Est. 2006
Est. 2006
We assess the end-to-end system architecture of AI SaaS platforms, including infrastructure choices, dependencies, and deployment patterns. Our review identifies architectural risks, technical debt, single points of failure, and gaps between claimed and actual implementations ensuring the system can support real users at scale.
We analyze how AI models are selected, trained, fine-tuned, and deployed. This includes reliance on third-party models, custom IP, retraining strategies, performance monitoring, and cost exposure. Our goal is to determine whether the AI is a true differentiator or a fragile dependency.
We map data flows across the platform, examining ingestion, storage, processing, and retention. This helps uncover security risks, privacy exposure, and compliance gaps. We assess how customer data, training data, and model outputs are protected and whether controls are aligned with enterprise and regulatory expectations.
We evaluate whether the product can scale reliably across users, workloads, and geographies. This includes infrastructure elasticity, cost efficiency, observability, failure handling, and operational processes. Our review highlights risks that often only surface after rapid growth or enterprise adoption.
Whether you’re investing, acquiring, or scaling an AI SaaS product, our due diligence gives you the clarity to act with confidence. We translate technical complexity into actionable insight — so you understand the real risks, real value, and real readiness of the system.
Make informed decisions with a clear view of real AI risk and readiness.
This service is designed for investors, acquirers, private equity firms, and enterprise buyers evaluating AI SaaS products. It’s also used by founders preparing for fundraising, enterprise sales, or acquisition to identify and remediate risks before scrutiny begins.
Traditional diligence focuses on general software quality and infrastructure. Our approach goes deeper into AI-specific risk including model dependency, data governance, scalability of inference and training, regulatory exposure, and whether the AI is genuinely defensible or easily replicable.
We do not require full source code access in most cases. Our assessments are based on architecture reviews, technical documentation, system walkthroughs, and targeted validation sessions. When deeper access is needed, reviews are structured to protect IP and confidentiality.
Yes. We specifically evaluate reliance on third-party models, including cost exposure, vendor lock-in, data handling, availability risk, and long-term defensibility. We assess whether the business can survive pricing, policy, or performance changes from upstream providers.
Yes. We evaluate governance maturity and readiness for emerging AI regulations, data protection requirements, and enterprise compliance expectations. This includes policies, controls, auditability, human oversight, and incident response not just written documentation.
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