Building Artificial Intelligence innovation, where we specialize in crafting bespoke AI models to revolutionize the way you do business. Utilizing the power of cutting-edge technologies like Nvidia AI models, ChatGPT, LLAMA, and other large language models, we offer tailored AI solutions that seamlessly integrate into various aspects of your operations. Our expertise extends beyond just development; we provide in-depth consulting to ensure that AI is embedded effectively into your immersive technologies, enhancing user experience and operational efficiency. Our services cater to a wide range of industries – from healthcare, where AI can assist in diagnostics and patient care, to finance, for advanced data analysis and prediction; from retail, enhancing customer engagement and personalization, to manufacturing, for optimizing production processes. As you delve further into our page, you’ll discover how our AI expertise can be a game-changer for your business, enabling smarter, faster, and more intuitive decision-making processes. Let us help you harness the power of AI to stay ahead in today’s rapidly evolving digital landscape.

Generative AI

Generative AI refers to a subset of artificial intelligence technologies that can generate new content, such as text, images, music, or even code, that is similar to human-generated content. Unlike traditional AI models that are designed to recognize or classify data, generative AI models learn from vast amounts of data to understand patterns, styles, or structures, enabling them to produce new, original outputs based on the learned information. This capability is powered by advanced machine learning techniques, particularly deep learning models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which can create highly realistic and creative outputs. Generative AI has a wide range of applications, from content creation and design to solving complex scientific problems.

Data Capture and Processing for AI

Data capture and processing are critical steps in developing and deploying AI and Generative AI systems. These processes involve collecting data from various sources, cleaning, and transforming it into a format that AI models can effectively learn from. Here’s an overview of some common techniques used in these stages:

  • Data Scraping / Web Scraping
  • Capture from devices
  • APIs and Databases 
  • Generated Content
  • Data Cleaning
  • Data Transformation
  • Feature Engineering
  • Data Augmentation
  • Synthetic Data

 

Synthetic Data Creation

Synthetic data creation involves generating artificial data that mimics real-world data, enabling AI and machine learning models to learn and make predictions without using actual data. This process is crucial when real data is scarce, sensitive, or expensive to collect. Techniques like Generative Adversarial Networks (GANs), computer simulations, and procedural generation are commonly used to create realistic images, text, or numerical data that closely resemble authentic datasets. By doing so, synthetic data helps overcome privacy concerns, biases in real data, and enhances the diversity and volume of data available for training AI models, thereby improving their accuracy and robustness in real-world applications.

Scenegraph utilises the latest in visual rendering to create synthetic data sets used with training AI. Visual Rendering is so photorealistic using Unreal Engine, NVIDIA Omniverse, Blender that it can render out many images used to train AI algorithms. Book a scoping call to discuss your data creation needs.

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