Tenanteval
A platform-operations workspace that versions evaluation suites, samples authorized tenant traces, separates deterministic checks from model judgments and human labels, and issues tenant-scoped quality and cost reports with uncertainty and correction.
The supplied research confirms several active evaluation platforms and one lightweight CSV-based entrant. It did not find per-tenant attribution as a first-class workflow for multi-merchant platforms. That gap is credible but not proof of exclusivity, and existing vendors can add segmentation.
Tenanteval should distinguish platform configuration, tenant configuration, prompt and model version, dataset rights, trace, expected behavior, deterministic check, model-judge output, human label, adjudication, cost, incident, and remediation. A judge score is a measurement under a named rubric and configuration, not ground truth. Cross-tenant comparisons require comparable tasks, enough samples, consent and contract, privacy review, and uncertainty.
The product does not decide whether a merchant is fraudulent, safe, compliant, high quality, or eligible for enforcement. It cannot expose one tenant's data to another or silently reuse traces for training. Alerts create review cases; platform owners and authorized tenant contacts control remediation and communication.
Product, AI operations, trust and safety, and platform engineering teams that run shared AI features for many merchant or customer tenants.
Product and AI operations teams at multi-tenant platforms are specific.
Suites and evaluation infrastructure can be reused across tenants with strict isolation.
The source has modest graph support and a clear operational signal.
The platform PM buyer, active evaluation market, tenant attribution gap, and recurring report workflow are concrete.
Adjacent tools can add tenant dimensions, ground truth is expensive, trace rights are sensitive, model judges are unstable, and cross-tenant comparisons can mislead.
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