Most AI workflows start with a question. Qonera can start one step earlier, with a question about the questions: is this document set actually fit to be analyzed at all? That is what a Source Audit does. Before the team asks anything substantive, the audit runs across the full document set looking for the problems that would quietly poison every answer built on top of it.
The idea comes from a pattern every professional team recognizes. The most expensive errors in AI-assisted work are rarely caused by the model misreading a good document. They are caused by the model faithfully reading a bad one: the superseded contract draft, the pricing sheet from two quarters ago, the two reports that quietly assume different market definitions. Analysis built on conflicted or stale sources is wrong before the first question is asked, and no amount of checking the answer will reveal it, because the answer accurately reflects the flawed material.
The Source Audit examines the document set as a whole rather than file by file, because the most dangerous problems live between documents rather than inside them. It surfaces conflicting assumptions between files, versions of the same document that disagree with each other, material that looks stale next to its neighbours, and gaps where the set implies a document that is not actually there. A single file can look perfectly fine on its own and still be the odd one out in the set.
Because this judgment matters, the audit does not rest on one model’s opinion. Three independent models examine the full document context and a judge model consolidates what they found, the same stress-test discipline Qonera applies to answers, applied to the evidence itself. Where the models converge on a problem, the team can be confident it is real. The result is a report the team reviews before deciding what to fix, what to remove, and what to proceed with.
The economics are straightforward. A source problem found before analysis costs a document swap. The same problem found after the analysis has shaped a deliverable costs the deliverable, plus the awkward conversation about why the recommendation rested on a file someone should have retired months ago. The audit is a deliberate, priced step precisely because it is worth doing at the moments that matter: the start of an engagement, a data room that just arrived, a document set inherited from another team.
It also changes what the team can say afterwards. A firm that audited its sources before analyzing them can tell a client not only that the answer was reviewed, but that the material behind the answer was checked for currency and consistency first. That is a stronger statement than most firms can make about their own manual workflows, where nobody can quite say when anyone last compared the versions in the folder.
The Source Audit is the explicit, on-demand version of the checking that runs through the whole platform: the Evidence Base grounds every answer in your actual files, and the review and approval workflow makes sure a named person signs off before the work leaves the team. The audit simply moves part of that scrutiny to the front, where it is cheapest, so the review at the end is confirming good work instead of discovering bad inputs.
Garbage in, garbage out is the oldest rule in computing, and AI has not repealed it. It has raised the stakes, because AI turns garbage into something polished enough to ship. Auditing the sources before the analysis is how a team makes sure the most confident answer in the room is built on material that deserves the confidence.
Multi-model stress testing, Conflict Heatmap, tamper-evident audit trail, and structured sign-off, built for teams who need defensible AI output.