Using Jev in Domain Abuse Review
Workflow design article: one Jev Noul asks whether a domain name refers to a protected brand, with the TLD kept out of the input.
This summary is based on how curated lists describe the project; its README was not available to review.
Overview
This article on abtdomain.com describes a production workflow for domain abuse review using Jev. According to the list summary, it asks a single Noul question: does this domain name refer to a protected brand?
Two design choices stand out. The top-level domain is kept out of the input so it cannot skew the answer, and a second-level domain is scored once even when it appears across many TLDs. It is a compact example of shaping state carefully so a decision model judges only what matters.
What the decision model decides
| Type | Question | Answers |
|---|---|---|
| Yes / no | Does this domain name refer to a protected brand? | yes · no |
How it works
- Strip the TLD from the domain name.
- Deduplicate second-level domains across TLDs.
- Ask Jev one Noul per second-level domain.
- Route likely brand references to abuse review.
Strengths
- Removes a confounding signal (TLD) from the input.
- Avoids paying for the same name repeatedly.
Caveats
- Known only from a list summary.
- Thresholds and accuracy are not stated.
At a glance
- Type
- Article
- Status
- production
Use cases and integrations
How curated lists describe it
Production workflow: asks one `Noul` whether a domain name refers to a protected brand, keeping the TLD out of the input so it cannot skew the answer and scoring a second-level domain once even when it spreads across TLDs.
yibie/awesome-jev
Frequently asked questions
What does Jev decide?
Whether a domain name refers to a protected brand.
Why remove the TLD?
So the extension cannot skew the judgment.
How is cost controlled?
Each second-level domain is scored once across TLDs.
Related projects
Crypto exchange risk gateway where Jev triages deposits and withdrawals while hard rules and Java code make the final call.
ServiceprototypeJavaSAP S/4HANA proof of concept that sends anonymised purchase-order risk indicators to Jev and classifies orders to release, approve or block.
ProjectprototypeABAPScam text checker where code extracts link and sender facts, Jev answers eleven narrow questions, and a pure policy scores risk.
ProjectprototypeTypeScriptUiPath coded agent for AML alert triage where an LLM reads and writes but Jev makes every red-flag, risk and disposition decision.
AppprototypePythonOpen-source link attribution platform whose malicious-link check calls Jev before a short link is created.
ProjectproductionTypeScriptx402 pre-payment risk-check provider combining sanctions lists, phishing feeds and simulation with Jev checks for injected instructions.
Projectproduction