AI Due Diligence Framework for Investors
5 pillars
Not disclosed
Not disclosed
Ecliptic's investment team lacked deep technical AI expertise to properly evaluate a target from a technology perspective, and had no systematic framework for assessing AI-specific maturity and risk.
PressW applied its 5 Pillars of AI Competency framework, covering data quality, governance, modeling, testing, and scalability, conducted direct technical interviews with the target, and delivered a findings report with positive and negative technical flags.
PressW met with the Ecliptic team to understand investment criteria, generated structured questions from the 5-pillar framework, interviewed the target healthcare company's technical team, and distilled the findings into a comprehensive report.
Private equity and venture capital firms evaluating AI-focused investments that lack internal technical AI expertise and want a repeatable diligence framework.
5-Pillar AI Competency Assessment
Direct Technical Interviews
Informed the Investment Decision
Bryson Greenwood
Ecliptic Capital used a structured 5-pillar AI competency framework covering data quality, governance, modeling, testing and scalability. The experts generated assessment questions from that framework, interviewed the target healthcare company's technical team directly, and distilled the findings into a report with positive and negative technical flags.
The 5 Pillars of AI Competency framework: data quality, governance, modeling, testing and scalability. It was applied as a structured interview and assessment process rather than a software build, so no specific models were involved.
The target was assessed across all five competency pillars, direct technical interviews with the target's engineering team surfaced real flags, and the findings report gave the deal team a technically grounded view of the opportunity to inform the investment decision.
The record does not state a timeline. The engagement covered a criteria-setting session with the investment team, structured question generation, technical interviews with the target, and a written findings report.
Private equity and venture capital firms evaluating AI-focused investments that lack internal technical AI expertise and want a repeatable diligence framework.
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