Private AI Built for Australian Telecommunications

The TIA Act imposes strict controls on who can access telecommunications data and under what circumstances. A custom LLM deployed on your own infrastructure handles data under your existing data governance framework, subject to the same access controls and audit requirements you apply to any internal system. No subscriber records, call data records, or lawful interception material is transmitted to any external party. For particularly sensitive operational functions such as lawful interception procedure documentation, we can deploy an air-gapped configuration with no network connectivity.

Yes. The Telecommunications Consumer Protections Code imposes obligations around complaint handling, billing transparency, contract terms, and service provisioning. The model can be trained on your TCP compliance documentation, complaint response templates, and internal policies. This allows contact centre agents to retrieve the correct procedure for any complaint scenario, draft compliant responses, and generate evidence for TCP self-assessment reports. We update the model when TCP Code revisions take effect.

Yes. The model is trained on your complete product catalogue, including the full range of technology types, retail and wholesale products, eligibility rules, and provisioning procedures. This is particularly valuable for telcos with complex product mixes where agents and technicians need to navigate different procedures depending on the service type. The model handles the complexity so your people do not have to hold it all in their heads.

Accuracy is managed through several mechanisms. First, the model is fine-tuned on your documentation rather than generating answers from general internet knowledge, which dramatically reduces hallucination. Second, every response includes citations to the source document so users can verify the answer. Third, the model is configured to flag uncertainty rather than guess when confidence is low. Finally, the pilot phase includes structured accuracy testing against a set of known-correct scenarios before rollout to the wider team.

It typically complements rather than replaces your existing systems. The LLM provides a natural-language interface over your existing documentation, ticketing history, and product content, making it far easier to find answers without navigating multiple systems. Many customers retain their existing document management and knowledge base platforms and use the LLM as the search and retrieval layer. For customers who want to consolidate, we can build the model as the primary interface, but this is not a requirement.

Contact centre deployments typically show measurable ROI within three to four months of pilot completion. The primary driver is average handling time reduction, which for complex service queries can be 30 to 45 percent. At typical telco contact centre scale, this translates to significant cost reduction or capacity freeing for higher-value interactions. NOC deployments show ROI through faster fault resolution, which reduces customer minutes affected and the associated SLA penalty and churn exposure. We model the expected return during the assessment phase against your actual contact volume and cost structure.