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Keye is an AI software that lets private equity firms do due diligence for acquisitions faster and more accurately. Our customers can evaluate 10x more deals and fund better companies which leads directly to better returns/more alpha. -- Our system is the first of its kind to instantly generate complex, comprehensive data packs from raw data in any format, directly from the data room. This enables investors to gain an 80% understanding of a potential deal within moments, empowering them to say No to a deal earlier in the process and focus on high-potential opportunities. Traditionally, creating these detailed data packs—like cohort or retention analyses and customer roll-forwards—takes weeks, during which critical details may be overlooked, potentially costing funds millions in missed insights and poor decisions. -- Keye combines cutting-edge AI with deep private equity expertise to deliver powerful, multi-layered insights that go far beyond the simple summarizers and search functions offered by other tools. Our platform dives deep into both quantitative and qualitative due diligence data, offering high-impact analytics crafted specifically for private equity workflows. This isn’t just automation—it’s a purpose-built solution that uncovers actionable insights, fueling smarter decisions and driving true alpha.

Historically, quantitative models are domain specific. Brilliant people spend their best years testing features, tuning hyperparameters, and iterating architectures within a narrow domain. But scale is the panacea: large models will find patterns people, and specialized models, could not. Forecasting generalizes. Zoa trains cross-domain event forecasting engines. *Automating Iteration* LLMs—embedded in multi-agent optimization loops and evaluated against fixed policies—can automate the build-test-improve modeling cycle. Think AlphaEvolve for forecasting problems. *Sample-Efficient General Models* Today’s forecasting models are narrowly crafted with deep human priors. But larger models will outperform state-of-the-art specialized models. Unlike existing event models, our models leverage data from across contexts and rely less on human intuition. And compared to LLMs, our models are built with more inductive priors and rely more heavily on inference-time compute—improving sample efficiency. *Why It Matters* In the real economy, our models could be useful for forecasting supply chain volatility, energy supply and demand, even earthquake risk. Science is, Ian Hacking writes, the taming of chance. It is the process of iteratively updating priors (something like: identify uncertainty, conceive experiment to reduce uncertainty, execute, update). If science is uncertainty-reduction, forecasting is a critical measure of progress. Better forecasting improves our ability to select interesting experiments (roughly those with greatest expected uncertainty reduction) and update priors. Our models will be used by labs and academics in data-heavy domains. Sam's ex-girlfriend introduced him to Greg back at Carnegie Mellon in 2017, and while that relationship didn't last, their friendship has. After college, Greg went to Harvard Law School, while Sam worked for three years at Jane Street on their Options desk, building & leading a satellite dev team.