AIAP is built for the era where AI agents don't just advise on architecture — they read it, extend it, and build it. So the architecture has to be machine-readable, governed, and trustworthy.
Architecture — and the governance around it — has lived in slideware and spreadsheets that humans skim, agents can't parse, and auditors can't trust. AI made the pile bigger and the drift faster. We turn it into structured, versioned, auditable data: the substrate AI needs to do real engineering work.
A single graph schema makes every architecture parseable, diffable, and safe for agents to mutate through validated tools — so the docs that govern it can fill themselves.
Multi-tenant RBAC, RLS on every table, encrypted secrets, and a complete activity log — built to stand up to ISO 27001 / 42001 and SOC 2 review.
Bring your own AI providers and connect any MCP client. Your architecture, your models, your control — no lock-in.
Make enterprise architecture a living engineering artifact — designed once, versioned like software, governed automatically, and handed to AI to build.
Spend less time drawing and more time deciding; the canvas keeps the structure, governance, and history for you.
Reusable components and org skills make good patterns the default across every project.
Hand the finished graph to AI to implement — the architecture is the spec, so there's no separate brief and no lost context.
Traceability and governance you can show an auditor, with the velocity AI makes possible.
Least-privilege roles, encrypted secrets and row-level security mean access is provable, not assumed.
Assessments fill from the live architecture and stay current, so coverage is provable instead of reconstructed the week before an audit.