Technical AI Due Diligence — System-Level Evaluation for Risk, Scale, and Execution
Roko Labs conducts comprehensive technical due diligence of AI systems, evaluating architecture, data pipelines, model strategy, evaluation frameworks, observability, security, and the engineering traits that determine real-world performance and scalability. This assessment delivers evidence-based findings on implementation quality, operational readiness, and risk vectors that matter to engineering and investment decision-makers.
[01] DEFINITION
/ SERVICE
Built for Investors and Operators
Private Equity Firms
Validate AI-related claims during diligence, surface hidden operational and data risk, and establish a fact-based view of AI maturity across portfolio companies. Our assessment supports underwriting, IC discussions, and post-close planning.
Portfolio Companies
Bring structure to fragmented AI usage. Reduce exposure, control AI spend, improve output reliability, and establish a standard operating model that supports scale.
[03] The hidden risks of AI
/ GOVERNANCE
Expose hidden risk and build confidence for execution, investment, or integration decisions.
AI tools deployed unevenly across teams and functions
Inconsistent or unmeasured AI outputs
Sensitive or proprietary data shared without adequate controls
Dozens of pilots with limited operational impact
Escalating model, infrastructure, and vendor costs
No clear ownership, standards, or accountability
[04] WHAT SETS US APART
/ IMPLEMENTATION
Comprehensive AI Implementation Due Diligence
We assess how AI is actually being used and operated—not how it is described in presentations.
AI Systems and Usage Inventory
A complete view of where AI is deployed, by whom, for what purpose, and with what data.
Model and Vendor Risk Assessment
Evaluation of model choices, third-party dependencies, and operational or contractual risk.
Cost and Efficiency Analysis
Identification of spend drivers, redundancies, and optimization opportunities across tooling and infrastructure.
Output Quality and Performance Review
Review of how AI outputs are evaluated, monitored, and improved over time.
Adoption and Standardization Score
Assessment of consistency, enablement, and governance across teams and functions.
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[05] OUR IMPACT
/FRAMEWORK
Purpose-Built for AI Due Diligence
Focused on AI risk, Cost, and Operational Maturity
Cross-Functional Expertise Across Technology, Data, Security, and Operations
Repeatable Framework Suitable for Portfolio-Wide Benchmarking
Outputs Designed for Immediate Remediation and Decision-Making
[07 ] OUR APPROACH
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A Structured, Time-Bound Engagement
[05] CASE STUDIES
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