FAQ

NSigma delivers Managed Analytics as a Service (MAaaS), a complete solution for investment firms that need advanced data science, machine learning, and AI without building costly in-house teams. Our modular platform, MOSAIC, integrates data engineering, predictive modeling, visualization, and operational automation.

MOSAIC is our four-part framework:

• dataACQUIRE – Builds your data foundation (collection, cleaning, governance).
• dataPREDICT – Turns data into signals and forecasts with AI/ML.
• dataVISION – Creates intuitive dashboards and visual storytelling for decision-making.
• dataADVANCE – Automates and secures pipelines, ensuring models scale reliably.

Each module can be used independently or combined into an end-to-end solution.

We work with active investment managers managing $5B–$100B AUM. These firms typically run equity or multi-asset strategies and need data science to maintain an edge. Our clients range from traditional long-only asset managers modernizing infrastructure to hedge funds deploying advanced ML.

Building elite data and AI capability internally is slow, costly, and talent-constrained. Recruiting, training, and retaining top engineers and data scientists often takes years and millions of dollars. NSigma offers instant access to a full-stack team and proven architectures so you can move from data problems to production insights in months, not years.

Most vendors offer generic APIs or one-size-fits-all dashboards. We specialize in tailor-made analytics for investment workflows, with deep expertise in time-series modeling, portfolio optimization, and explainable AI. Every MOSAIC deployment is customized to your firm’s strategy, data maturity, and performance goals.

We implement DataOps, MLOps, and DataSecOps best practices, including encryption, access governance, audit trails, and model monitoring. Our infrastructure is cloud-native, composable, and aligned with enterprise-grade regulatory expectations.

Typical outcomes include:

• 30–60% reduction in analyst time spent on manual data prep.
• Faster research cycles, turning signals into decisions in hours, not weeks.
• Improved alpha capture via cross-portfolio insights and predictive models.
• Scalable, resilient pipelines that adapt to new data and regulations.

See our Case Studies for concrete examples.

Timelines vary by scenario:

• Greenfield (no infrastructure): 3–6 months to full build-out.
• Brownfield (modernization): 2–4 months to unify systems and deploy analytics.
• Advanced firms (analytics focus): 6–12 weeks to deploy ML models and visualization layers.

We prioritize fast wins while building a long-term foundation.

We operate as an extension of your research and technology teams. Depending on engagement model, our experts can either:

• Deliver turnkey solutions end-to-end, or
• Embed alongside your analysts and engineers as a co-pilot team.

We are headquartered in the US with a dedicated engineering hub in Novi Sad, Serbia. Our team of 30+ senior data scientists and ML engineers partners globally with leading asset managers.

Ask yourself:

• Do we spend more time wrangling data than analyzing it?
• Are our analysts struggling to extract insights fast enough?
• Have we invested in infrastructure but can’t show ROI yet?
• Do we need predictive analytics but lack in-house depth?

If the answer is yes to any of these, NSigma can help.

We begin with a discovery call to assess your current data maturity, pain points, and strategic goals. From there, we design a tailored roadmap—whether that’s modernizing infrastructure, deploying predictive models, or industrializing your analytics stack.
Contact: info@nsigma.io