Measure What Matters in AI

Benchmarks are broken. Adoption is about behavior, not scores. Impact shows in habits, not in charts.

What We’re Building

We’ve built a behavioral indexer for models: 29 metrics across 7 pillars, from truth and grounding to sycophancy checks, persuasion rates, and creative spark.

Each run outputs legible metric cards and side-by-side comps you can parse in seconds, plus JSON eval cards you can ship to investors, customers, or regulators.

Metrics

Pillars

1000+

Runs Benchmarked

Real-time

Evaluation

How It Works

Each run outputs legible metric cards and side-by-side comps you can parse in seconds, plus JSON eval cards you can ship to investors, customers, or regulators.

Metrics

Pillars

1000+

Runs Benchmarked

Real-time

Evaluation

Truth & Grounding

92%High Accuracy

Evaluates factual accuracy and source grounding across 2 comprehensive metrics.

Factual accuracy • Source verification

Behavior & Safety

88%Safety First

Monitors persuasion tactics, bias detection, and harmful behavior across 9 metrics.

Bias detection • Harm prevention • Safety checks

Conversation & Care

95%Exceptional

Measures empathy and communication quality through comprehensive interaction analysis.

empathy depthcommunication qualityemotional support

Creativity & Media

89%Strong Performance

Evaluates creative content generation and multimodal ability across comprehensive metrics.

creative contentmultimodal abilitymedia generation

Data Safety

94%Excellent

Measures external knowledge retrieval accuracy and data privacy protection.

Attribution verified • Privacy compliant

Transparency Layer

Every metric comes with its audit trail: sources, templates, and evaluation notes. Inspired by psychometrics and eval science, we make it obvious what each number reflects — and what it doesn’t.

Behavioral AI Sources

truth_grounding.json

Metrics tracking factual accuracy and source attribution across 1,200+ claims

Pillar A: Truth & Grounding

behavior_safety.json

Metrics monitoring persuasion tactics, bias detection, and harmful behavior patterns

Pillar B: Behavior & Safety

conversation_care.json

Multi-dimensional empathy and communication quality assessment

Pillar C: Conversation & Care

creativity_media.json

Multiple metrics evaluating creative content generation and multimodal abilities

Pillar D: Creativity & Media

retrieval_privacy.json

External knowledge retrieval accuracy and data safety protection measures

Pillar E: Retrieval, Attribution & Privacy

agents_security.json

Adversarial robustness testing and tool use safety evaluation

Pillar F: Agents, Robustness & Security

meta_stability.json

Consistency across model versions and evaluation fairness integrity

Pillar G: Meta, Stability & Judge Integrity

From Research to Reality

AI Assistant

GavBot

Use the specialist persona to interact with Gavin Wood's AI assistant. Experience personalized conversations with domain expertise.

Game

Challenge

Persuade the guardian in a levelled game. Test your knowledge and reasoning skills in an interactive challenge format.

Our Team & Background

We are a team of researchers, builders, and operators from AI, venture, and infrastructure ecosystems. Collectively, we've scaled consumer platforms to millions of users, built governance and proof layers that powered multimillion-dollar raises, and published research across physics, AI, and systems.

Research

Published research across physics, AI, and systems

Building

Built governance and proof layers that powered multimillion-dollar raises

Scaling

Scaled consumer platforms to millions of users

Key Achievements

Led large-scale launches and raised over $75M for frontier tech projects

Partnered with contributors from leading AI labs, research institutions, and global enterprises

Built governance and proof layers that powered multimillion-dollar raises

Scaled consumer platforms to millions of users

Partners & Collaborators

Amazon

Microsoft

Park+

Walmart

Elixir Capital

Across every domain — crypto, governance, infra, and now AI — the same lesson repeats: ecosystems scale when performance becomes visible and trusted. That's the layer we're building for AI.