Where Custom LLMs Fit
Worked examples of private AI in regulated industries, built on your own data, deployed on your terms.
These are illustrative use cases describing how custom models are typically designed for different industries. They are not client case studies and do not describe results achieved by any particular organisation.
Compliance review and reporting
The Problem
Compliance teams manually review large volumes of transaction reports, internal communications, and regulatory filings each quarter. The work is slow, hard to scale, and edge cases are easy to miss. Sending any of that material to a public AI service is generally not an option.
How It Is Built
A private model is built around your internal policies, the relevant regulatory guidance, and your historical compliance decisions. It surfaces patterns for review, drafts reports, and cites the source material behind each conclusion so an analyst can verify it. The model and the data stay on Australian infrastructure you control, and a human makes every final call.
Internal knowledge and research
The Problem
A firm’s institutional knowledge is spread across decades of unstructured documents in multiple systems. Junior associates spend a large share of billable hours re-doing research that the firm has, in some form, already done. Client material cannot be exposed to public AI tools.
How It Is Built
A private model is grounded in your case history, research library, and practice-area playbooks. It performs multi-document analysis, surfaces relevant precedents with citation links back to the source, and produces first drafts for a lawyer to review and take responsibility for. Nothing leaves your environment.
Systematic literature review
The Problem
Systematic reviews require screening very large volumes of papers, extracting structured data, assessing methodological quality, and synthesising findings. A workload that can occupy a research team for many months per review.
How It Is Built
A private model is built around your taxonomy, citation standards, and published methodology. It screens papers for relevance, extracts structured data from studies, and drafts synthesis narratives for your researchers to assess. Screening thresholds are set by you, and researchers remain the arbiter of what is included.
Explore Your Own Use Case
Book a strategy session with our AI engineering team. We will assess your data landscape and identify the highest-value opportunities for private AI in your organisation.