Architecture decisions
Written decisions on which models to use, where data flows, how users sign in, where the system runs, and which vendors it relies on.
AI governance and architecture
We design access rules, approval steps, and release checks for AI applications, covering where data goes, what the system can change, and who handles failures.
ScopeA focused review or a staged program
Delivery timingUsually 1 to 3 weeks for a focused review; larger programs run in stages
How we check itThe review records each risk with the control or test that addresses it, or the limitation you chose to accept.
Define what your AI can access and do
AI that can read company data or change records needs clear limits before it goes live. We work through those limits with your product, security, and policy owners, then turn the decisions into an architecture that your developers, or ours, can build and review.
Good fit
- Teams bringing AI into sensitive workflows
- Leaders who must show how AI access and actions are controlled
- Product teams deciding what data can go to which AI vendors
Not the right fit
- Teams that only need a written AI policy, with no system to apply it to
- Organizations that need a certification or formal audit opinion, which we do not provide
- Designs that no team is assigned to build
What you get
Control design
Rules for who can access what, where a person must approve, which tests the AI must pass, and who responds to incidents.
Release criteria
A checklist your team can run before launching a new AI feature or changing an existing one.
How it works
Trace data and actions
We follow sensitive data and high-impact actions through the product, whether it is already running or still being designed.
Choose the controls
We match approval steps, separation between systems, logging, and recovery to what a mistake would cost.
Test the design
We walk through realistic failures with your team, check that the design handles them, and assign each open decision to a named person.
Scope, cost, and ownership
What we need from you
- Whatever architecture and data-flow documentation exists today
- Time from product, security, and policy owners
What affects cost
- Data sensitivity and the risk of a wrong decision
- Number of systems, vendors, and deployment environments
- Any required security or specialist review
Technical scope
- Identity and access boundaries
- System interfaces
- Model selection
- Audit logging
- Data retention
- Human review points
- Release checks
Support and maintenance
The design names who can grant access, approve model changes, and respond to incidents. It also records how policy exceptions are reviewed.
Common questions
Is this only for regulated industries?
No. Clear permissions, a record of what the system did, and controlled changes help whenever an AI system affects people or important data.
Can this be part of a build?
Yes. It is most useful while the product, integrations, and workflow are still being designed.