Predictive analytics
We build models that forecast demand, churn, equipment failure or cash flow using your own historical data. A typical engagement takes four to six weeks from data audit to a production-ready API your existing systems can call.
Custom AI software built for how your business actually works
We design, build and maintain AI tools for Australian companies that need to process data faster, automate repetitive tasks, and make better decisions without hiring a data-science team. Based in Western Australia, we work with clients across the country.
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What we actually build
Every project starts with a problem, not a technology wish list. We sit down with your team, map the workflow that's costing you time or money, and figure out where a trained model, an automated pipeline, or a simple classifier will make a measurable difference. Here's where most of our work falls.
Document processing
Invoices, compliance forms, medical records, tenancy agreements. We train extraction pipelines that pull structured data from PDFs, scanned images and handwritten notes. Most clients see a 70–85% reduction in manual data entry within the first month.
Internal chat and search tools
We connect large language models to your internal knowledge base so staff can ask questions in plain English and get answers drawn from your own policies, procedures and past projects. Retrieval-augmented generation keeps responses grounded in real documents, not hallucinations.
Customer-facing AI features
Product recommendations, dynamic pricing, intelligent support bots. We integrate these directly into your web or mobile app through clean APIs. Each model is monitored for drift and retrained on a schedule you choose, monthly or quarterly.
How a project runs
Week one is a discovery session. We spend half a day with the people who actually do the work, not just the managers, to understand the data you have, the data you wish you had, and the decisions that slow things down. We leave with a written scope document and a fixed-price quote.
Weeks two through four cover data preparation and model prototyping. You see a working demo by the end of week three. If the accuracy or speed doesn't meet the targets we agreed on, we adjust the approach before writing production code.
Deployment usually happens in week five or six. We package models as containerised services that run on your cloud account (AWS, Azure or GCP) or on-premises if compliance requires it. We hand over documentation, train your team, and stay on a support contract for at least three months.
No black boxes. You own the code, the trained weights, and the data pipeline. If you ever want to bring maintenance in-house or switch vendors, everything transfers cleanly.
Results from recent projects
We can't share every client's name, but here are three outcomes from the past twelve months.
Agricultural supply chain
A grain logistics company in the Wheatbelt was spending roughly 15 hours a week reconciling delivery dockets against purchase orders. We built an OCR pipeline that reads photographed dockets, matches them to orders in their ERP, and flags mismatches. Reconciliation now takes about two hours a week, and the error rate dropped from around 8% to under 1%.
Residential property management
A Perth property manager with 1,200 tenancies needed faster responses to maintenance requests. We trained a classifier on three years of maintenance tickets to auto-categorise urgency and route requests to the right trade. Average response time went from 26 hours to 7 hours, and tenant satisfaction scores improved by 18 points over two quarters.
Mining equipment reseller
Demand forecasting for spare parts was mostly guesswork. We built a time-series model using five years of sales data plus commodity price feeds. The model now runs weekly and generates purchase recommendations. Stockout events fell by 40% in the first six months, and carrying costs dropped by roughly $220,000 per year.