We build narrow-deep, domain-specific language models trained on proprietary industry data — systems that outperform general models by 25–30% on specialist tasks. Industry-native intelligence, built from the inside.
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's 175+ deployments generates proprietary training data — structured, industry-classified signal that no foundation model can access. The DSLM improves. The competitive gap widens. This is a continuous learning flywheel that compounds with every deployment.
Core Capabilities
Each capability feeds the others. Curated proprietary data trains better models. Better models generate richer interactions. Richer interactions produce more training signal.
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 — an AI system that gets demonstrably more accurate the more it works.
Strategic Partnership
175+ live Sophiie deployments across Australian SMEs generate the continuous, structured, industry-classified training signal that makes the DSLM thesis possible. Without this deployment network, the narrow-deep methodology has no proprietary data pipeline.
Customer-facing AI agents deployed across automotive workshops, service businesses, and professional services — operating 24/7 at scale.
Not raw transcripts — ATHENA CORE processes each interaction into structured signal: intent, domain category, resolution path, compliance flags.
The structured signal feeds directly into DSLM training pipelines. Proprietary data from real Australian industry interactions — not synthetic, not scraped.
Improved DSLMs make Sophiie agents more accurate on domain-specific queries. Better performance drives retention. More deployments mean more training data. The loop compounds.
Without Sophiie's deployment network, there is no proprietary data pipeline. Without xSeraAI's DSLM methodology, Sophiie's data stays unstructured. This is co-dependency — both get exponentially more valuable together.
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 using sovereign, domain-specific data.
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 and TGA compliance layers.
Research & Funding
xSeraAI is pursuing a Cooperative Research Centre Project (CRC-P Round 19) to fund the foundational research for sovereign, domain-specific AI systems in Australian industry — a pathway into CRC Round 28 ($50M AI Accelerator) for multi-vertical scale.
Methodology owner and IP holder. Data curation pipeline, ATHENA CORE architecture, and DSLM training framework. Nathan Nguyen Luu, Founder.
Motor Trades Association of Queensland. 6,000+ automotive businesses. 11 specialist divisions. 8-year partnership with the founding team. Primary data and pilot deployment partner.
Prof. Michael Milford, ARC Laureate Fellow. AI architecture validation, privacy framework development, and DSLM evaluation methodology. 9-year relationship, prior CRC experience.
Dr. Sue Keay. H2O AI 100. Former Chair, Robotics Australia Group. Provides national AI policy alignment and sovereign AI strategic advisory.
Luke Kelleher. 175+ live AI agents deployed across Australian SMEs — generating continuous, proprietary DSLM training signal. Without this network, the narrow-deep thesis has no data pipeline.
Dr. Brett Dale. Australian Medical Association Queensland. Healthcare vertical pathway partner — Medicare, TGA, and AHPRA compliance alignment for CRC Round 28 expansion.
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.