EVE AI Core
EVE AI Core
Technical guides, architecture references, and compliance documentation for AI governance enforcement. Learn how deterministic policy enforcement works and how to integrate CoreGuard into your stack.
The definition, purpose, and architecture of a dedicated enforcement layer between your application and LLM providers.
Why regulated industries need deterministic enforcement — and why LLM-based safety filters cannot satisfy compliance requirements.
Governance frameworks define policy. Enforcement layers execute it. Understanding the difference is critical for compliance teams.
PEP/PDP patterns, integration options, latency budget, policy versioning, and audit log schema for production deployments.
Complete REST API documentation including request/response schemas, Python SDK quickstart, authentication, and rate limits.
NIST AI RMF, ISO 42001, EU AI Act, and OECD principles compared — and where CoreGuard fits as the enforcement layer.
How CoreGuard detects and blocks biased AI decisions at the policy evaluation layer — ECOA, Reg B, and disparate impact compliance.
Step-by-step integration patterns for connecting CoreGuard to your LLM pipeline — SDK setup, webhook configuration, and policy pack deployment.
Deploy the egress governance gateway: fail-closed enforcement, signed decision certificates, and the Kubernetes sidecar pattern with non-bypass iptables routing.
How CoreGuard's signed decision certificates and tamper-evident audit log satisfy examiner and auditor requirements for AI governance records.
Complete reference for CoreGuard policy packs — built-in industry packs (lending, healthcare, HR) and how to author custom packs for your compliance framework.
How CoreGuard's signed, tamper-evident decision certificates work — schema, verification API, retention requirements, and auditor verification workflows.
Full API reference for the eve-coreguard Python SDK — installation, client setup, evaluate(), streaming, async usage, and error handling.
How the EU AI Act's Article 9 requirements for High-Risk AI systems demand enforcement, not just logging.
Federal Reserve model risk requirements and why financial institutions need deterministic enforcement for LLM deployments.
Pre-deployment checklist covering ECOA, HIPAA, SR 11-7 requirements and the enforcement layer gap most deployments miss.
A technical comparison of AI safety tools. Which layer solves which problem — and why regulated enterprises need deterministic enforcement.
Get a sandbox API key and live walkthrough in your environment within 24 hours.
Request Demo Access