Visual
AI
Visual AI makes it possible to analyze, understand, and generate image and video material with high speed and precision. Instead of manual reviews, you can use AI for image recognition, quality control, error detection, and sorting – or to create new visual content based on your needs.
- Examples of visual AI
From question to action – automatically and intelligently
Image recognition and quality control
This solution uses AI to identify objects, patterns, and anomalies in images – for example, on production lines, in scans, or in uploaded files. The model can detect errors, defects, or quality issues and flag them automatically. This ensures higher quality, fewer manual checks, and faster processing, while also making the workflow more consistent and scalable.
AI for scanning damages or irregularities
This solution uses AI to review images or video for damages, wear, or defects – for example, in vehicles, buildings, materials, or technical installations. The system automatically highlights relevant areas and classifies types of damage. It saves time in the assessment process and increases accuracy – whether used as support for humans or in full automation.
Automatic sorting of image content
AI can analyze large volumes of images and sort them based on content, context, or predefined categories. This could include classification by subject, product variant, condition, or use case. The solution reduces manual work, ensures consistency, and makes it easier to search, archive, and reuse visual data at scale.
Generative image production
With generative AI, you can create new images from text descriptions, examples, or combinations of existing materials. It is used, for example, in marketing, product visualization, image variations, or creative concepts. The solution enables fast production of visual material – without the need for photography or manual editing.
3D modeling and simulation
This solution uses AI to generate or adapt 3D models based on images, scans, or design parameters. It can also simulate variations, placements, or collisions in virtual environments. It is used, for example, in product development, construction, logistics, or visualization – where early insights and realism are crucial.
Video analysis and object detection
AI can analyze video material in real time or in batch mode and identify objects, movements, or events. This could include people, items, defects, or behaviors that need to be detected, counted, or tracked. The solution is used in areas such as surveillance, security, production, and logistics – eliminating the need for manual review of video recordings.
- Benefits of AI
Benefits of visual AI and image analysis
AI makes it possible to analyze images and video with a speed and precision that surpass human evaluation – especially when dealing with large volumes of data or repetitive tasks. This enables you to react faster, detect more, and document better.
At AIgentur, we help businesses leverage visual AI for everything from quality control to generation and categorization. Here are five typical benefits our clients experience:
Time savings
AI is able to analyze very large amounts of image and video data automatically – without the need for any manual review - saving precious time.
Accurate assessments
The models are trained to detect even small defects, damages, or patterns that might be overlooked by humans.
Consistent quality assurance
AI evaluates based on the same criteria every time – ensuring a stable and documentable level of control.
Automated sorting
Images and videos can be automatically categorized, tagged, and archived – making them easy to find and use.
Scalability
No matter how many images or recordings you have, AI can analyze them quickly – without requiring additional staff resources.
- Our process
How we create AI solutions that work in practice
At AIgentur, we work according to a structured and transparent process that ensures the solution fits your needs – and creates value from day one. We believe in close dialogue, clear agreements, and continuous quality assurance.
The process is carried out in six phases and can be adapted to both small and large projects.
1. Scoping
2. Approval
3. Development
4. Test
5. Feedback
6. Implementation and onboarding
- Contact us
Ready to get started?
Let’s take the first step together
At AIgentur, we meet you where you are and help transform your goals and challenges into concrete solutions.
You may already have an idea or a specific need. Or you may simply be curious about how AI can be applied in your business.
Either way, we’re happy to have a no-obligation conversation – and show you how a structured and flexible approach can deliver results.
Book a meeting
- Frequently asked questions
From curiosity to clarity in seconds.
What is visual AI?
Visual AI refers to artificial intelligence that works with images and video. It covers everything from image recognition and object detection to automated image sorting, error detection, and the generation of new visual materials.
How does image recognition with AI work?
AI models are trained on large amounts of image data and learn to recognize patterns, objects, and anomalies. They can identify specific elements in an image – for example, a defect, a product, or damage – and respond or classify automatically.
What types of images and video can AI analyze?
AI can work with product images, document photos, surveillance video, drone footage, 3D scans, and much more. The model is adapted to your domain and the data types you use – whether it’s visual archives or live feeds.
Do we need to annotate the images ourselves?
Not necessarily. We can assist with annotation or use existing datasets as the foundation for the model. For larger projects, we can set up automated pre-annotation, which is then reviewed and approved – making training faster and less resource-intensive.
Is AI better than humans at visual inspection?
AI is not better at everything – but it is faster, more consistent, and resistant to fatigue. It is particularly effective with repetitive tasks and large volumes of data, where it can serve as support or as the first step in quality control and documentation.
How is a visual AI model trained?
The model is trained on images or video where the relevant elements are annotated. It is then adjusted and tested until it reaches high accuracy. We handle the entire process – from dataset preparation and setup to integration and evaluation.
