StatSion

StatSion turns a task that traditionally takes a financial analyst days — pulling data from Xero, HubSpot, Salesforce, and Google Analytics, then modelling it into a board-ready strategy report — into something that finishes in under five minutes. It's a strong showcase of what machine learning development services look like when applied to real business intelligence problems rather than research demos.

Under the hood, StatSion runs five specialized engines: Normalisation, Statistical Analysis, Financial Modelling, Probability & Risk, and Forecasting. The Forecasting engine alone uses ARIMA and Prophet models to project revenue trends 12 months out with 95% confidence intervals — the kind of quantitative rigor that separates a genuine data science development company from a dashboard-wrapper. The Probability & Risk engine maps client concentration risk into visual zones partnership boards can interpret in seconds, and the whole pipeline exports into white-label PDF strategy reports.

Architecturally, this was a Python FastAPI development project paired with a Next.js frontend and PostgreSQL for structured storage, with one-click OAuth integrations handling the messy reality of connecting to four different third-party platforms without asking users to manage API keys manually. Building forecasting engines that professional services firms will actually stake investment decisions on requires more than calling a library function — it requires validating models against real historical data, handling missing/inconsistent inputs from live business systems, and presenting statistical uncertainty in a way non-technical partners can act on.

For businesses looking for an AI and business intelligence development partner — whether the goal is internal analytics tooling or a client-facing analytics SaaS product — StatSion is proof this team can take "we have data everywhere" and turn it into a forecasting product people trust with real capital decisions.