AI Trust, Privacy, and Safety
Built to be explained. Built to be trusted.
AI cost you cannot see, client data going into systems nobody fully understands, decisions that cannot be audited. We design against all of it — before we write a line of code.
The four things that go wrong
It is usually the same four problems.
The AI bill keeps growing and nobody knows which part of the system is causing it. Client data flows into a third-party model and the legal team starts asking questions. A decision gets made automatically and nobody can explain why. The team never adopted the system because nobody trained them on it.
None of these get fixed by adding a privacy policy. They get fixed by designing the system correctly in the first place.
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Most AI trust problems are architecture problems that got called something else after they happened.
What we build
Cost governance
Token usage tracking, cost-per-run visibility, lean architecture from day one
Data privacy design
Where data goes, who sees it, what happens when an integration changes
Governance controls
Approval paths, usage rules, vendor boundaries, and human checkpoints where they matter
Explainability
Human checkpoints where decisions have real consequence, audit logs throughout
Adoption design
Training, documentation, and handover built in, not added at the end
The process
How it works
We audit the current state
Where data goes, what gets logged, what can be explained.
We redesign or build
Privacy and explainability are structural decisions, not features.
We document it
So you can answer the questions before someone asks them.