Hi NSigma team, thanks for joining us today. To get started, could you briefly explain what NSage.ai is and which challenge it helps financial professionals solve?
NSage.ai is an AI-native and cloud-native software platform that enables analysts to quickly and accurately forecast key financial indicators (like EPS, revenue, and EBITDA) for companies listed on US, EU, and Asian exchanges. It addresses the lack of advanced quantitative tools in smaller and mid-sized investment firms, helping them overcome the inefficiencies and errors associated with manual, traditional forecasting.

Investment teams spend huge amounts of time cleaning data and rebuilding models. What were the biggest pain points you saw that pushed you to build this platform?
What really stood out was how much time teams were losing on repetitive, low-value work like cleaning data and rebuilding the same models over and over. Most were relying on static Excel files and consensus assumptions that don’t adapt well to new information or real-time signals. That left very little time for actual investment thinking, and that imbalance was the main reason we decided to build this platform.‍

For a mid-sized investment firm, what does a “before vs after” look like when NSage.ai becomes part of their workflow?  
‍Before: Senior analysts spend hours or days manually gathering data from balance sheets and transcripts to rebuild Excel models every quarter.
After: Firms achieve a 90% time savings in quarterly analysis. They gain institutional-quality quantitative insights "out-of-the-box" without the massive overhead of hiring an internal team of data scientists.

‍Accuracy and trust are critical in finance. How do you evaluate forecast quality and reduce the risk of misleading conclusions?
We evaluate models against real-world quarterly data and use a multi-model ensemble where a few specialized algorithms compete to find the most accurate fit for each specific KPI. To prevent “black-box” risks, we provide SHAP visualizations that explain exactly which variables impacted a prediction, ensuring every forecast follows economic logic constraints. In addition, we use an explainability agent that assesses the confidence of each prediction by analyzing the strength, consistency, and reliability of the underlying input signals.

What makes NSage.ai different from other analytics, forecasting, or “AI assistant” tools used in investment research today?
Unlike generic tools or single-model APIs, NSage.ai uses a multi-agent architecture where dedicated agents handle feature vetting, model tuning, and narrative reporting. It doesn't just average guesses (like consensus tools); it dynamically selects the best-performing model for a specific company and provides transparent, explainable insights rather than opaque predictions.

Could you tell us more about the team behind NSigma and what experience you bring from finance, data, or AI that shaped NSage.ai?
‍NSigma is built by a team that brings together deep experience across finance, data, and AI. On the finance side, Nikola Legetić (CFA) has over 20 years of experience as an analyst at U.S. investment funds, while Ivan Malčić, our CEO, focuses on finance and go-to-market strategy.
From a technical perspective, our AI and machine learning work is led by Zoranka Desnica, a PhD mathematician who previously developed advanced algorithms at Epic Games and Bosch. On the data and platform side, Siniša Stanojlović, our CTO, brings 20 years of experience in digital product architecture, and Stefan Filipović specializes in building automated data pipelines and real-time data processing systems.

What were your main goals for the Danube Digital Accelerator, and where did you hope to get the most support?
‍After completing the Danube Digital Accelerator, our focus shifts from preparation to execution. The program helped us gain clarity around our go-to-market strategy and sales approach, and now we’re ready to put that into practice.
Our immediate next steps are starting enterprise pilot sales and validating NSage.ai through real-world customer use cases. Bringing the product in front of actual users and proving its value in live investment workflows is our top priority.
At this stage, the support we value most is help with introductions to potential enterprise customers and early design partners, as well as continued guidance on enterprise sales and deal structuring. Securing our first customers and building early traction will be critical for our next phase of growth.

What’s next for NSigma, and what should we be excited to see from you next?
What’s next for NSigma is a strong push toward the market. Our immediate focus is on enterprise sales, working closely with a small number of institutional clients to deploy NSage.ai in real-world investment workflows. At the same time, we’re preparing to introduce the platform more broadly and clearly articulate its value to the market. What’s exciting is that we’re moving from building to selling, taking what we’ve developed and finally putting it in front of the world.

Thank you, NSigma, for sharing your story and for the work you’re doing to make investment research faster, more transparent, and more reliable. We’re glad to have you in the Danube Digital Accelerator and we’re keeping our fingers crossed for your continued progress and success.

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