Java-native AI, proven in production.
From payment fraud to particle physics – teams run Deep Netts where their systems already live: inside the JVM. Four real deployments, each without adding a second stack.
Real-time fraud detection inside the transaction flow
- Problem
A payments platform needed to score every transaction for fraud in real time – but routing traffic through a separate Python scoring service added latency, infrastructure, and a second stack to operate.
- Solution
Deep learning models built with Deep Netts run directly inside the Java transaction pipeline. Scoring happens in-process, with no network hop and no additional runtime to deploy or monitor.
- Outcome
Fraud decisions now land in milliseconds, inside the same JVM that processes the payment – and the fraud team iterates on models with the tools the platform team already runs.
Forecasting cloud resources before the bill arrives
- Problem
An enterprise platform team was over-provisioning cloud capacity to stay safe, because usage forecasts were coarse and reactive – and the cost of that safety margin kept growing.
- Solution
Deep Netts models forecast resource demand from historical usage, running natively inside the team's JVM-based tooling. Predictions feed autoscaling and capacity planning directly, with no separate ML pipeline.
- Outcome
Provisioning now follows predicted demand instead of worst-case guesses, cutting spend while improving stability during load spikes.
Illustrative data. Hover or use the arrow keys to read each month.
| Month | Demand | Provisioned | Fixed before |
|---|---|---|---|
| Jan (actual) | 520 | 572 | 920 |
| Feb (actual) | 560 | 616 | 920 |
| Mar (actual) | 540 | 594 | 920 |
| Apr (actual) | 600 | 660 | 920 |
| May (actual) | 640 | 704 | 920 |
| Jun (actual) | 620 | 682 | 920 |
| Jul (actual) | 690 | 759 | 920 |
| Aug (actual) | 710 | 781 | 920 |
| Sep (forecast) | 730 | 803 | 920 |
| Oct (forecast) | 760 | 836 | 920 |
| Nov (forecast) | 800 | 880 | 920 |
| Dec (forecast) | 770 | 847 | 920 |
Predicting particle trajectories with neural networks
- Problem
Every event in the CLAS12 detector leaves many segments in the drift chambers – from real particles and from background. The conventional algorithm has to try their combinations, tracking takes 80–90% of event processing time, and at high beam intensity good tracks get lost among the candidates.
- Solution
Two neural networks built with the Deep Netts community edition, in Java, inside the lab's Java reconstruction software: a multi-layer perceptron that labels each track candidate a positive, negative or false track, and an auto-encoder that fills in a segment a chamber missed.
- Outcome
On real data, 15.6% more reconstructed tracks – over twice as many with a missing segment – and 35% faster tracking. Physics analyses gain 15–35% more events (G. Gavalian et al., arXiv:2202.06869).
- 138
- Hits recorded
- –
- Kept (on tracks)
- –
- Rejected as noise
- –
- Tracks found
Illustrative hits. CLAS12: 6 sectors × 3 drift-chamber regions. Hover a track to isolate it.
Deep learning for drug discovery, in Java
- Problem
High-throughput screening produces tens of thousands of data points in one session. Finding the compounds that cause the desired change in a protein calls for deep learning – but tools that depend on native libraries are hard to run on specific hardware, and QSAR methods need structural data such as SMILES that many library compounds lack.
- Solution
Oscar Bastidas, Ph.D., analyses the screening data with Deep Netts feed-forward networks in Java, trained on the physical and chemical properties already in the compound library, with hyperparameters tuned to the data. His study of spike-protein dynamics with Zoran Ševarac, analysed in Java, was published in Scientific Reports (2024).
- Outcome
Hit identification runs entirely on the JVM – no native dependencies, no QSAR input, and in the language the researcher already works in.
42 of 48 predictions within ±0.05 of the reference (shaded band).
Illustrative data, normalised property values. Hover a point for details.
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