NAPA Is An On-Demand Data Scientist For Fundamental Investors
Your Fundamental Process.
Rigorously Validated by AI.
The Obstacle
Most AI tools boost productivity for qualitative analysis (summarizing and searching text)
Accessing data-driven portfolio insights remains both difficult and expensive
On-Demand Data Scientist For
Fundamental Investors
NAPA fills the gap between
Terminals / Dashboards
Display data without analysis or interpretation
and
Data Science Teams
Deliver expert insights but are limited by bandwidth
Portfolio Managers
Enhance your stock picking with scalable data-backed insights
Quants
Validate signals 100x faster, shifting focus from repetitive coding to high value projects
Risk Officers
Get deeper, more granular risk analytics than dashboards can offer
Analysts
Stress-test financial model assumptions with scientific rigor
Traders
Execute with sharper data-driven precision
Our Proof
We’re not your typical software vendor.
Our team combines world-class PhD Quants and PhD AI/ML Engineers who’ve systematically managed large portfolios. We know how hard it is to refine and adapt investment strategies.
FAQ
Productivity tools for qualitative analysis, such as those that summarize textual data, are abundant. Newcortex offers a new way for generating data-driven insights. Our product is a highly specialized tool for fundamental investors to enhance their portfolio management process.
What’s new or different about your product?
NAPA comes preloaded with premium US Equities market data, fundamental data, and risk models. It can also ingest your proprietary or subscribed datasets through a variety of plugins and adaptors.
What data can your system ingest?
Built-in enterprise-grade encryption on all data flows. Zero-retention policy with all third-party foundation models means your data is never stored externally.
How do you ensure security and privacy?
We work with early adopters who believe AI will be a key strategic advantage in generating alpha. Our team offers high-touch onboarding and support, as well as trial programs for demonstrating use cases.
How do I get started?