code Sit Without IP Assignment

In almost every founder-led company, the founders wrote the original code base before the company was formed, often using personal hardware, personal cloud accounts, and personal open source contributions. The IP needs to be explicitly assigned to the company at incorporation through a written assignment, and that assignment needs to survive in the records. The fix is to confirm the assignment paperwork is current and in the corporate records book before the data room opens, and to obtain new assignments retroactively for any gap period.

Treating the AI Workstream as Marketing Copy Instead of a Diligence File

Many companies describe themselves as “AI-powered” in the CIM and then arrive at diligence with no model registry, no training data licenses, no bias test results, and no documented model governance. For a buyer who is pricing the AI capability into the multiple, the absence of an AI diligence file is worse than the absence of the capability itself, because it suggests the AI narrative is positioning rather than substance. The fix is to maintain a real model registry from the day the first production model ships, not to construct one in the weeks before the LOI.

The Tech Diligence Timeline From LOI to Close

Below is the typical sequence on a 6-week tech DD engagement for a mid-market software acquisition, parallel to the 90-day overall confirmatory diligence window.

The pattern that wins deals is starting tech DD preparation 12 to 18 months before going to market, not 12 days before the LOI is signed. A SOC 2 Type II observation period alone takes 6 to 12 months. A copyleft license refactor takes 4 to 6 months. An IP assignment gap with a former employee can take weeks or months to resolve. None of these clocks compress under deal pressure.

A Pre-Market Tech Self-Diligence Checklist

Owners who want to control the tech narrative before a buyer ever sees the file should run this self-diligence sequence in the order shown.

Step 1. Pull a complete software inventory and reconcile it to the general ledger software expense line. Cancel inactive subscriptions. Negotiate any contracts within 12 months of renewal. Document a single named owner for every business-critical application.

Step 2. Run a software composition analysis tool (Snyk, Black Duck, FOSSA, or Mend) against the production code base. Identify and replace any GPL or AGPL dependencies that are linked into the proprietary code. Clear every critical CVE older than 30 days and every high CVE older than 90 days.

Step 3. Run a static analysis tool (SonarQube, Code Climate, or Semgrep) against the full repository. Set internal targets for test coverage on the core business logic, cyclomatic complexity, and technical debt ratio. Close the gap to the targets before the data room opens.

Step 4. Commission an external penetration test from a recognized third-party firm within 12 months of going to market. Close every critical and high finding before the report is shared in the data room.

Step 5. Begin the SOC 2 Type II observation period 18 to 24 months before going to market if the buyer pool includes enterprise-focused acquirers. Engage a top-100 audit firm and document control evidence continuously through the observation window.

Step 6. Audit every employee, contractor, and founder IP assignment. Obtain current assignments for any gap period, including from former employees. Update the corporate records book.

Step 7. Build a current architecture diagram with documented scaling headroom, single points of failure, and cloud vendor concentration. Run a load test to verify the documented headroom is real.

Step 8. For AI and machine learning products, build a model registry covering every production model with training data sources and licenses, intended use, performance metrics, and bias test results. Map the model footprint against the EU AI Act and any other applicable regulatory regime.

Step 9. Engage an independent tech DD firm (West Monroe Partners, Crosslake Technologies, OpenView Tech DD, Endava, or equivalent) for a mock buyer-side audit 9 to 12 months before going to market. Treat the findings as a punch list, not a report card.

How CT Acquisitions Approaches Tech Due Diligence on Sell-Side Engagements

CT Acquisitions is paid by buyers, not by sellers. That structure changes the incentive on tech diligence. A traditional sell-side broker is paid as a percentage of closing price, which can create pressure to downplay technical findings or rush past them to keep the deal moving. A buyer-paid intermediary has the opposite incentive: identify the technical risk early, price it honestly, and structure the deal so the buyer closes with eyes open and the seller does not face surprise repricing in week 5.

When a seller engages CT Acquisitions, the tech pre-screen happens before the business is ever shown to a buyer. The team requests the software inventory, the static analysis output, the most recent penetration test, the SOC 2 status, the IP assignment file, and the dependency audit, and produces a one-page tech risk memo with three categories: items that buyers will reward (clean code metrics, current Type II report, complete IP file), items that buyers will price into the offer (deferred upgrades, missing pen test, open critical vulnerabilities), and items that will block the deal entirely (AGPL contamination in proprietary code, missing founder IP assignment, undisclosed breach in the last 36 months).

Frequently Asked Questions

How much does tech due diligence cost on a typical mid-market software deal?

Standard scope tech DD on a mid-market deal runs 25,000 to 150,000 dollars, with West Monroe Partners, Crosslake Technologies, OpenView Tech DD, Endava, and Code Climate quoting in that band depending on code base size, regulatory complexity, and AI footprint. A complex software-product engagement with full static code analysis, an independent penetration test, an open source license audit, and an IP assignment audit runs 200,000 dollars and up. The buyer pays for the engagement, but the cost is sometimes shared on smaller deals through a closing-cost split.

How far back do buyers look in tech due diligence?

Code repository history goes back to the beginning of the repository, because the buyer needs to verify continuous IP ownership. SOC 2 Type II observation periods cover the most recent 6 to 12 months. Penetration test reports are expected within the last 12 months. Cyber incident logs are typically requested for the last 36 months. Cloud spend history is requested for the last 24 months to verify the scaling cost curve. IP assignment paperwork is requested for every person who ever touched the code base, with no time limit.

Can a seller cure a SOC 2 gap during diligence?

No. The SOC 2 Type II observation period is 6 to 12 months, plus another 4 to 8 weeks for the audit firm to issue the report after the observation window closes. A seller who arrives at diligence without a current Type II report cannot close that gap inside a standard 90-day diligence window. The practical answers are either to begin the observation period 18 to 24 months before going to market, or to accept a price reduction or an escrow holdback tied to the future report issuance.

What is the deal impact of a discovered data breach during diligence?

The impact depends on whether the breach was previously disclosed, the regulatory notification status, and the type of data exposed. An undisclosed breach involving personal data subject to state attorney general notification windows typically triggers a 10 to 25 percent purchase price holdback in escrow for the duration of the notification windows, plus an indemnity for any regulatory penalty or class action liability that emerges post-close. A breach involving regulated data (HIPAA, PCI, GDPR Article 9 special categories) can kill the deal entirely or trigger a complete repricing of the multiple.

How does technology due diligence apply to asset deals versus stock deals?

The technical work is the same in both deal structures, but the legal exposure differs. In a stock deal, the buyer inherits the full IP estate, the full security history, and the full regulatory exposure, so the indemnities are tighter and the survival periods longer. In an asset deal, the buyer takes only the listed assets, but successor liability under data privacy statutes (especially CCPA, CPRA, and certain state breach notification laws) often pulls the buyer back in for legacy exposure. Asset deals do not solve the IP assignment problem either, because the assignment chain still has to be clean for the assigned code.

Does tech due diligence apply to non-software companies?

Yes, although the scope is narrower. Every operating business in 2026 runs on software, has a cybersecurity exposure, and processes regulated data. For an asset-light services business, the tech workstream is typically a one to two week pass covering the software inventory, the cybersecurity hygiene basics (multi-factor authentication, endpoint protection, backup posture), the data privacy compliance for any customer data collected, and the third-party SaaS vendor concentration. The cost is at the low end of the range (25,000 to 50,000 dollars). The findings still move price.

What to Do Next

Owners who run a software, AI, or technology-enabled business and expect to sell in the next 24 to 36 months should treat the tech file the same way they treat the financial statements: as a primary diligence artifact that sets price, not as a back-office engineering task. The SOC 2 cycle, the IP assignment audit, and the open source license cleanup all run on multi-month or multi-year clocks that cannot be compressed in the weeks before listing. The owners who start early preserve both the multiple and the deal certainty.

CT Acquisitions runs a free pre-market tech pre-screen for owners considering a sale in any technology-enabled industry, including SaaS, fintech, healthtech, AI products, IT services, MSPs, and software-enabled physical product businesses. The pre-screen produces a one-page memo identifying the specific findings a buyer-side tech DD firm will surface, with a remediation cost estimate and timeline for each.