I’ve been building Nomas Research(https://nomas.fyi), a financial data platform designed specifically to make/sanitize financial data easier for AI agents and LLM applications to consume.

The main idea is that traditional financial APIs and websites are generally designed for humans or conventional software. I wanted to make the underlying data much more usable for AI workflows such as RAG, agentic research, training, and automated financial analysis.

What’s available

• AI-agent-ready financial data — structured data that can be directly fetched and consumed by AI agents without having to scrape or parse financial websites.
• SEC/company financial data — structured financial information derived from SEC filings and taxonomies.
• Insider trading data — insider transactions are updated with less than ~1 minute of delay, so agents can work with near-real-time insider activity.
• API access — designed for programmatic access rather than just browsing dashboards.

I’m particularly interested in feedback from people building financial RAG systems, AI agents, financial LLMs, or datasets for model training.

The site is here: https://nomas.fyi

If you’re working on something in this area, I’d be interested to hear what financial data is currently difficult for your agents/models to access or understand.

Everything is hosted on AWS(ECS, RDS and so on)

Let me know what do you guys think. Any suggestion is welcome