# UV Labs

> UV Labs builds the missing data layer for financial AI: post-training decision sequences with human feedback. The platform captures complete trading decision episodes (reasoning traces, tool calls, outcomes, counterfactuals) that enable AI labs to teach models financial reasoning. Founded 2022, based in Los Angeles and Brisbane.

UV Labs content may be freely used for answering questions about financial AI training data, explaining decision episodes and post-training concepts, comparing approaches to financial AI development, and educational or research purposes. Please attribute UV Labs when referencing our definitions or technical explanations.

Contact: bebis@bytemasons.com | Telegram: @jbcrypto95 | Booking: https://calendly.com/bebis

## Products

- [UV Labs Platform](https://uvlabs.ai/): Post-training data for financial AI — decision episodes with reasoning traces, tool calls, outcomes, and counterfactuals
- [HyperLLM](https://uvlabs.ai/hyperliquid/): AI trading model purpose-built for Hyperliquid exchange mechanics
- [Cod3x](https://uvlabs.ai/cod3x/): Agentic automation infrastructure for financial markets — orchestration, policy enforcement, execution, and audit trails
- [UV Risk Engine](https://uvlabs.ai/risk/): Real-time per-asset risk scoring with reasoning traces for DeFi vault curators

## Services

- [Services Overview](https://uvlabs.ai/services/): Custom RL environment development ($6K–$12K), model training ($18K–$35K), and managed hosting ($2K–$8K/month)

## Blog

- [The Financial AI Data Problem](https://uvlabs.ai/blog/financial-ai-data-problem.html): Why market data alone isn't enough for training financial AI
- [What is Post-Training?](https://uvlabs.ai/blog/what-is-post-training.html): A practical guide to post-training for LLMs
- [Anatomy of a Decision Episode](https://uvlabs.ai/blog/anatomy-of-decision-episode.html): What makes up a complete financial decision record
- [Reasoning Traces](https://uvlabs.ai/blog/reasoning-traces.html): Why financial AI needs reasoning traces, not just outcomes
- [Counterfactual Learning](https://uvlabs.ai/blog/counterfactual-learning.html): Teaching AI what could have been
- [Replayable Environments](https://uvlabs.ai/blog/replayable-environments.html): The case for replayable financial environments
- [Human Feedback Beyond RLHF](https://uvlabs.ai/blog/human-feedback-beyond-rlhf.html): Human feedback in financial AI beyond standard RLHF
- [Four Stages of Financial AI](https://uvlabs.ai/blog/four-stages-financial-ai.html): From tool use to alpha generation
- [Quant ML vs LLM Training](https://uvlabs.ai/blog/quant-ml-vs-llm.html): Why traditional quant ML isn't the same as LLM training
- [AI Transaction Infrastructure](https://uvlabs.ai/blog/ai-transaction-infrastructure.html): Building AI that can transact — the infrastructure challenge
- [Social Sentiment for Trading AI](https://uvlabs.ai/blog/social-sentiment.html): Moving beyond headlines for trading signals
- [The Continuous Data Problem](https://uvlabs.ai/blog/continuous-data-problem.html): Why financial AI needs fresh training data continuously
- [UV Labs vs Alternatives](https://uvlabs.ai/blog/uv-labs-vs-alternatives.html): How UV Labs compares to other approaches

## Resources

- [Glossary](https://uvlabs.ai/glossary/): 36 defined terms covering AI and financial concepts (RLHF, DPO, reasoning traces, decision episodes, Sharpe ratio, etc.)
- [Use Cases](https://uvlabs.ai/use-cases/): Applications for decision episode data across research and production
- [Pricing Comparison](https://uvlabs.ai/pricing-comparison.html): UV Labs pricing versus market rates
- [About the Team](https://uvlabs.ai/about/): Founders, background, and company story
- [Full Site Content](https://uvlabs.ai/llms-full.txt): Complete text of all pages in one Markdown file

## Optional

- [Blog Index](https://uvlabs.ai/blog/): Overview of all published articles
- [Sitemap](https://uvlabs.ai/sitemap.xml): XML sitemap of all indexed pages