Hidden Relationships
Competitive advantage through uncovering the unseen
No one can know everything. No one can read everything. But can you give somebody insight into something hidden? That’s a new kind of competitive advantage. The challenge for decision-makers today isn’t just that the important relationships and trends are hidden from view under piles of data. It’s that the existing tools for sifting through that data show you what was important at one time. They tell you what everybody else already knows. You need a tools to uncover and predict the future of those hidden relationships.
Evolution
Harnessing the power of complexity and convergence
Our AI solutions are conceived out of our lifelong drive to understand how ideas evolve and how to make smarter, more timely choices about the ideas that deserve greater investment. So we deeply understand the frustrations encountered by professionals in investing, R&D management, and adjacent fields as they attempt to use existing tools to suss out promising concepts or identify possible new investments or research programs. Consilience AI solutions are able to identify the major convergence of ideas across domains and forecast how those ideas would co-evolve.
Engineering
Exploiting data and technology at scale
Disciplined engineering enables us to exploit data and technology at scale. Recent AI technology has had manifold advances in the ability to process and analyze semi-structured and unstructured data at scale through AI/NLP and pattern recognition. This allows to uniquely prototype all possible ideas which means the best ideas win no matter where they come from. These ideas are implemented in standardized, repeatable, and reproducible processes to ensure consistency and quality.
Why We’re Here
Embracing Complexity: The Intersection of Math, Science, and Language for Revolutionary Insights
We dwell at the places where math, physics, biology, philosophy, and art overlap, and we have a culture that emphasizes hard engineering over traditional computer science or IT. We embrace complexity as the source of our opportunity. We care about reliability, so we reject statistical methods that require the use of samples, estimates, and uncertainties and that require a host of assumptions that break down in the real world. We care about explainability, so we employ explainable mathematics and exclude methods that are subject to all the biases inherent in their training data. We care about ease of use, so we are obsessive about enabling real-world outcomes with intuitive solutions accessible to users with varying levels of expertise.