Four independent verification layers. A response must pass all four — bypassing one does not bypass the others.
Most AI guardrails detect-and-alert after bad output ships. G8KEPR's Verification Engine is BLOCK-capable — sitting at middleware position 14, after generation and before delivery. Conversation integrity, source grounding, tool-call structure, and output constraints — each evaluated independently with explicit failure modes.
The last line of defense between AI outputs and your users
Every AI response, API output, and MCP tool result passes through the Verification Engine — after generation, before delivery. Four independent layers validate structure, grounding, tool-call shape, and output constraints. Bypass-resistant by design.
AI systems hallucinate confidently, leak PII, violate business rules, and emit malformed data. Without an intercept-and-block stage between generation and delivery, these errors reach users and damage trust.
Four independent layers catch errors before they reach users
Bypassing one layer does not bypass the others. A response must pass all four.
When multiple constraints fire on the same response, the highest-priority action wins. A BLOCK from one constraint overrides a WARN from another. Constraints are AND-combined — a response must satisfy all of them.
Five constraint types let an organization express any business rule. Version-controlled per org with full audit trail. For JSON outputs, enum values are injected into the prompt before generation AND validated via structured-output schemas — inject-then-validate reduces blocks at runtime.
Five capabilities that exist in the platform — not in detect-and-alert tools, not in schema-only validators, not in the LLM itself.
Conversation Integrity → Source Grounding → Structural → Output Constraint. Each emits PASSED / WARNED / FAILED / SKIPPED / ERROR / TIMEOUT. Failure in one is not failure in all.
verification_engine/Most validators only block. SUGGEST blocks AND returns a proposed compliant alternative the caller can accept — turning rejection into self-healing.
constraints/enforcer.pyEnum values injected into the LLM prompt BEFORE generation (steers the model) and validated via structured-output schema after. Reduces block frequency at the source.
function_calling · tool_useTraces factual claims back to actual source documents and tool outputs. Catches hallucinated URLs (that do not exist in source), invented financial figures, and fake citations.
grounding_verifier.pyConversation state is hash-chained. Tampering with prior turns breaks downstream hashes — the engine sees the manipulation, not just the symptom.
guardrails_provenance.pyProtect your users from AI errors — in every scenario
Verify ChatGPT, Claude, and Gemini responses before showing to users. Detect hallucinations, enforce output formats, redact accidental PII leakage, and block off-topic responses.
Validate results from MCP tools (file reads, database queries, API calls) before AI agents process them. Prevent data poisoning and ensure tool outputs match expected schemas.
Verify your API responses before sending to clients. Enforce schemas, detect data quality issues, and ensure compliance with API contracts and SLAs.
Everything you need to know about output verification
Questions about implementing verification?
Talk to our verification experts →Every layer score, hallucination risk, citation accuracy, and schema-validity record is written to verification_results and chained into the platform audit log. Mappings are pre-built — auditors get exports, not spreadsheets.
Subject to independent audit and attestation. G8KEPR provides the technical controls and evidence — your auditor issues the certification.
Four independent layers, four enforcement priorities (incl. SUGGEST), two-pass schema enforcement, and citation-grounded hallucination detection — at middleware position 14, before delivery.
No credit card required • 30-day free trial • Full verification pipeline