AI SaaS founders
AI Product Evidence Review for SaaS Teams
Turn an AI product’s architecture, data flows and governance claims into evidence a customer, investor or procurement team can understand.
Why teams reach this point
An AI demo can reach customers before its model, vendor, data and responsibility boundaries are properly documented. As scrutiny increases, teams need evidence for what the system does, what data it uses, where humans remain responsible and how changes are controlled.
Risks the review surfaces
- +Model, vendor and customer responsibility boundaries are ambiguous.
- +Personal data enters prompts, logs or training workflows without clear controls.
- +Human oversight, disclosure, monitoring and incident handling are only informal.
- +Marketing claims are stronger than the system documentation can support.
What I review
- +Map models, vendors, integrations, personal data and system boundaries.
- +Review disclosure, oversight, logging, change control and customer-facing evidence.
- +Identify likely EU AI Act and GDPR engineering questions without presenting legal sign-off.
- +Create an evidence backlog tied to pilots, procurement, fundraising or launch.
What useful closure looks like
- +An explicit AI system and responsibility map
- +A prioritised evidence and controls backlog
- +A stronger technical narrative for customers
Relevant experience
- +AI and sensitive-data product delivery
- +Privacy-by-design and governance implementation
- +Architecture communication for technical and non-technical reviewers
Frequently asked questions
Does this determine our final EU AI Act classification?
It supports technical scoping and evidence gathering, but it is not legal advice. Final legal classification should be confirmed with qualified counsel where needed.
Can you review third-party model providers?
I review how providers are integrated, which data crosses the boundary, the controls and documentation you rely on, and what remains your team’s responsibility.
Is this useful before enterprise procurement?
Yes. The review is designed to surface the architecture, privacy, oversight and evidence questions that commonly appear once an AI product moves beyond a demo.