Who it's for · Payments & merchant risk
Underwrite AI merchants with evidence, not attestations.
AI-native merchants are arriving in your onboarding queue faster than your review process was designed for. AetherLab tests what their systems actually do under adversarial pressure and hands your risk team a standardized, severity-scored record to decide on.
Your queue is filling with systems your process can't see into.
Merchant risk teams built rigorous processes for businesses whose behavior is documented in statements and websites. An AI product's behavior lives in its model, and changes with every deployment. Traditional review has no instrument for that.
Opaque behavior
What an AI merchant's system will produce for a motivated user is not visible from its marketing site or its policy PDF.
Network exposure
Content and conduct failures flow uphill: from merchant, to processor, to acquirer, to network, with your name on the file at each step.
Inconsistent review
Without a standard instrument, each analyst improvises, and decisions can't be compared across applicants or defended later.
From application to defensible decision.
The workflow is the one your team already runs, with an evidence step where the guesswork used to be.
A standing instrument for AI merchant review.
Assess
Adversarial assessment
AdversarialScan runs against the merchant's system with break-goals shaped by your policies and network rules, across text, conversation, and image.
Decide
Evidence Pack per applicant
Severity-scored findings tied to business impact, in a standard format your analysts can compare, escalate, and archive.
Monitor
Controls between reviews
Guardrails enforce content policy in the merchant's production flow, so approval conditions keep being met after the decision.
AetherLab already runs these controls at scale, including for a top-10 high-risk payment processor, screening text and image in production. In head-to-head evaluations in high-stakes workflows, customers chose AetherLab over Amazon Bedrock Guardrails and Hive AI.
Common questions from payments teams.
- What does AetherLab actually test when an AI merchant applies?
- We run adversarial testing against the merchant's live AI system, across text, conversations, and images, targeting break-goals derived from your onboarding policy and network rules. The output is a severity-scored set of findings tied to business impact, delivered as a standardized Evidence Pack your risk team can compare across applicants.
- How is this different from a questionnaire or attestation?
- Questionnaires record what a merchant says about their controls. AetherLab records what the system actually does under adversarial pressure, with reproducible evidence for every finding. The two are complementary; the Evidence Pack gives the attestation something to stand on.
- Can this run continuously, not just at onboarding?
- Yes. Assessments can be repeated on a schedule or on material change, and Guardrails can run in production between assessments, so a merchant's risk posture is monitored rather than sampled once.
- Does AetherLab handle image-generation merchants?
- Yes. Image generation and image understanding are first-class attack surfaces in AdversarialScan, and MediaGuard enforces visual content policies in production. Image content is where much of the payment-ecosystem risk concentrates, so it is treated with the same rigor as text.
Put an instrument behind your AI merchant decisions.
Tell us about your onboarding flow and the AI merchants in it. We'll show you what an assessment finds and what the Evidence Pack gives your analysts.
Ask about the Evidence Pack
Leave your email and we'll walk you through what an Evidence Pack contains for your use case: severity-scored findings, business-impact mapping, and the approval record.