We specialise in custom AI model development for Australian businesses that need more than generic, off-the-shelf tools. Fine-tuned models, ML pipelines, and production-ready inference infrastructure, all built around your specific data and requirements.
The Challenge
Off-the-shelf AI models are impressive at general tasks but mediocre at specific ones. A generic language model can write decent marketing copy, but it can’t accurately score a PTE Academic speaking response. A standard classification model can sort emails, but it won’t reliably detect the subtle patterns in your industry’s data that separate a good decision from a costly mistake.
That gap between what general-purpose AI can do and what your business actually needs? That’s where custom model development becomes essential. But here’s the thing: building custom AI models requires a rare combination of machine learning expertise, data engineering skills, and production engineering capability.
According to Gartner, only 54% of AI projects make it from pilot to production. Most data science teams can train a model in a notebook. Far fewer can build the infrastructure to deploy, monitor, and maintain it at scale.
And the cost of getting this wrong is measured in months, not days. A model trained on poorly prepared data, deployed without proper monitoring, or built without scalability in mind will fail quietly. It’ll produce confident but incorrect results that erode trust in AI across your organisation. So how do you make sure your investment actually pays off?
Our Approach
We start with the data, not the model. The single biggest factor in model quality is data quality, so we invest heavily in data assessment, cleaning, labelling, and augmentation before any training begins. Working alongside our AI strategy process, we evaluate whether fine-tuning a foundation model, training from scratch, or combining multiple approaches will deliver the best results for your use case.
Our fine-tuning process is methodical. We establish baseline performance with off-the-shelf models, then iteratively improve through domain-specific training data, hyperparameter optimisation, and evaluation against real-world test cases. For our EdTech platforms, this process achieved scoring accuracy that closely mirrors human examiners. That kind of performance only comes from rigorous, domain-specific work.
Now, production deployment is where many AI projects stall. It’s also where our engineering depth makes the difference. We build ML pipelines that automate the entire lifecycle: data ingestion, preprocessing, training, evaluation, deployment, and monitoring. A 2024 MLOps survey found that 60% of organisations struggle most with the deployment phase. Our inference infrastructure is optimised for your specific latency and throughput requirements, with cost-efficient scaling that handles peak loads without burning through your cloud budget.
Every deployed model includes drift detection and automated retraining triggers. This connects directly with our data analytics capability, ensuring performance stays consistent as your data evolves. And if you need a RAG knowledge base alongside your custom model, we integrate those seamlessly.
What We Build
| Model Type | Use Cases | Typical Timeline |
|---|---|---|
| Fine-Tuned LLMs | Domain-specific content, classification, extraction | 6-10 weeks |
| Custom Classification | Fraud detection, sentiment analysis, categorisation | 8-12 weeks |
| Prediction Models | Churn, demand forecasting, risk scoring | 8-14 weeks |
| Computer Vision | Quality inspection, document processing, retail analytics | 10-16 weeks |
| Recommendation Systems | Product suggestions, content personalisation | 8-12 weeks |
Look, every model we build comes with full documentation, API endpoints, monitoring dashboards, and a handover that ensures your team can manage it confidently. (We’re not interested in creating dependency. We want you to own it.)