Most AI tools give you one answer from one model. That answer may be confident, well-structured, and completely wrong on a specific claim with no signal that anything is off. The multi-model stress test is Qonera’s default chat mode because it changes that setup: three independent AI models answer the same question in parallel, and a fourth model synthesises where they agree and where they diverge before the result reaches the reviewer. Here is exactly what happens at each step.
When you submit a question in Qonera, the platform sends it to three AI models at the same time. Each model receives the same prompt and the same source documents from your workspace. Crucially, none of them sees what the others are generating: each model works independently, without the answers of the other two in context. This is deliberate. If the models could see each other’s output, they would converge artificially. Running them in isolation means any agreement between them is genuine convergence on the evidence, and any disagreement reflects a real difference in how the evidence can be read.
Once the three working models have finished, two separate calls start at the same time. The first is the judge, which reads all three drafts and streams a synthesised answer directly to the chat interface. The user can read the synthesis as it generates. The second is a review call that runs alongside the judge, reading the same three drafts to analyse where the models disputed each other, where one model was an outlier, and where claims were unsupported by the source documents. The review call never sees the judge’s synthesised answer: it works from the raw model drafts only, which keeps the analysis independent.
The judge produces the answer the user reads. The review call produces the data behind the Conflict Heatmap. They run at the same time so the user does not wait for both sequentially.
When the review call completes, Qonera sends its findings to the verification panel: which claims were disputed across models, which model was an outlier on a specific point, and which claims were not supported by the source documents. These findings are mapped onto the synthesis at the claim level, producing the Conflict Heatmap. Each significant claim is annotated to show whether the three working models agreed on it independently, partially aligned, or diverged.
This is what makes the stress test actionable for a human reviewer rather than just producing a cleaner answer. Claims that all three models agreed on independently are meaningfully stronger than claims where only one model reached that conclusion. Claims flagged by the heatmap are direct pointers to where a reviewer’s attention should go first, without requiring a full critical re-read of the entire response.
The three AI layers above produce the answer. What happens next depends on how the workspace is configured. Workspace administrators can set an approval policy that determines when a human sign-off is required before an answer can be used: every answer, deep-research answers only, high-risk flagged answers only, or none. When a gate is configured, the answer enters a review queue and a named supervisor can approve it for continued use, approve it for client delivery, or send it back for revision. That decision is recorded in the audit trail alongside the stress-test run.
When no gate is configured, the human review step is still available but optional: any answer can have a sign-off requested manually from the message actions. For claims the Conflict Heatmap flags as uncertain, a peer review turn can also be triggered directly on the synthesis before any sign-off decision is made. The approval system and the peer review turn are independent: they can be used together or separately depending on how much scrutiny the work needs.
Every stress-test run is recorded in full to the tamper-evident audit trail. The log captures the timestamp, the identity of each working model and the judge, token counts and cost per model, the hash of the system prompt used, and the source document set that was active during the run. Each audit record is hash-chained to the one before it, which means the sequence of events cannot be modified after the fact. That record is created automatically: the team does not need to capture it separately or assemble it from logs. It is available for export as CSV or PDF from the workspace settings.
Before any of the above runs, Qonera checks the source documents in the active workspace for staleness, contradictions between files, and version mismatches. That check is not optional: it runs on every question that touches uploaded data. The stress test can only be as strong as the evidence the models work from, and flagging weak sources before the models run is part of making the answer defensible, not just well-structured.
The multi-model stress test is designed for professional work where a single wrong claim has real consequences: client-facing analysis, investment research, regulatory submissions, due diligence. Any situation where the output will be presented to someone outside the team and the team needs to be able to account for how it was verified is a situation where running three independent models and capturing the disagreements is worth the extra steps. See how the full workflow operates at qonera.ai/how-it-works, or compare it with single-model peer review to decide which fits your work.
Multi-model stress testing, Conflict Heatmap, tamper-evident audit trail, and structured sign-off, built for teams who need defensible AI output.