ECP0 provides structured audit, traceability, and tone risk analysis for LLM-generated outputs. A deterministic governance layer for mission-critical AI systems.
Analyze historical logs to identify long-term tone drift and systemic risk patterns.
Deterministic rule-based logic that explains why an output was flagged.
Reduces 'AI judging AI' noise by using high-performance logic modules.
Standardized JSON trace outputs designed for integration into existing engineering workflows.
Audit chatbot logs to identify high-risk tone patterns.
Debug model outputs using structured trace logs.
Detect recurring issues in prompt or policy design.
{
"manipulation_detected": true,
"type": ["coercive_tone"],
"confidence": 0.76,
"hits": [
{ "module": "TX_04", "score": 0.91 }
],
"trace": "eval_node_7 -> tx_04 -> flag_urgent"
}
Telemetry first. See the problem with high-fidelity before deciding how to govern.
Focus on the core governance primitives that matter most for production systems.
Reduce uncertainty from LLM-based moderation using verifiable, rule-based execution.