SystemsCatalog
A living map of every system your organization runs on.
One map of the whole estate
Custom applications and off-the-shelf systems like your SIS, finance and identity platforms, stitched into a single machine-readable map.
Lives with the code
Each app keeps a short SYSTEMS.md beside its README. Architecture documentation evolves with the architecture instead of going stale in a wiki.
Validated on every change
Schema and cross-link checks run in CI, so broken dependencies and missing owners are caught before they merge.
Diagrams for free
Dependency maps of the full estate, of tier-1 systems only, or of each category, plus a graph export for ad-hoc analysis.
Impact and ownership answers
"If we change this app, what breaks?" and "Who owns every system that touches student data?" become queries, not meetings.
A foundation for KPIs
Signals harvested from each repo, including activity, tech stack and size, roll up into comparable scorecards across the estate.
Why a catalog
Most IT teams carry the map of their systems in a few people’s heads. When those people are busy or move on, every change becomes a risk. SystemsCatalog makes that knowledge explicit, versioned and checkable.
How it works
Every custom repository carries two files in its root.
| File | Audience | Answers |
|---|---|---|
README.md | Developers | How do I build, test and change this code? |
SYSTEMS.md | Architects and owners | How does this app fit in? Who owns it, what does it talk to, how critical is it? |
Off-the-shelf systems with no code repository get their SYSTEMS.md in the catalog itself. Tooling reads the structured frontmatter in every file to validate, diagram and answer questions about the estate.
Before you adopt the next tool
New learning tools, AI tutors among them, arrive faster than ever. Before one connects to student data, SystemsCatalog tells you what it would touch, who owns those systems, and what else depends on them.
Security and sizing insight
Onboarding a real estate into the catalog surfaces what’s hiding in it, such as committed credentials or sensitive data in repositories, so you can fix it before it becomes an incident. Sizing baselines show where your engineering effort actually sits.
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