The Challenge
AI's growth is outpacing what infrastructure can sustain.
The conventional AI model demands ever-expanding data centres — centralised, energy-hungry, expensive, and increasingly at odds with data sovereignty regulations. The cost is measured in billions, and it's accelerating
Unprecedented Energy Demand
Data centres now consume more electricity than many countries, with AI workloads doubling energy use year on year.
Soaring Infrastructure Costs
GPU clusters, centralised storage, and full model retraining push AI costs into the billions — pricing out most organisations.
Environmental Impact
The carbon footprint of training a single large AI model equals the lifetime emissions of five cars.
Sovereign Security Risks
Centralised data aggregation creates critical vulnerabilities — exposing national, healthcare, and financial data.
The ATD Solution
Four innovations.
One AI paradigm.
ATD AI sends the model to the data — not the other way around, eliminating the need for centralised infrastructure entirely. From edge nodes to petabyte-scale Spark clusters.
How It Works
A new paradigm,
in four steps.
The ATD framework reverses the conventional AI workflow — moving intelligence to data instead of data to servers.
Model travels to data
Lightweight AI model is sent to each node — hospital, bank, sensor, or shard.
Local learning
Each node trains on its own data. Raw data never leaves its origin.
Knowledge exchange
Only learned weights are shared peer-to-peer between nodes — no central server.
Compounding intelligence
Each round deposits reusable knowledge — new models recombine in zero time.
Validated Globally
Benchmarked against
Google & HPE.
Across 123 disease types, 30 institutions, and 311,703 medical images — ATD outperforms both Federated Learning (Google/US) and Swarm Learning (HPE/Germany).
| Metric | Federated (Google/US) | Swarm (HPE/Germany) | ATD AI 🇦🇺 |
|---|---|---|---|
| Diagnostic Accuracy | 76.65% | 64.80% | 95.06% |
| Training Time (123 diseases) | 32.18 hrs | 52.76 hrs | 9.74 hrs |
| Energy Consumption | 9.7 kWh | 15.8 kWh | 2.9 kWh |
| Compute Used | 607k TFLOPs | 607k TFLOPs | 35k TFLOPs |
| Central Server Required | Yes | Blockchain | None |
| Big Data / Spark Integration | Limited | Limited | Native |
Proven Performance
The numbers speak for themselves.
Validated across healthcare, nature recognition, and distributed enterprise deployments.
Across 123 disease types using 311,703 medical images from 30 institutions — with zero raw data shared.
ATD completed training in 9.74 hrs vs 52.76 hrs for Swarm Learning (HPE/US) on the same dataset.
2.9 kWh vs 15.8 kWh (Swarm) for equivalent training tasks — a transformational reduction in power demand.
Big Data ATD running on Apache Spark slashes compute infrastructure cost — without sacrificing scale or accuracy.
The ATD Solution
Built for every sector where data privacy matters.
ATD's framework is sector-agnostic by design. Wherever sensitive data exists and AI needs to scale — ATD delivers.
Strategic Partners & Backing
ATD reduces dependence on high-cost computing infrastructure and energy-intensive data centres, unlocking truly scalable, secure, and sustainable AI.
Key Value Proposition
Australian Innovation · FOur World-First Patent · Industry 5.0 Ready
Ready to break the cycle?
Talk to our team about how ATD AI can transform your organisation's AI capability — from edge devices to petabyte clusters.