Build Trust Into Every Job Listing.

Reviewing every posting by hand isn’t realistic for a busy platform. Our approach is risk-based: together we agree which postings to prioritize — for example, listings from first-time or unverified employers, listings reported by users, and listings that match known fraud indicators such as upfront fees, off-platform contact demands, or unrealistic pay.

Those postings receive an in-depth analyst review, supported by AI-assisted pattern analysis. Each review records the indicators found, a risk level, and a recommended action. Your package limit is applied to the highest-risk postings, and coverage can be scoped to your actual volume.

An employer authenticity assessment looks at whether an account appears to represent a real, established organization: public business registration records, domain age and ownership signals, whether the email domain matches the company, the company’s wider public presence, and consistency between the account details and the organization it names.

The result is a documented outcome with supporting notes, so your team can approve, request more information, or escalate according to your own policies.

Yes — this is one of the most useful outputs of ongoing reviews. Fraudulent listings often reuse wording, contact details, compensation structures, or domains. When we review postings over time, we note these recurring elements and summarize them in your fraud risk report.

Those patterns can inform moderation rules, onboarding checks, and user-facing safety guidance. Pattern findings depend on the volume and period reviewed, so we describe what we observed rather than predicting future activity.