Defense industry
Detecting objects in imagery, predicting faults before they happen and finding the right information in large sets of technical documents are all tasks AI can take on in the defense industry. Most of them have to run on closed networks or on the platform itself, so models are built for limited hardware and tested until their behavior can be verified.
Challenges
Closed networks and classified data
Data is often classified and systems run on networks with no internet connection. Models have to be trained, run and updated inside those networks.
Limited hardware on the platform
Models on vehicles, aircraft and field devices have to work within tight limits on power, weight and computing capacity, often without a connection.
Behavior has to be verifiable
Before a system is used, it is tested in the conditions it will meet, and its results have to be traceable. A model that cannot be verified is not put into use.
Typical problems
Image and sensor analysis
Detecting, classifying and tracking objects in camera, thermal, radar and satellite data.
On-platform AI
Running models on vehicles, aircraft and field devices with limited hardware, without depending on a connection.
Predictive maintenance and fault prediction
Predicting faults from sensor data and maintenance records, and planning maintenance before a part fails.
Technical documentation and knowledge access
Assistants that search technical manuals, standards and test reports on closed networks and give each answer with its source.
Simulation and synthetic data
Producing synthetic data and simulations to train and test models where real data is scarce or cannot be shared.
Our research areas
See all research areasLightweight models
Smaller, faster models that keep their accuracy on the platform's own hardware.
Working with sensitive data
Training and running models on closed networks without the data leaving them.
Learning from limited data
Learning from a small amount of real data together with synthetic and simulated data.
Interpretability & evaluation
Testing how a model behaves in different conditions, and showing what each result is based on.
Related services
All services- Computer visionSystems that find, count, measure and inspect objects and events in images and video.
- Edge AI optimizationModels made small and fast enough to run on devices, vehicles and local servers.
- Anomaly & fraud detectionSystems that flag the unusual transactions, failures and faults worth a closer look, even with few confirmed examples.
- AI assistantsAssistants that answer from your own documents and systems, with sources and access control.
- Synthetic dataGenerated training and test data for cases that are rare, sensitive or missing in your real data.
- LLM deploymentLanguage models running on your own servers or private cloud, sized and secured for your workload.
Work with us
Work usually starts with one system or one process, and models are developed and run on the organization's own closed network. Once the results are measured, the scope can extend to other systems and platforms.
R&D projects
For problems with no proven solution yet. We test approaches on your data and work toward a prototype in agreed milestones. At each milestone we tell you plainly what looks feasible and what doesn't.
Software development & consulting
For problems with known solutions. We build production software on proven methods, or advise your team on architecture, tooling and model choice.
Funded research
Joint R&D projects backed by national and international research funding, with industry and academic partners.
Corporate training
Training programs tailored to your team. Topics include everyday work with AI assistants, product development, integration and strategy.
Frequently asked questions
Can the system run on a closed network without internet access?
Yes. Models are trained and run entirely on your own network, and no data leaves it.
Can AI models run on a platform's own hardware?
Usually, yes. Models are made smaller and faster to fit the hardware, and their accuracy is measured again on the device.
Does AI make the decisions?
No. The system detects, classifies and prepares information. A person always makes the decision.
How is a model tested when there is little real data?
Real data is combined with synthetic and simulated data, and the model is tested on real cases it has not seen before.
Where should a defense company start with AI?
Usually with one well-defined task whose results are easy to measure, such as a single type of sensor data or a frequently used set of documents.
From our blog
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When should you use synthetic data?
Synthetic data can help when real data is scarce, sensitive, or unavailable. When is it worth using, and how can you tell whether it improves results?
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