How NNAISENSE Built an AI-Powered Hedge-Fund And

Building smarter financial forecasts

NNAISENSE, a hedge fund startup, uses AI to predict market movements and create multi-strategy investment portfolios. Their goal is to feed generative models vast amounts of wide-ranging financial data to produce forecasts that guide better decisions to give them an edge.

Jonathan Masci, Co-Founder and Chief Scientist for Deep Learning, said, “We wanted to bring innovation and efficiency into financial forecasting, but the complexity of standard AI infrastructure was a real barrier.”

Infrastructure overhead and distractions

The startup faced a daunting task: building and maintaining its own technology infrastructure. "We were spending too much time fixing infrastructure issues instead of focusing on developing and improving our financial models," explained Masci. Custom setups with Kubernetes, persistent storage issues, and dependency management presented significant challenges, resulting in inefficiencies and bottlenecks that slowed progress and threatened their ability to compete in a fast-moving market. The team sought a solution that could streamline these tasks and allow them to focus on what mattered most, model development and strategic innovation.

A catalyst for efficiency

To get past these challenges, NNAISENSE adopted Lightning AI to reduce infrastructure friction and simplify workflows. They now had a seamless layer over cloud services to help the startup offload the infrastructure management. By adopting this tool, the team aimed to simplify their training and experimentation processes, reduce dependency on individual expertise, and improve workflow efficiency. “Lightning AI allowed us to move from setup to strategy,” said Vojtech Micka, Software Infrastructure Lead. “We also appreciated having a responsive support team ready to help when we hit snags.”

The tool acted as a quiet partner, letting the team focus on modeling and innovation without getting bogged down by infrastructure details.

Cutting costs by 35%

The transition was not without its bumps, yet the payoff was clear. With Lightning AI’s persistent studios and adaptable infrastructure management, NNAISENSE cut costs by 30–35% in the first year.

These savings freed up budget and time, allowing the team to direct more energy toward developing financial models and strategic innovation.

Moving toward market-ready models

Well past the experimental phase, the startup is laying the groundwork for an investment fund backed by model-driven insights. With Lightning AI, the team has shifted decisively towards the future, confidently moving from experimentation to execution. "The ability to easily scale from development to testing, to extensive market simulations is a game-changer," Masci acknowledged.

This dashboard is NNAISENSE's internal tool where they can analyze predicted scenarios of cumulative return for all companies. This particular page shows an example of such predictions for Microsoft.

Scaling with purpose

As NNAISENSE prepares to tackle the demands of quantitative trading, they’re stepping into a competitive arena where speed and precision define success. The opportunity to reshape financial markets with AI-driven strategies is enormous, and the stakes are high for a young startup.

“Our goal is clear: to adopt AI not just to keep up, but to lead,” Masci said. “By streamlining our infrastructure with tools like Lightning AI, we’re positioning ourselves to move fast, innovate, and take on opportunities that traditional firms can’t.”

With this strong foundation, NNAISENSE is poised to scale models and data into a real-world advantage. They remain focused on their mission to use AI for smarter, more transparent, and responsible investment decisions, ultimately creating value that benefits both their clients and the broader market.

How NNAISENSE Built an AI-Powered Hedge-Fund And Cut Infrastructure Costs By 35%