EVE AI Core
EVE AI Core
CoreGuard evaluates every AI decision and model update against your regulatory policies — ECOA, Reg B, SR 11-7, HIPAA — deterministically, before it executes, and signs a record your compliance team can replay years later. Evidence, not claims.
Every AI request passes through deterministic authority resolution. Trust is computed, not assumed, before any token is generated.
Post-hoc filtering cannot undo a committed action. By the time an LLM outputs a decision, trust has already been assumed — not verified. EVE resolves authority before the model touches a token.
Patent-pending applications cover the complete stack: a cryptographically authoritative trust runtime that resolves authority, routes intelligently, and attests every decision before execution — independent of model behavior.
The CoreGuard trust runtime intercepts every AI action, resolves authority against your governance corpus, and returns a signed ALLOW / BLOCK / MODIFY verdict with cryptographic provenance — before the model produces a single token.
EVE CoreGuard deploys in lending, healthcare, and enterprise AI where governance must be deterministic, not probabilistic — and where post-hoc filtering is not a defensible compliance posture.
Governance decisions are cryptographically bound at issuance. The authority chain is independently replayable — years after the decision, by any verifying party, without EVE involvement. Trust is in the chain, not the system.
Architecture specifications, compliance integration guides, and enforcement pattern documentation for regulated AI infrastructure teams.
Engineering a governance gate that runs before every LLM call, adds under 1ms of latency, and produces the same verdict for the same input every time.
A practitioner's guide to Article 9 risk management and enforcement requirements under the EU AI Act.
Step-by-step integration guide — REST API, Python SDK, and sidecar proxy deployment patterns.
Federal Reserve model risk requirements and why financial institutions need deterministic enforcement for LLM deployments.
A trust substrate is the infrastructure layer that makes governance guarantees structurally true — not policy-dependent, not configuration-dependent, not semantically dependent. EVE is that layer for AI systems.
Charter rules produce identical outputs for identical inputs — every time, regardless of model behavior, prompt content, or runtime state. Contradictions are structurally impossible, not prevented by configuration.
Trust in EVE's governance decisions does not require trusting EVE. Every decision is signed. Every chain is hash-chained. Any verifying party replays the authority chain independently. Semantic trust is eliminated.
Hard governance invariants survive prompt injection, model hallucination, jailbreak attempts, and infrastructure failure. The enforcement layer operates below the semantic surface — invariant to model output.
Trust in distributed systems has always required infrastructure. TLS doesn't ask you to trust that the server is who it says it is — it provides a cryptographic mechanism that makes the answer computable. PKI provides a chain of evidence that any verifier can traverse.
AI governance has not had an equivalent. Today it relies on semantic trust: you trust the model will follow its prompt. You trust the guardrail will catch edge cases. None of these are structural guarantees.
EVE changes the question from “do you trust this system?” to “can you verify this decision?” The answer is in the chain, not the system.
CoreGuard deploys as a sidecar proxy, REST API, or SDK integration. Deterministic governance corpus, cryptographic authority chain, and independently replayable audit lineage. Contact us for an enterprise architecture review against your regulated AI deployment.