Aimeterly vs Black

See how your organization uses Claude Code, usage, estimated cost, productivity, and audit logs by user. No infrastructure to run; live in 5 minutes.

A black-box evaluation of how AI-generated tests find functional bugs in live APIs.

About Aimeterly

See how your organization uses Claude Code, usage, estimated cost, productivity, and audit logs by user. No infrastructure to run; live in 5 minutes. When your organization enables content capture, you can filter the audit log by keyword, user, and capture type to review the matching records (this is search and review, not automatic detection or alerts). Gauge adoption depth with DAU / WAU / MAU, sessions, and tokens by model per user, and find the members that need support.

About Black

A black-box evaluation of how AI-generated tests find functional bugs in live APIs. The harder question is whether those tests find bugs. Each system receives only a JSON schema and one valid sample payload, then must generate API test cases that expose failures in a live reference API. The evaluation uses APIEval-20 v1.0, a black-box benchmark contributed by KushoAI. Because KushoAI is also one of the evaluated systems, this report includes the methodology, workflow definitions, repeated-run setup, and robustness checks so readers can understand where the performance difference comes from.