We help organizations extract insights that drive better decisions and measurable outcomes — from exploratory analysis to full predictive analytics platforms.
Most organizations are sitting on data that could be driving better decisions — but it is locked in silos, undocumented, or simply not being analyzed. RadiCorp's data science and analytics practice bridges the gap between raw data and business action: we clean, model, visualize, and deliver insights in a form that decision-makers can actually use.
From one-time exploratory analysis to full self-service analytics platforms with live dashboards and predictive scoring, we tailor our engagement to your data maturity and business goals. Our practitioners combine statistical rigor with engineering discipline — so the models and dashboards we build are not just accurate, but production-ready and maintainable.
From raw data profiling to production-grade predictive models and self-service BI — we handle every stage of the analytics lifecycle.
From Jupyter notebooks to production BI platforms — we work with the tools that fit your team and infrastructure.
A rigorous, business-aligned analytics process that ensures outputs are accurate, interpretable, and actionable.
Every engagement starts with the questions your business needs answered — not the data you have. We work with stakeholders to define success criteria and the decisions that better analytics would improve.
We catalogue available data sources, assess quality and completeness, identify gaps, and profile distributions — producing a data quality scorecard before any analysis begins.
Statistical exploration of the data — patterns, correlations, anomalies, and hypotheses — validated and documented in reproducible notebooks that become part of your analytical asset library.
Iterative model building with rigorous cross-validation, hyperparameter tuning, and interpretability analysis. Models are evaluated against business metrics — not just statistical accuracy scores.
Insights are surfaced through BI dashboards, automated reports, and — where appropriate — model API endpoints that integrate directly with your operational systems and workflows.
We train your business and data teams to use and interpret the outputs, document the analytical methods, and establish refresh schedules and monitoring to keep dashboards and models current.
Great analytics requires great data infrastructure. Our Big Data engineering practice builds the pipelines, lakes, and lakehouses that feed your analytics platform with clean, governed, timely data.
Explore Big DataMove beyond descriptive analytics into predictive and generative AI — production ML models, LLM integration, and MLOps infrastructure that keep models accurate and reliable over time.
Explore AI & MLRun your analytics workloads on cloud-native platforms — BigQuery, Redshift, Snowflake, and Databricks SQL Analytics — with the cost governance and governance controls that enterprise demands.
Explore Cloud ComputingTell us what questions you wish your data could answer. We will show you how to get there — with analytics that are accurate, maintainable, and built for your business.