EVE AI Core
The Infrastructure of No.
EVE AI Core
The Infrastructure of No.
How EVE AI Core compares to alternative AI governance approaches. Deterministic pre-inference enforcement versus probabilistic post-inference filtering.
You control which decision fields are sent; in VPC and on-prem deployments, governed content and evidence stay inside your boundary. As EU AI Act obligations phase in and SR 11-7 examiners ask for model-decision evidence directly, the record has to exist at the moment of the decision. Trust Center · Security
Framework mappings describe technical control support and evidence capabilities. They do not constitute legal advice, regulatory certification, or a guarantee that a customer’s deployment is compliant. “SOC 2 readiness” is a preparation status, not a completed audit.
| Feature | EVE AI Core | NeMo GuardrailsNVIDIA | Guardrails AI | Lakera Guard | Manual Review |
|---|---|---|---|---|---|
| Enforcement Model | Deterministic Pre-inference, fail-closed | Probabilistic LLM-based rail execution | Rule-based Validator framework | ML-based Classifier models | Human Subject to fatigue, bias |
| Governance Latency | <1ms Pure deterministic logic | 50 – 200ms Additional LLM call per rail | 10 – 50ms Validator chain execution | 5 – 20ms API round-trip | Minutes – Hours Queue + human review |
| Hardware Enforcement | Roadmap PolarFire SoC FPGA target — software today | No | No | No | N/A |
| Pre-Inference Scanning | Yes Prompt Firewall + CRD scoring | Partial Input rails (often LLM-based) | Partial Input validators available | Yes Prompt injection detection | N/A |
| PII Redaction | Built-in (10 types) Regex + entity recognition | No Requires custom rail | Partial Via community validators | No Focused on prompt defense | Manual Reviewer-dependent |
| Cryptographic Proof | Ed25519 Signed decision certificates | No | No | No | No |
| Audit Trail | Hash-chained Tamper-evident, 7+ yr retention | Basic logging LangSmith integration | Basic logging Validation history | Basic logging Dashboard + API logs | Spreadsheets Manual record-keeping |
| Policy-as-Code | JSON-compiled rules 15 immutable charter rules | Python code Colang DSL + Python | Python / RAIL RAIL XML specification | Dashboard Web UI configuration | N/A |
| Multi-Turn Detection | Cumulative scoring Cross-turn CRD aggregation | No Per-turn rail evaluation | No Per-output validation | No Per-request classification | No Single-response review |
| Model Drift Detection | Fingerprint-based Identity invariant tracking | No | No | No | No Undetectable at scale |
| Data Residency | Multi-region enforcement Per-tenant region pinning | No Self-hosted only | No Self-hosted only | No EU + US regions | Manual Requires process enforcement |
| Cost Governance | Per-tenant budget caps Metering + quota enforcement | No | No | No | Manual Budget tracking in spreadsheets |
| Regulatory Compliance | Auto-mapping EU AI Act / NIST / ISO 42001 | No General-purpose framework | No General-purpose framework | Partial OWASP LLM Top 10 coverage | Manual Requires compliance team |
| Patent Protection | 90 filed U.S. provisional applications Provisional + utility filings | Open source Apache 2.0 license | Open source Apache 2.0 license | Proprietary Closed-source SaaS | N/A |
| Red Team Score | 21/21 Internal adversarial regression suite | Not published | Not published | Not published | N/A |
Most AI governance tools operate as post-inference filters. The model generates a response, then a secondary system scans the output for problems. This creates a fundamental gap: the harmful content has already been generated, already consumed compute, and the filter itself may miss edge cases or novel attack patterns.
EVE AI Core takes a fundamentally different approach. Governance enforcement happens before inference begins. The Three-Layer Trust Infrastructure physically isolates the Authority Resolution Layer (authority packs, charter enforcement, veto logic) from the Governed Inference Layer (model inference). This means:
If the governance system fails, the request is blocked. There is no code path where an unscanned prompt reaches the model. By contrast, a post-inference filter that crashes can fail open, allowing an unscanned response to ship unless it is explicitly designed to fail closed.
Charter rules compile to deterministic logic that executes in under 1ms. The same input under the same policy version produces the same governance decision. There is no confidence threshold to tune and no false-negative rate to accept.
Because the veto logic is pure and side-effect-free, it compiles to FPGA firmware. A PolarFire SoC is the target for enforcing the same 15 charter rules in hardware, with the design goal of a governance boundary that software cannot bypass. This hardware path is on the roadmap; today the veto core runs deterministically in software.
Every governance decision produces an Ed25519-signed certificate. Auditors can verify that a specific decision was made by a specific version of the rules at a specific time, without trusting the operator's logs.
Deterministic governance compiles policy into fixed logic that returns the same allow, modify, or block decision for the same input under the same immutable policy version every time, with no confidence threshold to tune. Probabilistic governance, used by most LLM-based guardrails, scores outputs after generation, so the same input can pass or fail depending on the model, and harmful content may already have been produced.
They use fundamentally different architectures. NVIDIA NeMo Guardrails runs configurable rails around the model — input rails (typically LLM- or classifier-based) run before the LLM, and output rails run after generation. EVE AI Core enforces deterministic charter rules before inference in under 1ms and fails closed, so an unscanned prompt can never reach the model. EVE is built for regulated, audit-grade use cases; NeMo is a flexible open-source rail framework.
Guardrails AI is a rule-based validator framework and Lakera Guard provides runtime AI security screening across prompts, outputs, tool calls, tool responses, and agent workflows. Both run as software filters without a hardware-targetable enforcement design or signed, replayable decision records. EVE AI Core adds pre-inference scanning, fail-closed enforcement, an FPGA-targetable design (hardware enforcement is a roadmap item), and cryptographically signed decision certificates for independent audit.
Pre-inference enforcement governs a prompt before the model generates anything. If governance fails or a charter rule is violated, the request is blocked before any tokens are produced, unlike post-inference filters, which scan output only after the model has already generated a response and paid the compute for it.
Yes. Because EVE AI Core's veto logic is pure and side-effect-free, the same 15 charter rules are compilable to FPGA firmware on a Microchip PolarFire SoC -- the target for a hardware governance boundary that software cannot bypass (today the veto runs deterministically in software). NeMo Guardrails, Guardrails AI, and Lakera Guard are software-only.
EVE AI Core's governance decision executes in under 1 millisecond because it is pure deterministic logic with no extra LLM call. Approaches that add an LLM-based rail, a validator chain, or a classifier API call add materially more latency per check; published figures vary by vendor and configuration.
Try the live demo to see deterministic governance enforcement operating in real time.
Launch DemoArchitecture comparison based on publicly available vendor documentation; competitor capabilities evolve quickly, so verify current vendor docs before relying on any row. NeMo Guardrails supports input, dialog, retrieval, execution, and output rails (input rails run before the LLM); Lakera Guard provides runtime screening across prompts, outputs, tool calls, and agent workflows. Latency figures are typical/published estimates, not independent EVE benchmarks. Last reviewed July 2026.