We build narrow-deep, domain-specific AI systems that outperform general models on specialist industry tasks. Proprietary data in. Industry-native intelligence out.
The Four-Layer Stack
The Thesis
A general-purpose LLM cannot interpret an Australian Design Rule, diagnose a fault code from a Bosch ECU, or navigate MTAQ's apprenticeship compliance framework. These tasks require models trained on data no foundation model has ever seen.
xSeraAI builds Domain-Specific Language Models (DSLMs) — narrow-deep systems fine-tuned on proprietary industry data. They go deep where general models go wide.
Broad knowledge, shallow depth. Hallucinates on domain-specific regulatory and technical queries.
Narrow focus, deep mastery. Trained on proprietary data — ADRs, OEM specs, state regulations.
The moat: Every interaction through Sophiie generates proprietary training data. The model improves. The competitive gap widens. This is a data flywheel that compounds with every deployment.
Core Capabilities
Each capability feeds the others. Curated data trains better models. Better models generate richer interactions. Richer interactions produce more training data.
Ingesting, cleaning, and structuring proprietary industry data that no foundation model has access to. Domain ontologies built from the ground up.
Multi-agent workflows that route queries through specialist models. Not one monolithic LLM — a coordinated system where each agent handles what it does best.
Every interaction generates signal. Models retrain on real-world performance data. The system improves with use — an AI system that gets smarter the more it works.
Current Verticals
Each vertical gets a purpose-built DSLM trained on proprietary data from within the industry. Not adapted from a general model — constructed from first principles.
Partnered with MTAQ (Motor Trades Association of Queensland) to build Australia's first automotive-specific DSLM. Trained on ADRs, OEM service manuals, fault code databases, apprenticeship competency frameworks, and live operational data from Sophiie-powered workshops.
Domain-specific model for functional medicine, peptide protocols, biomarker interpretation, and longevity optimisation. Built for telehealth consultation support with structured clinical reasoning pathways.
Research & Funding
xSeraAI is pursuing a Cooperative Research Centre Project (CRC-P Round 19) to fund the foundational research for domain-specific AI systems in Australian industry. This feeds into CRC Round 28 ($50M AI Accelerator) for multi-vertical scale.
Methodology owner and IP holder. Data curation pipeline, ATHENA CORE architecture, DSLM training framework.
6,000+ automotive businesses in Queensland. 11 specialist divisions. 8-year partnership with founder.
AI architecture validation, privacy framework development, DSLM evaluation methodology. QUT/UQ/Griffith.
175+ deployed AI agents. Customer-facing interface and real-world data pipeline. Partnership with Luke Kelleher.
Get Involved
We're building the consortium for CRC-P Round 19 — researchers, industry bodies, and technology partners who understand that narrow-deep outperforms broad-shallow.