We Build AI Systems That Work in the Real World

Challenge

An Australian financial advisory firm was spending hours per analyst manually extracting data from PDF statements, prospectuses, and Morningstar feeds into spreadsheets — a bottleneck that delayed every research cycle.

What we built

We designed an AI-powered document processing pipeline using LLMs to extract structured data at scale, plus an ETL pipeline that ingested Morningstar feeds straight into PostgreSQL. Reporting was rebuilt as automated jobs analysts could trigger on demand.

Challenge

A private health insurer wanted to predict which members were most likely to churn so they could intervene earlier. Legacy reporting was manual, inconsistent across teams, and couldn't surface signals fast enough to act.

What we built

We built predictive models for customer churn and lifetime value in Python, then delivered self-service Power BI and Qlik Sense dashboards on top so retention, marketing, and exec teams could all work from the same numbers — no analyst in the loop.

Challenge

A food supply chain operator was drowning in operational data — orders, inventory, deliveries — across systems that didn't talk to each other. They needed visibility into bottlenecks and a way to spot trends before they became problems.

What we built

We built data-driven optimisation workflows: consolidated raw operational data, applied ML to surface patterns, and automated reporting in Python so the team got daily updates without anyone touching a spreadsheet.