The Challenge
A private equity firm was evaluating an acquisition of a healthcare AI company — a target that looked compelling on revenue but carried hidden risks standard due diligence couldn't surface. Without AI-specific technical diligence, the firm risked overpaying by 15–20% for capabilities that were overstated, vendor-dependent, or concentrated in a single key person whose departure would collapse the product.
What They Built
AI4ALL Solutions evaluated the target's AI architecture, model dependencies, training data provenance, and team composition alongside a PE firm's standard M&A process, surfacing a single-engineer key-person risk and vendor-dependent capabilities that led to performance-based earnout restructuring.
AI4ALL Solutions embedded a dedicated technical AI diligence track alongside the PE firm's standard M&A process — a parallel workstream that evaluated dimensions standard financial and legal diligence cannot assess. The assessment covered the target's AI architecture design, underlying model dependencies, training data provenance and ownership, and the composition and concentration of the technical team responsible for maintaining AI capabilities.
The diligence surfaced two critical risks. First, a single engineer held responsibility for maintaining and updating the core model — a key-person concentration that would represent a material product risk if that individual departed post-close. Second, multiple AI capabilities that were presented as proprietary were found to depend significantly on third-party vendor APIs, making them more fragile and less defensible than the acquisition thesis assumed. These findings were presented to the PE firm with supporting technical evidence. Rather than killing the deal, the findings enabled a restructuring: performance-based earnouts were tied to post-close technical milestones, reducing the effective acquisition cost by 15–20% and aligning seller incentives to the delivery of genuinely proprietary capabilities.
AI Role
Critical dependency on a single engineer identified before close, allowing buyer to negotiate technical team retention guarantees.
Infrastructure
• AI architecture assessment framework (proprietary diligence methodology) • Model dependency mapping tools • Training data provenance review process • Technical team composition analysis
Integration Points
• Diligence workstream integrated with PE firm's standard M&A process timeline • Target company's AI systems, APIs, and vendor contracts reviewed for dependency mapping • Technical findings connected to deal structuring team for earnout design • Post-close milestone framework tied to diligence-identified capability gaps
