Who it's for · AI builders
The last mile of every enterprise deal is a risk review. Arrive with evidence.
You built something that works. Now a merchant-risk analyst, a procurement committee, or a platform partner has to approve it, and they can't see inside. AetherLab tests your product the way their fears run, fixes what surfaces, and documents it in the format their process wants.
Deals don't die on demos. They stall in review.
The questionnaire wall
Security reviews now ask AI-specific questions your SOC 2 doesn't answer: jailbreak resistance, content policy enforcement, model behavior under abuse.
The payment gate
If your product touches payments, a processor's risk team decides whether you get to charge for it, and they are looking at your content risk.
The platform bar
App stores, model providers, and integration partners all run policy review. One violation clip can undo a launch.
Scan before they do. Guard in production. Hand over the pack.
Step 1
AdversarialScan your product
We attack it across text, conversations, and images, against break-goals matching what your reviewers fear. You get ranked findings, not a fail stamp.
Step 2
Close the gaps with Guardrails
PromptGuard and MediaGuard enforce your policies in production: flat pricing, real-time checks, one API.
Step 3
Walk in with the Evidence Pack
A standardized record of what was tested, fixed, and now enforced, written for the person whose job is to say no.
Two calls. Your policies. In production today.
Guardrails are one pip install aetherlab away. The same API screens text with check_prompt and images with check_media, against the policies you define.
from aetherlab import AetherLabClient
client = AetherLabClient() # reads AETHERLAB_API_KEY
result = client.check_prompt(
"user or model text to screen",
blacklisted_keywords=["guaranteed returns"],
)
print(result.compliance_status) # "Non-Compliant"
print(result.avg_threat_level) # 0.94Common questions from builders.
- We already did a pentest. Why is this different?
- A pentest covers your infrastructure. AI risk reviews increasingly probe your model's behavior: what it says, generates, and leaks under adversarial use. AdversarialScan tests exactly that surface, across text, conversations, and images, and produces evidence written for the risk team on the other side of the table.
- Will the Evidence Pack actually be accepted by enterprise reviewers?
- It is designed for them: severity-scored findings, business-impact mapping, remediation records, and an approval trail, structured so reviewers can reference it against frameworks like NIST AI RMF. It replaces the ad-hoc security-questionnaire scramble with a standing document you control.
- How fast can guardrails go into our product?
- The SDK is a pip install (aetherlab on PyPI) with two core calls: check_prompt for text and check_media for images. Policies are configured with our team against your rules; customers run up to 225 custom policy rules with flat pricing regardless of count.
- Does this help before we have enterprise deals in flight?
- Yes, and it is the cheapest time to do it. Finding and fixing your break-points before a prospect's risk team finds them converts diligence from a blocker into a proof point, and the same guardrails protect your consumer traffic in the meantime.
Turn review season into a strength.
Tell us what you're building and which review is next: enterprise procurement, payment onboarding, or platform policy. We'll scope the scan against it.
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.