Behavioral Observability
The industry is obsessed with "Correctness". We are obsessed with Character.
Traditional LLM evaluation focuses on three things:Factuality, Syntax, and Safety. While critical, these metrics are table stakes. They don't tell you if your AI is actually doing its job in a way that represents your brand or environment.
The Character Gap
When an LLM goes "off-script", it doesn't always start hallucinating fake facts. More often, its personality drifts. A support bot might become subtly passive-aggressive. A sales assistant might lose its "Assertiveness". A technical tutor might become too "Agreeable", agreeing with a user's wrong logic instead of correcting them.
These behavioral drifts are invisible to traditional monitoring tools but are immediately felt by your users.
The Lindr Insight
"We believe that LLM performance is a spectrum of 10 primary behavioral dimensions. By monitoring the distribution of these dimensions, we can detect catastrophic model failure hours before a human reviewer would spot it."
Why Monitors need Personas
Observability tools have long monitored Metrics (latency, error rates). Lindr introduces the concept of monitoring Personas. A Persona is a mathematical target across 10 dimensions. When your production model drifts from this target, Lindr triggers a "Behavioral Alert".
Character Consistency
Ensure your AI maintains its specific voice across different models (e.g. GPT-4o vs Claude 3.5 Sonnet).
Quantifiable Tone
Turn "vibe checks" into hard engineering data that you can plot on a dashboard.