Infrastructure for the Next Generation of Quant Research
From raw data to execution, we’re building the stack for AI-driven quant research.
AI Infrastructure for Quantitative Research
Edgebot is building the first LLM-native research environment for systematic trading.
We design every layer of the stack — from raw market data to distributed AI training, strategy research, portfolio construction, and execution — to train and evaluate our proprietary LLM.
This model performs the tasks of a quant researcher, enabling research at unprecedented scale.
Purpose-Built Infrastructure
Our systems are built entirely in-house — from real-time data pipelines to high-performance research frameworks and distributed AI training systems, all designed to power quantitative research at scale.
Nothing is outsourced. Every component is optimized for discovery, testing, and execution — and to enable our proprietary LLM to act as an autonomous quant researcher.
Autonomous Strategy Research
Our proprietary LLM acts as a quant researcher, generating and refining trading strategies continuously, exploring vast spaces of market behavior beyond human reach.
It evaluates hypotheses, runs large-scale simulations, and adapts models over time — driving an iterative process of discovery without manual intervention.
This is quantitative research as it should be: fast, scalable, and self-improving.
From Research to Real-World Insights
Strategies generated by our LLM are tested in realistic market environments.
Adaptive systems measure risk, track performance, and feed results back into research — creating a continuous loop of learning and refinement.
The outcome: research validated in practice, not just in theory.
Interested in collaborating with us?
Our research platform is private and invitation-only. We work with partners who share our vision for AI-native quantitative research. Interested in exploring collaboration? Let’s start the conversation.
Express Interest
Interested in partnerships, private funds, or just want to learn more?
Get in touch and let’s connect.