PREEXEC - 2026 Pricing, Features, Reviews & Alternatives

PREEXEC is a pre-execution governance architecture designed for AI systems. It evaluates inputs structurally before they reach a model, producing auditable decisions categorized as EXECUTE, HOLD, or BLOCK. The system uses three independent decision lanes that operate in parallel. It is designed for organizations deploying high-risk AI systems in regulated industries, such as financial institutions, healthcare providers, defense contractors, and legal firms. The architecture helps address compliance requirements in sectors where post-execution monitoring poses significant risk.

The first decision lane enforces universal hard blocks for content that violates applicable laws, prompt-injection attempts, jailbreak attempts, and practices prohibited under Article 5 of the EU AI Act. The second lane applies vendor-default policies tailored to various industries, including finance, healthcare, defense, and legal sectors. The third lane allows operators to define custom policies in plain language using an AI Policy Generator. These policies can be validated against proprietary test prompts through a Customer-Corpus Evaluation Pipeline, which measures catch rates and false-positive rates.

The system operates as a self-hosted solution deployed via Docker or Kubernetes in a single container. It adds minimal decision latency and maintains an tamper evident audit chain using SHA-256 hashing, ECDSA signing, and RFC-3161 timestamp anchoring. This ensures records that can be independently verified by external auditors and regulators. The architecture is model-agnostic, functioning upstream of any AI model without reliance on specific providers. It supports consistent governance policies across different models, including OpenAI, Anthropic, and local models.

Every decision is reproducible, with full scoring breakdowns provided. The deterministic engine ensures that identical inputs produce identical outcomes over time. The system aligns with EU AI Act requirements for risk management, transparency, human oversight, and accuracy.