Change a table without
breaking production.
The impact analysis that used to take a week of reading decompiled code is a scan and a few questions. You still make the calls. You just make them knowing what is actually connected to what, instead of guessing and finding out in production.
Everything bound to a table, in one call.
Point Pathix at a table and it returns every component that touches any column on it, deduplicated, with the columns each one writes or reads folded in. In one screen you can see which components only exist because of how the table is shaped today, and which carry real rules you have to preserve. That split is the whole migration, visible up front instead of discovered three weeks later.
Where a write can hide. Where a privilege can leak.
13 surfaces today · more on the waySee the blast radius before you touch a cascade.
Change a relationship behavior and the reach can be enormous and invisible. Pathix lays out the cascade fanout for a table: the direct relationships, the full total once you follow the cascades down, how deep each goes, whether a delete cascades or clears the reference, and exactly which tables it reaches. You see everything downstream that is going to feel the change before you make it.
A leaf-first rewrite order that flags its own unproven edges.
Point Pathix at a table and it returns a leaf-first rewrite order: what is safe to change first, the wave nothing downstream depends on, through the roots everything else depends on, with column data-flow and orchestration coupling folded into one sequence. What makes it usable is what it admits. It marks the cycles with no provable internal order, the edges it could not resolve, the components it could not place in the sequence, and the coupling it only modeled. It is a labeled direction to verify, not an asserted build order, and that line is what separates a plan you can act on from a diagram that looks certain and isn't.
We gave an AI agent the graph, not a migration plan. It rebuilt the plan anyway.
We gave an agent a real normalization job with no plan and junior-developer prompting. Working off the Pathix graph, it rebuilt the whole target model: one overloaded table resolved into four, every plugin's disposition mapped, a sequenced migration drafted. A second agent from a different maker, same graph, drew the same answer. The plan comes from the structure underneath, not the model.
And it caught what a schema diagram never would. Five of the six plugins on that table silently swallow their own errors, which means the rollups and totals sitting in production may already be wrong. The plan's instruction is blunt: recompute those values on migrate, do not trust-migrate them.
Read the migration plan it produced →
With AI on: an agent drafts the migration plan straight from the graph, with the disposition of every component and the data-integrity catches a static diagram can't show.
The artifact you hand your boss, your board, or your auditor.
One click produces a printable Environment Report: a state-of-the-environment writeup with remediation and control mappings, and an explicit note on what was and wasn't scanned. It is the document that turns a scan into something you can put in front of someone who will never open the tool.
Bring the table that scares you.
A 30-minute walkthrough on a pre-scanned demo environment. We'll trace whatever you're afraid to change.