Core Concepts
Sixteen concepts in sixteen diagrams. No jargon, no walls of theory.
The asset that compounds. Every Record, every Link, every approved artifact lives here as a versioned, role-owned, lifecycle-stated entity.
A governed hierarchy that makes delivery accountability enforceable. Roles travel with the role, not the person who left.
Every record (artifact, decision, policy, design) flows through a state machine. Every transition is versioned, actor-attributed, and timestamped.
An approved spec triggers a workflow. The workflow reads your tenant graph, applies installed standards, calls an LLM, and writes back a policy-checked artifact.
Read-only scanners that connect to your real systems and emit typed records into your tenant graph. No surveys, no spreadsheets.
Hundreds of pre-built standards, patterns, and reference architectures. Install in 1–2 weeks, customise 10–30% to your org, get governed defaults for the rest.
Arcitopsia overlays the standard delivery lifecycle without replacing it. Every SDLC stage gets a capability that runs on top of your existing tools, with the Persisted Knowledge Graph as the shared substrate.
Enterprise Architecture standards cascade down to Delivery Streams as machine-readable artifacts. TOGAF governance layers (L3 through L10) map cleanly to delivery team types, and every layer's standards travel as records into the streams that deliver.
Every record links to its origin and its descendants. A business requirement traces through architecture, design, code, deployment and operations, all the way to the audit evidence it produces. Single graph query, every level.
The reason AI workflows produce useful outputs and not generic drafts. Before any LLM call, the workflow auto-fetches the relevant slice of your tenant graph and injects it as grounding context. The model sees your reality, not a Stack Overflow average.
Connector credentials and settings (AWS account, GitHub PAT, DB endpoint, region, retention policy) live as Tool Configs. Values resolve down a hierarchy from Tenant → Program → Project → Team → Environment. Each level can override values from above.
Targets define what to scan (which AWS account, which GitHub org, which database). Tool Configs supply how to reach it (credentials, region, scope). The Probe Analyzer routes each target to the right probe and orchestrates the scan that populates the Persisted Enterprise Knowledge Graph.
Approvals run as policy-driven flows, not email chains. When an artifact is submitted, the policy engine checks it against guardrails. Compliant changes auto-approve; non-compliant changes route to the right reviewer with the violations highlighted.
Governance lives inside the operating loop, not as an annual audit prep. Four pillars work together against the Persisted Enterprise Knowledge Graph as the source of truth.
The bidirectional integration layer that plugs Arcitopsia into your existing toolchain. Workflows push and pull from ITSM, ticketing, observability, identity, cloud, and CI/CD systems, so the platform never becomes another island.
Every AI-generated artifact carries full provenance: the context records used, the LLM invocation, the prompt template, the model version, and the generation timestamp. Provenance is what makes generated outputs auditable and trustworthy in regulated environments.
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