Pre-Close Diligence

Diligence that connects to purchase price, not just a checklist

Most technology diligence tells you what a company has. Ours tells you what it's worth, what it costs to fix, and what the value-creation thesis can actually support, so the deal team underwrites with confidence and the 100-day plan starts before close, not after.

Our diligence team has completed more than 300 buy-side and sell-side engagements. Alongside the advisory team, our bench includes data architects, AI practitioners, and cybersecurity specialists who build what they assess. That technical depth produces findings that are more precise, more financially grounded, and harder for a seller to reframe.

Impact Story

A PE Firm Got the Tech Validation It Needed to Underwrite a 3x Growth Thesis. In Two Weeks.

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How we help

Most technology diligence teams understand transactions. Ours understands the technology underneath them. That distinction matters in three specific ways.

Findings tied to financial impact. Every observation connects to a number: remediation cost, integration cost, revenue risk, or value-creation opportunity. The deal team gets a clear picture of what the current state costs to maintain, what it costs to fix, and what it can support for the thesis.

Technical claims that hold up to scrutiny. AI capabilities, proprietary software value, and data infrastructure quality are increasingly central to deal theses and deal pricing. Our practitioners can validate or challenge those claims at a technical level that general advisors cannot.

A direct line to the hold period. The diligence and the hold period execution are separate engagements. But when the same team understands both sides, the findings don't get lost in translation. The value-creation plan that comes out of diligence is one the hold period team can execute.

What we assess

AI Diligence

We assess the target's AI posture from two angles: what it can do with AI, and what AI could do to it. For capability, we evaluate data readiness, infrastructure, talent, and organizational maturity against the use cases that affect the thesis. For risk, we assess whether AI presents a structural threat to the target's product, service, or competitive position, and how quickly that risk could materialize. The output is a clear view of AI as both an opportunity to capture and a risk to price.

Technology Diligence

We assess the full technology landscape, including architecture, infrastructure, technical debt, team depth, and scalability, against what the deal requires. AI and data maturity are standard scope: we evaluate whether the capability behind the thesis is real, whether the data foundation supports it, and whether it holds up without the consultant who built it. Cyber risk is assessed the same way, quantified as a financial exposure rather than a checklist finding.

Every observation connects to the deal model: what the current state can carry, what it costs to remediate, and what integration or carveout complexity looks like before the term sheet is signed.

Proprietary Software Diligence / Proprietary Product Due Diligence

For technology-enabled companies, the software platform is often the most valuable and most misunderstood asset in the deal. We assess the codebase for quality, maintainability, architectural dependencies, and licensing risk, connecting every finding to what it means for the deal:

Can the platform scale to support the revenue thesis?
What does the DevOps organization behind it look like, and is it built to last beyond the current team?
Where is the differentiated capability, and is it defensible?

The output goes beyond what financial diligence surfaces: a clear picture of integration complexity, hold period investment requirements, and whether the technology can carry the commercial thesis to exit.

Operational Due Diligence

Cost structure, process efficiency, and vendor dependencies determine whether the value-creation thesis is executable and what it will take to get there. We assess SG&A against sector benchmarks, evaluate procurement and vendor relationships for concentration risk and savings opportunity, and identify where manual processes or functional gaps constrain hold period performance.

The output is a quantified picture of operational risk and opportunity before close: what it costs to fix, what it unlocks, and where the hold period work should start.

How we work

Every engagement is scoped to the deal. A technology-enabled PortCo with significant proprietary software gets different coverage than a services business running on packaged applications. A compressed timeline gets a different structure than a full buy-side process.

We work with the deal team before the engagement starts to understand the thesis, what's already known, and where the real risk lives. The engagement is built around that, not around a standard template applied regardless of what the deal requires.

Pre-Close Diligence FAQs

What separates a "red flag" finding in technology diligence from a "yellow flag" finding?

A red flag is a finding serious enough to change price or deal terms before signing, typically a structural risk such as unmaintained core systems, a single-vendor lock-in on a contract nearing renewal, or a key-person dependency where the one person who understands the platform is not staying on. A yellow flag does not block the deal but changes what the 100-day plan needs to prioritize, such as cloud spend running well above benchmark or a team structure that will not hold up under the growth the thesis assumes. Resultant's diligence practice is built around this exact distinction: every finding connects to a number, remediation cost, integration cost, or revenue risk, which is what determines whether it becomes a price adjustment or a 100-day priority instead of a general concern.

How do you know if a target's AI capability is a genuine competitive moat or just a feature competitors can copy quickly?

The test is whether the capability depends on proprietary data or a defensible market position a competitor cannot easily replicate, rather than on a model or tool equally available to everyone. A feature built on a widely accessible model, with no differentiated data behind it, is a commodity capability no matter how impressive the demo looks. Practitioners who build AI systems themselves, rather than advisors who only evaluate capability claims on paper, are positioned to tell a durable advantage from a feature any competitor with the same tools could reproduce.

How much technical debt is normal to find during diligence, and when does it become a deal-breaker instead of a fixable finding?

Some technical debt is normal in nearly every target and does not change the outcome of a deal on its own. It becomes a deal-breaker when it is tied to a single person's undocumented knowledge, a vendor relationship with no viable alternative, or an architecture that cannot scale to the volume the thesis assumes, since these are forms of debt that cannot simply be budgeted for and fixed after close. Answering this requires evaluating the codebase and the DevOps organization behind it directly, not a general advisor's opinion, since only that level of technical depth can tell whether the platform holds up once the team that built it moves on.

Why is quantifying cybersecurity risk in dollar terms replacing the old red/yellow/green rating during technology diligence?

A red, yellow, or green rating tells a deal team that a risk exists without telling them what it costs, which makes the finding easy to acknowledge and easy to set aside once the deal moves forward. Translating a vulnerability or compliance gap into an estimated remediation cost and potential exposure gives the deal team a number that can actually be negotiated into price or deal structure. This is the standard Resultant holds cyber findings to as well: cyber risk gets quantified as a financial exposure rather than logged as a checklist item, which is what lets it be negotiated the same way any other technical finding is.

What operational red flags during diligence signal that a value-creation thesis will not be executable?

The clearest signals are cost structure running well outside sector benchmarks, vendor concentration with no realistic alternative, and manual processes that will not scale with the growth the thesis assumes. Each of these determines not just whether the thesis is directionally sound, but whether it can be executed on the timeline the deal assumes. Resultant's operational diligence is built to produce exactly this kind of quantified picture before close, what each issue costs to fix, what it unlocks, and where hold-period work needs to start first, rather than a general narrative about operational risk.

Why do some technology diligence findings never make it into the actual deal price?

A finding fails to move price when it is presented as a general risk observation rather than a number, since a deal team cannot negotiate against a concern with no dollar figure or timeline attached to it. Findings that connect directly to remediation cost, integration cost, or revenue risk get reflected in the model; findings that describe a problem without quantifying it tend to get acknowledged and then set aside once negotiations move forward. Resultant's diligence team has built this discipline across more than 300 buy-side and sell-side engagements, which is also why findings built this way are harder for a seller to reframe once they are on the table.

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Pre-Close Diligence

How we help