AI is built to sound right. Plumb shows whether it is.
The vendor is obligated to deliver a remediation plan within 30 days of any material breach, subject to written approval from both parties before implementation.
The vendor is expected to deliver a remediation plan within 30 days of any material breach.
A 60-page report gets fed to AI. The summary comes back in minutes. Tight, readable, looks like it covered everything. Two of the five key findings in the source didn't make it in. One that did was reframed as a conclusion the report never actually drew.
The summary gets used. Nobody goes back to the report. That's the whole point of a summary.
A partner reviews an AI brief before a client call. It reads well. The client's actual concern from last week's notes was flagged as a condition, not a signal of approval. The brief called it approval.
The call starts from the wrong premise. The material behind it had the right one.
AI doesn't fabricate randomly. It compresses, synthesises, and moves on. A condition becomes a statement. A risk gets dropped. A client's hesitation becomes a confirmation. The output reads clean because that's what AI does.
The problem isn't that your team is using AI wrong. It's that there's nothing between the evidence and the output that tells you what changed.
Grounding tells the model what to look at. It does not verify what the model did with what it looked at. That gap is where decisions get made on a smoother version of reality than the evidence warranted.
Plumb closes that gap.
The AI reflected the material but softened a commitment, dropped a condition, or reframed the conclusion. Nothing was invented. Something changed.
A risk. A constraint. A client flag. It was in the evidence. It never made it into the output. The output doesn't signal the gap.
The recommendation sounds grounded. The material behind it didn't actually get you there. The inference filled the space the evidence left open.
Beginning, middle, end. Feels reviewed. Half the underlying material is missing and nothing in the output tells you that.
Plumb reads the evidence and the output. Tells you exactly where they diverge.
No new process. No new interface. Plumb reads your source and your output and tells you where they diverge.
The material your AI was actually working from. Contracts, emails, briefs, retrieved context, workflow outputs. Whatever the evidence set was.
→The summary, brief, evaluation, or recommendation your AI produced. Exactly as generated.
→What held up. What drifted. What was dropped. What has no evidence behind it. Before anyone acts on the output.
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 evidence and the output and tells you where they diverge.
Plumb is for teams already using AI to produce work that drives decisions. Consulting. Advisory. Legal. Finance. Anywhere the gap between what the AI produced and what the source actually said has consequences.
The workflows differ. The problem doesn't.
You're not reading every output line by line. You don't have time. But your name travels with the work when something's wrong.
Plumb is how you know it can go out. Not because it looks right. Because it checked out.
You know the output quality depends on things that can go wrong. Right now there's no layer that catches it before it leaves.
Plumb is the 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.