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.
Predicting particle trajectories with neural networks
- Problem
Reconstructing particle trajectories from detector data with traditional statistical methods was computationally expensive, limiting how fast experiment data could be processed.
- Solution
Jefferson Lab replaced the statistical approach with Deep Netts neural networks trained on detector data – running in the lab's existing Java-based analysis infrastructure.
- Outcome
Trajectory prediction became significantly more computationally efficient, letting the lab process experiment data faster on the same hardware.
Machine learning for advanced materials research
- Problem
A materials research group needed ML models for property prediction and data processing, but the surrounding tooling was Java – adding a Python stack meant new dependencies and maintenance for a research team.
- Solution
The group builds and trains models with Deep Netts directly in Java, keeping the whole workflow – data processing, training, prediction – inside one ecosystem.
- Outcome
Faster data processing and property prediction, with no new language stacks or dependencies added to the research environment.
Bring high-performance AI into your Java architecture.
Evaluate Deep Netts on your workload. See what Java-native AI can do.