Automotive
AI in automotive has to meet strict safety requirements and often run on the hardware inside the vehicle. Within those limits it can support driver assistance, check quality on the production line, detect faults from vehicle data and plan fleet operations. It also has to work reliably across many vehicle variants and road conditions.
Challenges
Safety requirements come first
Systems that affect how a vehicle behaves have to meet the sector's functional safety and approval requirements, and be tested and documented against them.
Models run on the vehicle's hardware
Many systems have to run in the vehicle itself, with limited computing power and energy, and respond in real time.
Results hold up across variants and conditions
Vehicles come in many models and sensor setups and are driven on every kind of road and in every kind of weather. Models have to be tested across these, including rare situations.
Typical problems
Driver assistance and perception
Models that detect vehicles, pedestrians, lanes and signs from camera, radar and lidar data, and run on the vehicle's own hardware.
Quality control in production
Checking parts, paint and assembly with cameras in vehicle and component production, and flagging defects for review.
Fault detection from vehicle data
Spotting early signs of faults in vehicle and fleet data, and helping service teams find the likely cause.
In-vehicle assistants
Voice assistants that answer drivers' questions and control vehicle functions, and keep working without a constant connection.
Fleet and route planning
Forecasting demand and planning vehicles, routes, maintenance and charging for fleets and transport services.
Our research areas
See all research areasLightweight models
Making perception and assistant models small and fast enough for in-vehicle hardware.
Learning from limited data
Learning rare road situations and faults from few real examples, supported by synthetic data.
Multimodal understanding
Combining camera, radar, lidar and vehicle signals so that each one makes up for the others' blind spots.
Interpretability & evaluation
Measuring how models behave across variants and conditions, and finding where and why they fail.
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.
- Synthetic dataGenerated training and test data for cases that are rare, sensitive or missing in your real data.
- Anomaly & fraud detectionSystems that flag the unusual transactions, failures and faults worth a closer look, even with few confirmed examples.
- Speech & voice AITranscripts and structured information from calls, meetings and spoken requests, in Turkish and English.
- Decision optimizationSchedules, routes, prices and stock plans that respect your constraints and leave planners in control.
Work with us
Work usually starts with one function, one production line or one fleet, and the system runs on your own infrastructure and hardware. Once the results are measured, the scope can extend to other models, lines and services.
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 AI models run on in-vehicle hardware?
Yes. Models can be made smaller and faster so that they fit the computing power and energy limits of the vehicle.
Where does vehicle and customer data stay?
On your own infrastructure. Models are trained and run there, and the data does not leave the company.
How is safety handled in AI systems for vehicles?
Models are tested across vehicle variants, conditions and rare situations, and the results are documented. This feeds into the safety and approval processes the vehicle has to go through.
Where should an automotive company start with AI?
Usually with one well-defined function whose results are easy to measure, such as a single inspection step or one type of fault.
Who runs the system after handover?
Your own engineers. The system is handed over with its documentation, and we can also provide support after that if needed.
From our blog
All articles
Why AI projects stall after the prototype
AI makes drafts and prototypes cheap. The advantage moves to organizations that can choose the right work, stop the rest, and finish what matters.
Read article
You're not doing AI. You're doing integration.
Most AI projects depend on integration. Company data, business rules, human review, and workflow design turn a capable model into a useful product.
Read article
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?
Read article