Real Results from Private AI
See how Australian enterprises across regulated industries are achieving measurable ROI with custom AI models built on their own data.
Major Australian Bank
The Challenge
The compliance team was manually reviewing thousands of transaction reports, internal communications, and regulatory filings each quarter. The process required 14 full-time analysts and still missed edge cases that led to two regulatory notices in a single year.
The Solution
We built a custom compliance AI trained on the bank's internal policies, APRA guidelines, AUSTRAC regulations, and five years of historical compliance decisions. The model automatically flags suspicious patterns, drafts compliance reports, and explains its reasoning with citations to source material.
Annual Savings
$0
$2.1M
Review Accuracy
87%
97.3%
Report Generation
4 hours
12 min
Regulatory Flags Caught
340/qtr
890/qtr
“The custom LLM catches patterns our analysts would never find manually. It reduced our compliance review backlog by 80% in the first quarter and hasn't generated a single false regulatory notice. The fact that our data never leaves Australian infrastructure was non-negotiable.”
— Head of Compliance Technology
Whitfield, Arden & Partners
The Challenge
Junior associates spent 60% of their billable hours on legal research, reviewing precedents, and drafting initial memoranda. The firm's knowledge base of 25 years of case work was trapped in unstructured documents across multiple systems, making institutional knowledge inaccessible.
The Solution
We deployed an internal knowledge base AI trained on the firm's entire case history, research library, and practice area playbooks. The model performs multi-document analysis, identifies relevant precedents, and generates first-draft memoranda with citation links to source documents.
Research Speed
8 hours
90 min
Precedent Coverage
45%
94%
Cost per Research Task
$1,200
$180
Knowledge Base Queries
0
2,400/mo
“Our partners now trust the AI to surface every relevant precedent from our 25-year case history in minutes. Junior associates produce higher-quality work in a fraction of the time. The model understands our firm's argumentation style and Australian legal conventions perfectly.”
— Catherine Arden, Managing Partner
Pacific Health Research Institute
The Challenge
Conducting systematic reviews of medical literature required researchers to manually screen thousands of papers, extract data points, assess bias, and synthesise findings. A single review took 6 to 12 months and consumed the capacity of an entire research team.
The Solution
We built a medical literature AI trained on the institute's taxonomy, citation standards, and 15 years of published systematic reviews. The model screens papers for relevance, extracts structured data from studies, assesses methodological quality, and generates draft synthesis narratives.
Review Completion
9 months
7 weeks
Papers Screened/Day
50
800
Research Output
4 reviews/yr
18 reviews/yr
Annual Cost Savings
$0
$1.4M
“This AI understands our methodology, our citation format, and our quality assessment criteria. It screens papers with 96% agreement to our senior researchers. We've quadrupled our research output without adding a single team member.”
— Dr. Rachel Okonkwo, Director of Evidence Synthesis
Ready to Achieve Similar Results?
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.