Evaluating Jev as a Prompt-Injection Guardrail

A guardrail benchmark that ports the policy types of Amazon Bedrock Guardrails - six content filters, denied topics, word filters, sensitive information and contextual grounding - to TypeSafe's System One model Jev, then measures latency, cost and classification quality per case.

A benchmark comparing TypeSafe Jev with Claude Haiku 4.5 as input guardrails across jailbreak, evasion and benign prompts, measuring detection rates, false positives, latency and cost. At n=1,000 Jev traded some detection for a 328 ms median latency and $0.03 per 1K calls versus Haiku's 2,066 ms and $1.87.

Harmful/benign classification and answer-choice robustness for five type-safe models: 83,183 source requests from XSTest, OR-Bench and HarmBench in original, reversed and order-only variants.

An independent Laya-versus-Jev comparison on one RTX 5090: phishing detection over 2,000 emails, the typed-decisions suite, latency scaling to 100 questions per pass and eleven probe assertions.

A timestamped benchmark and trading arena that replays resolved prediction markets: models commit to probabilities using only information available at the time, and realized P&L is the headline metric with Brier, log-loss and ECE as checks.

GTM workflow experiments with Jev from deepdave98: weekly datasets, prompts, and benchmark results that test Jev against Claude on lead triage and routing, deal risk, reply triage, MEDDPICC from call summaries, duplicate accounts, and closed-lost reactivation.