Research & Innovation
At Nexwin, Australia's leading AI research lab, we're pioneering advancements in artificial intelligence tailored specifically for the unique linguistic and cultural contexts of Australia and New Zealand. Our multidisciplinary team of 38 researchers—including computational linguists, machine learning specialists, and voice technology experts—is pushing the boundaries of what's possible in AI.
Our flagship AI:MODEL:NEXWIN:v5.2 represents over four years of intensive research and development, trained on more than 780,000 hours of Australian and New Zealand speech data and 42 billion tokens of regional text. This model powers both our commercial products and cutting-edge research into next-generation AI solutions.
Core Research Areas
Generative AI for Oceania
Our AI:MODEL:NEXWIN:v5.2 is specifically designed to understand and generate content for Australian and New Zealand markets. With a context window of 128,000 tokens and 175 billion parameters, it achieves state-of-the-art performance on regional language tasks.
Technical highlights:
- 98.7% accuracy on Australian colloquialism comprehension
- 93.2% success rate with indigenous term recognition
- Transformer-based architecture with 48 attention heads
- Regional context vectors for location-specific responses
- Proprietary training on 12.8M documents from Australian and NZ sources
Our specialized dataset includes transcripts from Parliament, ABC broadcasts, and regional news outlets to ensure the model understands the full spectrum of Australian English.
Regional Natural Language
Nexwin has developed specialized NLP models that recognize and correctly interpret the unique linguistic patterns of Australian and New Zealand English.
Key technologies:
- RegioNER: Our named entity recognition system with 96.5% accuracy on Australian place names, businesses, and public figures
- OzSentiment: Fine-tuned sentiment analysis for Australian expression patterns
- KiwiContext: Contextual understanding module for New Zealand cultural references
- MultiAccent: Dialectal variation processing across 8 regional Australian accents
Our 2024 benchmark tests show our models outperforming general-purpose AI by 23% on Australian-specific queries and 18% on New Zealand content.
Oceanic Voice Technology
Our voice research has produced Australia's most advanced speech systems, with specialized technology for regional accents and environments.
Flagship technologies:
- NexVoiceID™: Speaker verification with 99.7% accuracy and antispoofing capabilities
- OzTTS: Text-to-speech with authentic Australian and New Zealand accents across 12 regional variations
- AcousticEnvironment: Noise suppression optimized for Australian urban and rural environments
- EmotiveResponse: Real-time emotion detection calibrated for cultural expression differences
Our voice models are trained on the largest corpus of Australian and New Zealand speech data ever assembled, including 12,500 hours of annotated regional dialects and 7,800 hours of indigenous language recordings.
The Nexwin Research Laboratory
Established in 2020, our Sydney research facility houses Australia's most powerful AI computing cluster, including:
- 84 NVIDIA A100 GPUs in a custom high-throughput configuration
- 1.2 petaflops of dedicated AI computing power
- 12 petabytes of high-speed storage for training data
- Direct connection to the AARNet research network
Our Melbourne lab focuses on voice technology research with specialized acoustic environments and testing facilities. The Wellington satellite office specializes in New Zealand linguistic and cultural adaptation.
Academic Publications
Our research team actively contributes to the global AI community with peer-reviewed publications:
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May 2025
"AI:MODEL:NEXWIN:v5.2: A Transformer-Based Architecture Optimized for Australian and New Zealand English Processing"
Kim, J., Patel, S., Nguyen, T., and O'Connor, M.
Transactions on Machine Learning Research, Vol. 12(3), pp. 487-512.
View Paper -
February 2025
"Understanding Cultural Context in Conversational AI: Lessons from Australian and New Zealand Deployments"
Thompson, E., Williams, K., and Chen, L.
International Conference on Computational Linguistics (COLING 2025), pp. 1823-1837.
View Paper -
November 2024
"Enhancing Voice Assistant Intent Recognition for Australian Market Contexts"
Johnson, M., Patel, A., and Thompson, R.
Proceedings of the 28th Conference on Natural Language Processing, pp. 438-452.
View Paper -
September 2024
"NexVoiceID™: Robust Speaker Verification in Noisy Environments using Deep Attentive End-to-End Networks"
Zhang, L., O'Donnell, S., and Patel, V.
IEEE Transactions on Audio, Speech, and Language Processing, Vol. 32, Issue 1, pp. 87-103.
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August 2024
"Voice Biometrics in Australian Banking: Security Enhancement and Customer Experience"
Roberts, S., Patel, N., and Chang, H.
Journal of Financial Security and Technology, Vol. 14(3), pp. 178-196.
View Paper -
June 2024
"Adapting Large Language Models for High-Accuracy Intent Recognition in Australian Financial Services"
Baker, M., Singh, R., and Wong, A.
Journal of Natural Language Engineering, Vol. 30, Issue 2, pp. 215-238.
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March 2024
"Preserving Indigenous Languages Through AI: The Nexwin Approach to Cultural Sustainability"
O'Connor, M., Djugun, A., and Thompson, E.
Journal of Cultural Preservation and Technology, Vol. 18(2), pp. 157-179.
View Paper
AI:MODEL:NEXWIN:v5.2 Technical Specifications
Model Architecture
| Architecture Type | Transformer-based with regional context enrichment |
| Parameters | 175 billion |
| Context Window | 128,000 tokens |
| Attention Heads | 48 |
| Hidden Layers | 96 |
| Hidden Size | 12,288 |
| Activation Function | SwishGELU (proprietary variant) |
Training & Performance
| Training Compute | 3.8 x 10^24 FLOPS |
| Training Data | 42B tokens (regional text), 780K hours (speech) |
| Regional Benchmarks | AusGLUE: 89.7%, KiwiBERT: 91.2% |
| General Benchmarks | MMLU: 86.3%, GSM8K: 92.5% |
| Inference Speed | 47K tokens/sec on optimized hardware |
| Quantization | 4-bit, 8-bit, and 16-bit variants available |
Real-World Research Applications
Emergency Services NLP
In collaboration with New South Wales Ambulance, we developed a specialized version of our AI:MODEL:NEXWIN system that can analyze emergency calls in real-time, detecting Australian regional accents and slang even under stress conditions. The system achieved 97.3% accuracy in identifying critical medical information and location details, reducing dispatch times by an average of 23 seconds.
Key metrics: 97.3% accuracy | 23-second reduction in dispatch times | 12% improvement in resource allocation
Indigenous Language Preservation
Our research team developed specialized transfer learning techniques that allow our AI models to be fine-tuned on extremely low-resource languages. Working with the Australian Institute of Aboriginal and Torres Strait Islander Studies, we've helped digitize and create interactive learning resources for 12 endangered indigenous languages using as little as 200 hours of recorded speech per language.
Key metrics: 12 languages digitized | 8,400+ hours of analyzed content | 15 educational applications developed
Agricultural AI Assistant
In partnership with the CSIRO, we adapted our AI:MODEL:NEXWIN:v5.2 for use in rural Australia, creating a specialized voice-based AI assistant for farmers that understands regional agricultural terminology, works offline, and functions reliably in harsh outdoor environments. The system helps with crop management, livestock monitoring, and weather analysis, with a focus on Australian-specific agricultural practices.
Key metrics: 35,000+ agricultural terms recognized | 94% accuracy in noisy environments | Used by 1,800+ farms across Australia
Future Research Directions
As we continue to advance our AI:MODEL:NEXWIN platform, our research is focusing on several frontier areas:
AI:MODEL:NEXWIN:v6.0 (Development)
Our next-generation model currently in development includes:
- 512K token context window for enhanced document understanding
- Multimodal capabilities for analyzing Australian landscapes and environments
- Enhanced cultural context understanding through 3D embeddings
- Reduced latency for real-time applications in remote areas
Target research release: Q4 2025
Edge AI for Rural Australia
Research into compact, energy-efficient AI models that can run on limited hardware in remote Australian locations with:
- Satellite connectivity optimization for intermittent networks
- Solar-powered inference capabilities for sustainable deployment
- Specialized models for agricultural and mining applications
- Extreme weather resilience for consistent performance
Cultural Heritage Preservation
Expanding our work with Indigenous communities to:
- Develop AI systems that preserve storytelling traditions
- Create cross-generational knowledge transfer platforms
- Build self-supervised learning systems for low-resource languages
- Design community-controlled AI deployment frameworks
Collaborate with Us
We are always looking for opportunities to collaborate with researchers, universities, and organizations. If you are interested in partnering with us, please reach out.