AI accelerates code. Impact compounds silently. We show teams what every change touches, what’s at risk, and what broke — across the whole system.
Stale docs. Risky PRs. Root causes. Spec concerns. One feed of actionable outcomes — each with a clear next step.
Before you build — is this plan sound? Before you merge — what does this change actually affect? When something breaks — why, and where did it start?
Review specs, plans, and proposed changes against your live architecture. Catch breaking changes, missed dependencies, and design mistakes while you’re still in the IDE — not after the PR is up.
Every PR gets checked against a live model of your system. Not just the diff — the dependencies, the consumers, the docs that reference the code you changed. Impact analysis, not just code review.
Issues are enriched with system graph context the moment they’re filed. Probable root cause, affected components, and suggested approaches — so whoever picks it up starts with the answer, not from scratch.
Real specs, diffs, and issues. Every one checked against the live system graph — not just the lines that changed.
SearchQuery is a shared contract serialized across service boundaries. Adding a required field will break deserialization of in-flight messages and compilation of all 5 downstream consumers.
Architecture, dependencies, release readiness, root cause. In Slack, your IDE, or the API — every answer traced back to real code.
Contributors break things. AI agents break things faster. Superpositional gives both the context they need while they code and before they merge. No more “I didn’t know that touched X.”
Cross-repo dependency visibility. Standards drift detection. Hard numbers on whether your architecture is holding up or quietly falling apart.
Release readiness. Risk exposure. Architecture health. Incident root cause. In plain English, no code, no “let me check with the team.”
Full structural analysis — entities, relations, and dependency graphs extracted from your codebase across every major language.