Leg3 helps organizations move from scattered data and technical complexity to practical analysis, dashboards, automation, and self-hosted analytics workflows. Our work sits at the intersection of data science, infrastructure, and operational decision support.

We work with public, operational, and business datasets to identify patterns, compare indicators, and build explainable models. Current areas of focus include time-series analysis, rolling-window indicators, sentiment and volatility measures, and practical statistical workflows that help turn noisy data into clearer questions.

Leg3 develops visual tools that make analysis easier to understand and communicate. This includes interactive dashboards, reporting workflows, exploratory visualizations, and decision-support tools built for technical and nontechnical audiences.

Reliable analysis depends on repeatable data collection, cleaning, and transformation. Leg3 builds scripts, workflows, and lightweight pipelines using tools such as R, Python, APIs, and reproducible project structures so that analysis can be refreshed, audited, and extended over time.

Leg3 favors privacy-conscious and self-hosted tooling where appropriate. This includes analytics platforms, internal dashboards, Docker-based deployments, and infrastructure-aware workflows that keep organizations closer to their own data.

Leg3 explores locally hosted AI systems and large language model workflows for research, documentation, analysis support, and internal knowledge workflows. The focus is practical: matching the tool to the problem, preserving control where possible, and keeping AI-assisted work explainable and reviewable.

Data systems do not exist in isolation. Leg3 brings infrastructure experience into analytics work, with background in Linux, Windows Server, Docker, networking, reverse proxies, and self-hosted services. This helps bridge the gap between analysis, deployment, and long-term maintainability.