Design TOGAF-layered cloud and AI systems on a structured canvas, govern them with versioned snapshots, and let every assessment — and every agent — read from one source of truth over MCP.
Every component is typed and placed on a Business / Data / Application / Technology layer, so the diagram is structured data — not a picture.
React Flow node/edge graphs with typed components, capabilities, and labeled integrations — validated by a single schema at every boundary. The diagram renders the data; the data is the truth.
Publish immutable snapshots with changelogs, run solution-design / architecture-review / DPIA / AI-impact assessments, and keep a full audit trail you can hand to a reviewer.
Pin a component to another project's component; when the upstream changes, dependent links flip to needs-review automatically — provenance, never a silent break.
Governance docs that used to live in slides, wikis and spreadsheets now fill from the architecture itself. Design once; the coverage takes care of itself.
Solution Design, Architecture Review, DPIA and AI Impact draw straight from the architecture — no re-typing the same facts into four documents.
Role-based access, row-level security, encrypted secrets and complete activity logging map to ISO 27001 / 42001 and SOC 2 evidence.
Because every form reads one source, you can show what's covered across every layer at a glance — and prove nothing has drifted.
Connect your own providers — Anthropic, OpenAI, Google, Azure, Bedrock, Vertex, Groq, Mistral or any OpenAI-compatible endpoint.
Provider keys are AES-256-GCM encrypted at rest and only decrypted server-side. Models are pulled into a per-org catalogue you control.
An AI Copilot reads the live project graph and proposes concrete, reviewable changes for architects to apply — answers-only for viewers.
Author SKILL.md skills once and run them from the Copilot with /skill across every project in the org — good patterns become the default.