Private Equity Technology Due Diligence: Clearing a SaaS Investment to Close

Situation: A Growth Story the Investors Needed to Verify

A private equity firm was preparing a significant investment in a fast-growing software company. The company sold a cloud-based video surveillance platform that tied security video to point-of-sale data for loss prevention, operational audits and sales analytics. It had about 50 employees and more than $600,000 a month in contracted recurring revenue.

Challenge: Will the Platform Scale?

The firm's investment thesis depended on the platform scaling with demand. They needed an independent view of the architecture, code quality, infrastructure, open-source use and the team behind it. They needed that before committing capital, not after.

Action: A Rated, Priced Technology Due Diligence

Don Hook led the technology due diligence. The review covered:

• The full product suite: a proprietary recording device, the cloud video application, the business intelligence tools, traffic analytics and managed services.
• Code and architecture reviews, plus on-site interviews from developers to executives.
• Infrastructure, network, database, backup and recovery, and open-source licensing.

Each risk was rated two ways: business impact (disruption hours or dollars), and the probability it would hit within 12 months of closing. Each one came with a mitigation and an order-of-magnitude cost. Key findings:

• Architecture: The early application wasn't built for large-scale deployments. Refactoring was needed to scale, with remediation potentially above $500,000.
• Backups: Finance and file server backups were kept unsecured on USB drives in the main office.
• Capacity: Hardware capacity would last 6 to 9 months at the current growth rate, with little capacity planning in place.
• Resilience: There was no tested disaster recovery or business continuity plan, and only one manufacturer supplied the recording device.
• Strengths: Development, security and QA practices were more mature than typical for a company of its size.

Result: Investment Made, and It Paid Off

The report was reviewed with the company for accuracy, then with the investors. The firm had a clear, priced view of what had to be fixed, and in what order, and went ahead with the investment.

The company grew into an industry-leading platform for fraud detection, sales analytics and audit compliance, used by national restaurant and retail brands. The investment paid off for the firm.

Why it matters today: Open-source licensing was the provenance question then. Today the same question applies to AI: where did the code, the models and the training data come from?