Everything in the AI Context Platform
Three surfaces, one source of truth. Capture requirements as they occur to you and keep the whole doc library in one place, build the standards and plans your AI agents read before they write a line, and document what shipped — for the people on your team and the agents working alongside them.
Capture → Build → Document
Capture
Get the Requirement Down Before It Evaporates
A voice note after a client call, a photo of a whiteboard, a dropped-in PDF, plain text. AI refines each one into a structured requirement or design — and gap analysis surfaces what nobody has thought of yet.
Tech Stack Declaration
Explicitly declare the frameworks, languages, and infrastructure your team uses. This drives check suggestions and CLAUDE.md generation.
Spec-Driven Requirements
Define expected behaviour in Given/When/Then format before code is written. Specs become the single source of truth for AI agents.
Checks from Mistakes
When AI generates code that violates your architecture, capture the mistake and correct approach as a check — so the same error never recurs.
Gap Analysis
AI reviews what you captured against what a complete requirement or design needs, and surfaces the questions nobody has thought to ask yet.
Build
AI Agents That Follow Your Rules — and Prove It
Set the standards your AI reads, plan the epics, and validate every change against your architecture. The code is written by agents in your own AI tool, which pull your ADRs, guardrails and plans over MCP before they generate — then fitness tests, contract checks and CI monitoring check what comes back.
AI Product Planning
Describe what you want to build. AI generates a structured plan with epics, issues, and dependencies — scoped to your architecture patterns.
Automated Execution
AI agents implement planned issues one by one, following your workflow steps, referencing your specs, and respecting your checks.
Context-Aware Chat
Get answers and code reviews from AI that knows your checks, tech stack, and architecture decisions. Every response is informed by your standards.
Architecture Fitness Tests
Auto-generated shell scripts that run in CI/CD to detect architecture violations. Every check becomes an executable check run on every pull request.
Integration Test Reports
View test results across all services with pass/fail status, duration metrics, and HTTP request/response traces. Spot regressions instantly.
CI/CD Monitoring
Real-time visibility into build and deploy status across all repositories. Know immediately when something breaks.
API Contract Testing
Browse OpenAPI specs and detect drift between deployed APIs and repository definitions. Living documentation that alerts on divergence.
Document
A Source of Truth Humans and Agents Both Read
Publish and the documentation in your repositories becomes a portal that stays in step with them — and feeds the CLAUDE.md, .cursorrules and MCP context your AI coding agents consume.
Documentation Portal
Publish from Capture and any markdown tree in a connected repository becomes a searchable, read-only portal on your own subdomain. It is public to the whole internet until you lock it to your tenant.
Living Documentation
The portal rebuilds from the repositories it describes, so what your team reads is what actually shipped — not a wiki page somebody forgot to update.
Drift Detection
When API specs diverge from code, or when CLAUDE.md becomes stale, SystemDox surfaces the drift and suggests updates to keep context accurate.
CLAUDE.md & MCP Context
Deliver the same source of truth to AI coding agents — auto-generated CLAUDE.md and .cursorrules files, plus a live MCP server that Claude Code, Cursor and Copilot query mid-session.
The Closed Loop
Gets Smarter With Every Sprint
One loop runs across all three surfaces: each failure becomes a check, each check prevents recurrence, and architecture quality ratchets up automatically.
Failure-to-Check Pipeline
Paste a failed fitness test, a review comment or a bug into Build, and SystemDox drafts the check and its fitness test for you. You review and save the rule, then publish the test to your CI repository as a pull request — and every AI session picks up the new rule through MCP.
Compounding Accuracy
Each check prevents an entire class of future mistakes across every AI-assisted session, every developer, and every repository. Quality compounds.
Enterprise-Grade Security
AI context data contains sensitive architecture decisions, tech stack details, and proprietary patterns. SystemDox is built with security as a core principle.
Ready to Transform Your Documentation?
Start capturing architecture decisions, meeting notes, and technical designs with your voice.