Product
Professional teams are shipping AI assisted work to clients every day with no structured way to verify what's in it. Qonera stress-tests the output against your files, multiple AI models, and live evidence before anything goes out.
The problem
In professional environments, where a wrong assumption can cost a client deal, a signed memo, or a fund allocation, “probably right” isn't good enough. Teams need a structured way to verify AI output before it reaches the client.
The EU AI Act is accelerating this. Firms using AI in professional services face growing expectations around human oversight, traceability, and documented review. Qonera builds those controls into the workflow itself, so compliance is a byproduct of doing the work, not a separate exercise.
Core capabilities
Your documents, indexed and integrity-checked before analysis begins.
Qonera builds your Evidence Base from every file you upload, indexing each document and making it searchable across the entire workspace. When you ask a question, all models are grounded against that verified source set. Not the internet. Not training data. Your files. Outdated sources are flagged before analysis begins. Conflicts between documents are surfaced before any model draws a conclusion.
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Before any model runs, Qonera audits your document set for problems that would silently corrupt the output.
File versions are checked against each other. Timestamps are compared. Conflicting assumptions between documents are surfaced and flagged. Stale sources are caught before they become stale conclusions. The review starts with clean evidence, not a hope that the files are current.
Every question runs through multiple AI models independently. No cross contamination.
Each model analyzes your question and documents on its own. No model sees another's output. No consensus is forced. When two models reach different conclusions from the same evidence, you know the reasoning is fragile. When they converge independently, the finding is stronger.
Every claim tagged Green, Orange, Red, or Outlier with per claim citations.
The heatmap shows exactly where models agree, where they diverge, and where evidence is weak or missing. Each tag is clickable, linking directly to the source material behind the claim. No ambiguity about what's supported and what isn't. Reviewers see the confidence landscape before they approve anything.
Nothing leaves without a named sign off. Every inference logged in a tamper evident, hash chain verified trail.
A named supervisor reviews flagged claims, approves, annotates, or sends back for revision. The sign off is recorded with reviewer identity and timestamp. The full audit trail is append only, hash chain verified, and exportable as CSV or PDF for client assurance, internal governance, or regulatory review.
“We don’t just check the AI’s answer. We check whether the team gave the AI the right evidence to begin with.”
EU AI Act readiness
Qonera gives teams the practical review controls the EU AI Act pushes toward: human oversight before delivery, documented review steps, and traceable audit records, built into everyday workflow, not bolted on after the fact.
See our AI Governance page →Get started
Qonera is the review and approval layer for AI assisted client deliverables.