The AI goes
to the data.
ATD Learning is a fully serverless, peer-to-peer AI paradigm. The model travels to each node, learns locally from data that never moves, then shares only distilled knowledge with peer institutions. No central server. No raw data transfer. No sovereignty risk.
Privacy-Preserving by Architecture
Raw data never leaves its origin. Only learned model weights are exchanged — structurally incompatible with data exfiltration.
Fully Serverless & Peer-to-Peer
Unlike Federated (central server) or Swarm (blockchain), ATD operates with zero central coordination infrastructure.
Regulation-Ready Across Jurisdictions
Compliant with GDPR, Australian Privacy Act, HIPAA, and national data sovereignty laws — by design, not workaround.
Every node gives knowledge to every other simultaneously.
Zero central server. Zero raw data movement.
ATD AI
Four innovations.
One paradigm shift.
ATD AI has built the world's only fully serverless, peer-to-peer AI ecosystem — sending intelligence to data, not data to servers. From edge devices to massive Spark clusters: no data centres, no sovereignty risk, no unnecessary cost.
ATD Learning
Decentralised · Sovereign · Serverless
Beehive Learning
Centralised · Incremental · Efficient
Knowledge Bank
On-Demand · Adaptive · Zero-Cost
Big Data ATD
Spark-Native · Scalable · Lean Compute
ATD vs. the field.
No comparison.
| Capability | Centralised AI | Federated Google / US |
Swarm HPE / Germany + US |
ATD AI 🇦🇺 |
|---|---|---|---|---|
| Central Server | ✗ Required | ✗ Required | Blockchain | ✔ None |
| Raw Data Movement | ✗ Required | ✔ None | ✔ None | ✔ None |
| Sovereign Compliance | ✗ High risk | Partial | Partial | ✔ By design |
| Training Speed (123 diseases) | — | 32.18 hrs | 52.76 hrs | ✔ 9.74 hrs |
| Energy Consumption | Very high | 9.7 kWh | 15.8 kWh | ✔ 2.9 kWh |
| Diagnostic Accuracy | ~85% | 76.65% | 64.80% | ✔ 95.06% |
| Continuous Learning | ✗ Full retrain | ✗ Partially | ✗ Sequential | ✔ Incremental |
| Big Data / Spark Integration | ✗ Costly HPC | ✗ Limited | ✗ Limited | ✔ Spark-native |
| Cost Reduction | Baseline | Moderate | Moderate | ✔ Up to 50% lower |
| Patented Technology | — | — | — | ✔ World-first |
Learn once.
Never forget.
Beehive is an advanced centralised training method that updates AI continuously from new data only. Like a living hive, each new cell adds knowledge without dismantling what already exists — on a single GPU, at any scale.
Single GPU — Any Scale
Millions of images and thousands of classes processed at 2× the speed of conventional methods, on hardware a fraction of the cost of centralised GPU farms.
Surgical Model Updates
Correct or improve specific classes without touching the rest — reducing update cycles from weeks to hours.
Stable on Imbalanced Data
Consistent accuracy across heterogeneous, real-world datasets — critical in healthcare, finance, and defence deployments.
Incremental learning, cell by cell.
Single GPU. Any scale. Never forget.
Petabyte scale.
Lean compute.
Big Data ATD is a new concept engineered for large-scale data workloads. Native integration with platforms like Apache Spark distributes intelligence across the data — radically reducing the need for powerful GPU clusters while improving throughput and efficiency.
Spark-Native Integration
Drops directly into Apache Spark pipelines — leveraging existing distributed compute infrastructure without rewriting your data stack.
Built for Petabyte Workloads
Designed from the ground up for terabyte-to-petabyte datasets in genomics, telemetry, climate science, finance and logistics.
Drastically Reduced Compute Footprint
Eliminates the need for premium GPU farms by sending compact intelligence to data partitions — typically running on a fraction of legacy HPC cost.
Built for terabyte-to-petabyte workloads.
Massive scale on lean compute — Spark-native.
Build any model.
In zero time.
The Knowledge Bank stores distilled intelligence from all previously trained models and instantly assembles task-specific sub-models by recombining learned components. No retraining. No delay. No additional compute cost.
Instant Sub-Model Generation
Task-specific models assembled from reusable components in real time — eliminating the compute cost of training from scratch.
Compounding Intelligence
Every trained model deposits reusable knowledge. The more tasks trained, the richer the recombination pool.
Zero Marginal Compute Cost
Once knowledge is banked, new model variants cost nothing additional — fundamentally changing the economics of AI.
Recombine any learned knowledge into new models.
Instant generation — zero additional compute.
Proven across
real-world deployments.
// READY TO DEPLOY
Sovereign AI.
From edge to petabyte.
Four innovations. One unified ecosystem. Discover how ATD AI deploys across healthcare, finance, defence, agriculture and any large-scale data environment.