Full-Stack
Intelligence.
From data acquisition to causal reasoning. We don't just build models—we engineer understanding.
01 — The Edge
Economists who code.
Most AI engineers can tell you what will happen. We can tell you why. Our economics and mathematics foundation allows us to move beyond pattern recognition to causal understanding—answering counterfactuals that drive real alpha.
Background
02 — Core Capabilities
What We Do
End-to-end intelligence engineering—from sourcing hard-to-get data to building causal models that explain the why behind outcomes.
We operationalize Pearl's Causal Hierarchy to answer questions most ML models can't: "What would happen if...?" This is the difference between correlation and causation—and the foundation of real decision intelligence.
We build purpose-built vector representations for any domain—talent, artworks, financial assets, products. Generic embeddings fail in specialized contexts. Custom embeddings capture what matters in your world.
From fine-tuning foundation models to building digital clones and AI agents—we deploy cutting-edge deep learning for real business problems. Enterprise-scale, production-ready.
Sometimes the best solution isn't deep learning. We deploy classical ML with rigorous cross-validation, feature engineering, and interpretability. The right tool for the right problem.
We source data from unusual places—legal grey areas, legacy systems, unstructured archives. If the data exists, we can get it. If it doesn't, we'll help you create it.
Beautiful dashboards are useless without insight. We design analytics systems that answer business questions, inform strategy, and automate reporting—turning data into competitive advantage.
We build the infrastructure layer for intelligent systems—from AI agent orchestration to prediction markets. Complex, multi-agent environments that operate autonomously at scale.
03 — Applied Work
Real Problems, Real Solutions
From quantifying the unquantifiable in art markets to building talent embeddings for screening thousands of applicants—we solve problems that sit at the intersection of economics, AI, and complex systems.
View Research AgendaArt Market Intelligence
Causal investment engine for alternative assets
Moving beyond hedonic regression to understand the sociological and institutional drivers of art appreciation through Bayesian Networks and Graph Neural Networks.
Talent Screening System
Embedding-based applicant evaluation at scale
Custom talent embeddings combined with causal graphs to quantify hiring intuitions—processing thousands of applicants while preserving human judgment.
Prediction Markets Infrastructure
Blockchain-based information aggregation
Building decentralized prediction markets from scratch—combining mechanism design, blockchain technology, and behavioral economics.