Keep the character recognizable?

Drift detection

When a character starts moving away from its tone or behavioral line, Moonborn can catch it before the response reaches the user.

At the start of a conversation, a character may speak exactly as defined. Thirty turns later, it may have softened into a neutral assistant tone. Drift detection compares new responses against the character baseline and marks the moment when the gap becomes too large.

Limit. Drift detection measures character and behavior consistency. It does not verify factual accuracy on its own.

Voice fingerprint

Each character has a reference signature derived from how it speaks across different scenarios.

Moonborn probes the character with different prompts and turns the resulting speech pattern into a reference signature. Later responses are compared against that signature, so drift is measured instead of guessed.

Limit. When the intended speaking style changes, the fingerprint should be recalculated. If only surface details such as name or location change, it usually remains stable.

Pre-launch stress test

Before a character goes live, Moonborn can run tests designed to pressure it: role-break attempts, contradictions, pressure prompts, and boundary tests.

One test tries to pull the character out of role. Another asks it to contradict a previous answer. Another pushes safety boundaries. The results show where the character holds and where it breaks.

Limit. Built-in tests cover general robustness. Medical, legal, financial, and other regulated domains should add domain-specific test sets and policy controls.

What is measured, what is not

This layer measures whether the character still behaves like the same character. The following areas require separate safety and quality layers:

• Factual accuracy and hallucination control.
• Toxicity, policy violations, and platform safety.
• Real-person impersonation risk.
• PII leakage and data masking policies.