Our mission
Financial AI requires real judgment, not generic data
The missing layer
Finance runs on expertise shaped through execution, analysis, and judgment under pressure. That expertise has never been structured into intelligence that AI systems can learn from or be evaluated against
The intelligence layer
Proxion exists to define that layer. We transform institutional financial expertise into structured training and evaluation data for the AI systems reshaping finance
The problem
AI systems are being trained on the wrong financial data
Financial AI is trained to retrieve, not decide
Filings and transcripts teach AI systems to retrieve information. They do not teach how to evaluate a credit covenant, stress test an LBO or assess a capital structure under distress. Those tasks require judgment that has never been captured at scale
Expert judgment does not scale through data alone
Financial judgment is built through years of deal execution, credit analysis, and high stakes decision making. It cannot be extracted from documents or replicated without domain professionals. Data volume is not a substitute for the expertise required to produce it
Without finance professionals, evaluation breaks
Evaluation requires judgment shaped by direct execution experience. It cannot be replicated through generalist review or inferred from documents alone. Without finance professionals involved, models are evaluated against the wrong standard and fail where it matters most
Our approach
Depth over credentials
We work with finance professionals from bulge bracket banks, elite boutiques, and top-tier private equity and credit funds. The standard is direct, hands-on experience in the domain being modeled, not general finance literacy
Structured from the ground up
Financial intelligence requires structured tasks, explicit rubrics, and expert-verified ground truth. Every output is designed for training and evaluation, not generic annotation
Infrastructure, not services
We are building a scalable intelligence layer for financial AI. Every task, rubric, and expert evaluation compounds into a system that improves over time
Our operating principles
Quality is the product
One expert reasoning trace generates more signal than thousands of shallow annotations
Structure is not optional
Only structured tasks with explicit criteria teach systems to reason
Finance expertise is tacit
Judgment is built through execution and cannot be crowdsourced or learned from documents
Built to perform at institutional standards
Rigorous, auditable, and engineered for consistency across every engagement and at any scale
The infrastructure for financial AI does not exist yet
AI is moving into high-stakes domains where reasoning determines outcomes. The required level of rigor for financial AI is missing. Proxion closes that gap.