Build the evidence bundle.
Attach prompt IDs, model responses, cited URLs, page snapshots and crawl results. Timestamp the inputs so every finding refers to a specific version of the evidence.
Turn missed recommendations
into practical fixes.
Vasa’s agents check your pages, technical access and cited sources to investigate missed recommendations. Your team gets a proposed fix, the evidence behind it and a way to measure the result.
PatagoniaCorrective analysis
Rainwear buying journeySelect a node to explore its findings
Investigate the evidence
from three angles.
A missed recommendation can have more than one cause. Agents examine the same evidence from different angles, then reconcile their findings into a correction your team can assess.
Investigate in parallel.
Technical, content and citation agents check different hypotheses. Each returns supporting references, conflicting evidence and any missing information.
Challenge the conclusion.
Check whether the proposed cause explains the observed pattern. Unresolved disagreements and weak evidence stay attached to the action for human review.
Watch a finding
become a fix.
In this Patagonia example, agents investigate why answers blur the distinction between a lightweight shell and insulated rainwear.
A product distinction
gets lost.
Question
“Which rain jacket should I choose for a cold-weather hike?”
Observed issue
The answer treats a shell as an insulated layer.
The content agent checks the answer against the supplied product references. It marks the claim for review and records the affected prompts.
Clarify the product.
Preserve the facts.
BeforeGeneral weather-protection copy
ProposedExplicit shell, insulation and layering guidance
The action specifies which page to update, the factual distinction to clarify and the references that support the change. A reviewer checks the wording before publication.
Rerun the questions.
Compare the evidence.
Baseline4 / 12 accurate
Follow-up10 / 12 accurate
Track the same affected prompts after the source change. Read improvement across repeated runs; the timing of a change alone does not prove what caused it.
Every action should
answer five questions.
Give your team the information they need to make the change.
01
What is wrong?
The affected prompts, responses, pages and exact claim or access problem.
02
What supports it?
Source references, timestamps, confidence and any competing explanations.
03
What should change?
A bounded correction with acceptance criteria and the expected effect.
04
Who owns it?
The responsible team, dependencies, review requirements and estimated effort.
05
How is it measured?
A baseline, the prompts to rerun and the signals that would demonstrate progress.
Agents investigate.
Your team stays in control.
Prioritise actions by buyer intent, evidence strength, affected prompt coverage and effort. High confidence describes the diagnosis, not a guaranteed ranking or recommendation uplift.
Review and approve changes before they reach your site or an external publication. Track each action from proposed to approved, implemented and measured.
ProposedApprovedImplementedMeasured
Find what’s holding
your AI visibility back.
Start with the evidence behind your AI visibility.
Request your free reportExplore AI Crawler Visibility