Wherever AI turns a source into an output, meaning can drift. Plumb reads the output against the source and shows you exactly what changed, before anyone acts on it.
Plumb does not care what the workflow is called. Insurance claim. Clinical handoff. Customer escalation. Legal review. Underwriting note. Support summary.
The pattern is the same. A source says one thing. An AI output says something close enough to sound right. But the meaning moved.
Three examples. Three different workflows. The same underlying failure.
Water damage extends to the subfloor and load-bearing wall. Structural assessment required before repair.
Water damage noted to flooring. Recommend repair.
Patient stable. Penicillin allergy, confirmed reaction on file.
Patient stable and cleared for a standard antibiotic course.
Your update broke my checkout. I have lost two days of orders and I am ready to cancel.
Customer reports friction with the checkout flow after the update.
Plumb finds these movements before they become decisions.
Plumb reads the source and the output and tells you exactly where they diverge.
Teams adopt AI for the speed, and the speed is real. But every time it turns a source into an output, the meaning can move, and the output reads clean either way. The more of your decisions run on those outputs, the more of them sit on a version of the source that quietly shifted.
Grounding tells the model what to look at. It does not check what the model did with it. Plumb is the layer that checks, reading the source and the output against each other and telling you where they diverge.
The output reflected the source but softened a commitment, dropped a condition, or reframed the conclusion. Nothing was invented. Something changed.
A risk, a constraint, a flag. It was in the source. It never reached the output, and the output doesn't tell you it's gone.
The recommendation sounds grounded. What the source actually supported didn't get you there. The inference filled the space the source left open.
Beginning, middle, end. Feels reviewed. Half the source is missing and nothing in the output signals it.
No new process. No new interface. Plumb reads your source and your output and tells you where they diverge.
The material the AI was working from. Tickets, claims, notes, contracts, retrieved context, workflow outputs. Whatever the source set was.
→The summary, brief, assessment, or recommendation, exactly as generated.
→What held. What drifted. What was dropped. What has no source behind it. Before anyone acts on it.
Plumb sits at the end of what you already run. It doesn't touch the generation pipeline. It doesn't change how your team works. It compares the source and the output and tells you where they diverge.
Only the two things you send it: the source and the output. Not your systems, your databases, or your prompts.
Outside how the output gets made. It reads after the fact and never changes your pipeline.
The findings, and nothing else. Your material stays yours.
Plumb is for teams using AI to turn their own information into the reads they decide on. Wherever a stream of source material gets summarized by AI before someone acts on it, and the gap between what the AI produced and what the source said has consequences.
The workflows differ. The problem doesn't.
You're not reading every output line by line. You don't have the time. But the call is yours, and it travels with you when the read was wrong.
Plumb is how you know the output held before you act on it. Not because it looks right. Because it checked out.
You know the output depends on things that can quietly go wrong. Right now nothing catches it before it leaves.
Plumb is that layer. One integration at the tail end of what you already run.
15 minutes. We run Plumb against a real output, yours or one of ours, and show you exactly what it finds. No deck. No pitch. Just the product working.