The AI Accuracy Audit Doesn't Keep What It Reads
Running an AI accuracy audit means putting a business's internal reference material in front of the person doing the work: a fact sheet, pricing that hasn't gone public yet, a service line still in planning, sometimes a document comparing the business directly with a named competitor. None of that needs to remain in our application once the audit is finished, and in the version we built, it doesn't. The audit runs on the same zero-retention architecture from the series we just closed out, applied to the kind of engagement that made building it worth the trouble.
What passes through an audit
The audit itself is a comparison. You establish what's actually true about a business first, then ask five different AI systems what they say about it. The discrepancies become the basis for the findings.
Establishing what's actually true means reading real internal material, not just the public website. An audit based only on public information can identify inaccuracies in what's already online, but it can't identify discrepancies between what a business knows about itself and what AI systems say about it.
That step is also the one that makes storage tempting. Once a client's internal documents are sitting in a processing pipeline, keeping them around for the next audit cycle looks like a small convenience. It's the same convenience that turns a stateless tool into a document repository one small decision at a time.
The reusable question bank without the client's documents in it
The one part of an audit that does need to accumulate over time is the question bank: the prompts used to probe an AI system about a given industry, refined across engagements so each new audit doesn't start from a blank page. That's a real advantage, but it's also where reusable information can get confused with client data.
The two are different. A question like "what does this business specialize in, and who does it primarily serve?" is a reusable pattern, tagged by industry, with nothing client-specific in it. The client's answer, drawn from internal documents, is the material that gets discarded when the audit closes.
The bank grows. The client's material doesn't join it.
What it costs to get right
None of this is exotic, but it has a cost. A system that processes documents without retaining them between sessions works differently from one that stores everything and indexes it for later, the tradeoff we wrote about for the general case. It can require repeating operations that a persistent system could reuse. It's also the version we'd want a vendor to have built if we were handing over documents we hadn't published yet.
What to ask if you're evaluating an audit like this
Some AI visibility tools are built around growing a dataset, which is a valid design if that's the product. Ours is built differently. Ask which one you're getting before you hand anyone your internal material:
- What happens to the source document after the audit finishes, specifically, not in general terms?
- Are the vendor's subprocessors, including OCR, extraction, and hosting providers, covered by the same retention commitment the vendor makes?
- Is anything you submit used to train a model, theirs or anyone else's?
- Can you get a straight answer to those questions in writing, not just in a sales conversation?
An audit that reads your internal documents to tell you the truth about your public presence should not become a second place those documents live. The same goes for the tool doing the auditing.
If you want your own AI visibility audited under exactly this architecture, get in touch.