Generative AI

Create without limits

Generative AI models can help your business create unique and innovative content, including text, images, videos, designs, and sounds. This is a big change in deploying AI, because it’s no longer just analyzing existing content but actively generating new content based on user specifications.

We have the experience of developing ethical and high quality Generative AI applications, so we can provide technical expertise to keep your business up to date with the latest innovations.

Custom generative model creation and fine-tuning

Looking for a fine-tuned Stable Diffusion model that generates specific art styles, stock photos, or logos? We'll take care of collecting and pre-process the training data, and then fine-tune the model to create images in your desired style. Check out our blogpost on fine-tuning DALL-E for more info.

Video de- or reidentification

Need to protect witnesses in a documentary or create a younger version of an actor's face for a movie? Our advanced deepfake technology can replace any face and help you create high-quality footage that stands out. Our solutions have even been nominated for the NPO innovation awards.

Artificial lip syncronization

Need to sync dubbed speech to your animation or film? Our AI-driven solution can directly map lip movements onto your video images, even if the speech is artificially generated, to create a natural end result.

Deepfake detection

Looking for a way to detect deepfake images, speech or text? We can create a customized system to identify potentially manipulated media and restore confidence in the accuracy of your information.

And many more !

New applications of generative AI are appearing almost every day. If you're looking for help with generative design, pose transfer, virtual try-on, creating artificial images, or other things like that, we can help! Contact us and we'll give you a tour of the exciting world of generative AI.

Typical challenges

Building a computer vision solution can be complex process. Let's take a look at some common challenges.

Data quality and availability

To create accurate output, generative AI needs diverse and high-quality data. We can help build efficient data pipelines to meet these needs for optimal performance.

Model complexity and performance

Generative AI models need a large amount of computing power. As a result, training and implementing them can be difficult and time-consuming.

Interpretability and explainability

It can be difficult to understand how generative AI models generate output or solve problems. Luckily, we always try to effectively analyze and optimize the model through our expertise and tools.

Hardware and software infrastructure

Generative AI models need a lot of computing resources, including special hardware and software infrastructure, to be trained and operated successfully.

Speed of research

Keeping up with the latest developments in generative AI can be challenging due to the many guidelines to follow for training, deployment, and interaction. That’s why we stay up-to-date with the latest trends to make it easier for you.

Ethical considerations

Generative AI has the potential to create valuable content, but responsible use is important to avoid unintended harm and promote ethical deployment.

High level outline of the solution

Generative AI comes in different forms depending on the situation.

Sometimes, you can use a pre-trained generative model.

Other times, you can fine-tune a pre-trained model to fit your needs.

And sometimes, you need to train a custom model from scratch.

Some of the building blocks of our generative AI solutions are:


For generative AI to deliver the best results, it needs a lot of diverse and high-quality data. This is important for the model to learn patterns and generate new output.

Model architecture

The architecture of the AI model affects how it generates new content. There are different types that work better with specific data and situations. Some of the most commonly used architectures include GANs, VAEs, autoencoders, and Transformers.


In the training phase, the model learns from data and tweaks its parameters to generate new output. This can take a lot of computing power and may need specialized hardware and software infrastructure.


Following training and evaluation, the model is ready to generate new output. Based on available resources and infrastructure, it can be deployed on-premises or in the cloud.


Prompting is the process by which you tell generative AI what you want it to create. The better you describe your desired outcome, the better the AI's output will be. It usually involves providing feedback and making changes to the AI's instructions.

Fine-tuning and adaptation

Fine-tuning is a method of modifying a generative AI model to make it create things in a specific style. To train the model to work well for what you want, you use a small but carefully curated set of data. Fine-tuning can be used for image or text generation, depending on your needs.

You can combine these building blocks in different ways to make custom Generative AI solutions for specific needs. Solutions can range from simple models that generate simple images to advanced models that generate music or natural-sounding text.

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Connect with our experts in Generative AI solutions

Contact us to learn more about the latest AI innovations, including Chat GPT, Large Language Models, Foundation Models, and More.

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