AI Setup, Governance & System Architecture

Growing teams inherit architecture decisions faster than they can question them — and now they're adding AI tools to the mix, often faster than anyone can assess the data-privacy and security implications. We step in as an outside technical authority, covering both the system design and the governance framework around how AI gets used inside it.

What we deliver

System architecture

• Architecture review: single points of failure, scaling ceilings, data-flow risks, and technical debt that's quietly becoming a liability.
• Target-state design: service boundaries, data ownership, and integration patterns sized to your actual growth trajectory.
• Migration roadmap and build-vs-buy guidance, sequenced to avoid a rewrite-everything bet.

AI governance

• Data privacy review for AI and LLM tool adoption — what data can safely reach which vendor, and under what terms.
• Acceptable-use policies and vendor risk assessment for AI tooling.
• Secure integration patterns for adopting LLMs and AI products without leaking sensitive or regulated data.

Who this is for

Engineering leaders who need a second opinion before a major rebuild, teams racing to adopt AI tools without a governance framework in place, and organizations preparing for due diligence, an audit, or a platform migration that now has to account for AI usage.

How it starts

A working session to map your current systems and your current or planned AI tool usage, followed by a written findings report and roadmap — no slideware, no boilerplate recommendations.