A behavioral indexer for AI models: 29 metrics across 7 pillars, from truth and grounding to sycophancy checks, persuasion rates, and creative spark.
The AI evaluation space is broken. We're measuring the wrong things, in the wrong ways, and calling it science. MMLU, GPQA, HellaSwag — these benchmarks tell us how well a model can memorize and regurgitate, not how it behaves in the wild when it matters.
Looks good on paper. Academic benchmarks that measure narrow capabilities in controlled environments.
Actually works in practice. Real-world behavior that matters when humans interact with AI systems.
The protocols that survived and scaled weren't the ones with the most elegant whitepapers. They were the ones where performance became visible and trusted.
The platforms that dominated weren't just technically superior—they made their value visible and measurable to users and stakeholders.
We need metrics that capture behavior, not just capability. We need evaluation frameworks that make performance visible and trusted, not just statistically significant.
Built governance systems that needed to be both technically sound and socially legible
Created proof layers that made performance legible to investors and users
Applying the same lens to AI: measuring behavior, not just capability
Adoption only happens when performance is visible and trusted.We're building the behavioral indexer that makes AI performance visible, measurable, and trustworthy.
We started in crypto, building governance systems that needed to be both technically sound and socially legible. The challenge wasn't just making decisions—it was making decisions that stakeholders could understand, verify, and trust.
“Adoption only happens when performance is visible and trusted”
This insight carried forward into infrastructure. We built proof layers that powered multimillion-dollar raises, not because the math was novel, but because the proof systems made performance legible to investors and users alike.
Now we're applying the same lens to AI. The models are getting more capable, but we're still measuring them like they're academic exercises. We need to measure them like they're products that humans will actually use and depend on.
EOTM V1 measures 29metrics across7pillars, from truth and grounding to sycophancy checks, persuasion rates, and creative spark.
Each run outputs legible metric cards and side-by-side comparisons you can parse in seconds, plus JSON evaluation cards you can ship to investors, customers, or regulators.
Measures factual accuracy and grounding
Tracks influence and argument quality
Evaluates emotional intelligence and empathy
Unlike traditional evaluation frameworks that treat AI as a black box, EOTM V1 measures behavior, not just capability. We're not asking “Can the model do X?” We're asking “How does the model behave when it does X?”
Our metrics are designed for real-world deployment, not academic papers. They're interpretable by non-technical stakeholders, auditable by regulators, and actionable by product teams.
Get access to the EOTM V1 dashboard and start measuring AI behavior that matters.