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Generative AI
Development
Generate custom content on demand and automate resource-intensive tasks using cutting-edge generative AI technology.
Our Generative AI Services
Generative AI
Consulting
Identify opportunities for automation and business optimization.
Model
Training
Optimize generative AI models through transfer learning.
Custom
Development
Develop intelligent chatbots, virtual assistants, and recommendation engines.
Generative AI
Integration
Integrate generative AI systems with existing business applications, such as CRM.
Our Strengths
AI
Expertise
A strong team consisting of data scientists and machine learning professionals experienced in deep learning and NLP forms the backbone of our generative AI services. Their combined expertise helps us come up with high-performing solutions.
Custom
Solutions
Generative AI is highly adaptable to business needs, but there's no one-size-fits-all. We create impactful solutions by tailoring them to your unique requirements, ensuring they are customized and optimized for your use case.
Multidisciplinary
Team
To successfully launch and operate your Generative AI applications, you can leverage the expertise of a strong team consisting of business analysts, software developers, UI/UX designers, cloud consultants, and other professionals.
AI Expertise
A strong team consisting of data scientists and machine learning professionals experienced in deep learning and NLP forms the backbone of our generative AI services. Their combined expertise helps us come up with high-performing solutions.
Custom Solutions
Generative AI is highly adaptable to business needs, but there's no one-size-fits-all. We create impactful solutions by tailoring them to your unique requirements, ensuring they are customized and optimized for your use case.
Multidisciplinary Team
To successfully launch and operate your Generative AI applications, you can leverage the expertise of a strong team consisting of business analysts, software developers, UI/UX designers, cloud consultants, and other professionals.
Types of Generative AI Models
Generative AI, a powerful subset of artificial intelligence, is capable of producing new data, text, images, and videos with impressive accuracy. It uses models like generative adversarial networks (GAN) and variational autoencoders (VAE) to imitate human creativity in the media it generates.
Based on their input / output, AI models can be categorized into text-based models, video models, audio models, and more.
Text-Based Models
Large Language Models (LLMs) generate contextually relevant text from prompts or partial sentences and perform summarization, translation, and question-answering. Small Language Models (SLMs) are computationally less intensive. They can handle specialized language tasks and knowledge management while safeguarding intellectual property.
Video Models
Variational Autoencoders for Video (VAE-Video) models such as Video Pixel Networks and MoCoGAN can learn motion representations and generate realistic and diverse video content. Often they are used along with CNNs.
Audio Models
Audio Generative Adversarial Networks (Audio-GANs) vary in their capability to handle different types of audio such as speech, music, special effects, etc. Examples include GANSynth and HiFi-GAN.
3D Models
3D Generative Adversarial Networks (3D-GANs) generate three-dimensional objects, complete 3D shapes etc. Some leading models are EG3D and AtlasNet.
Image Models
Deep Convolutional Generative Adversarial Networks (DCGANs) are widely used for image generation and editing. Progressive GAN and BigGAN are some popular examples.
Multimodal Models
Multimodal models, such as CLIP and DALL-E, take one or more input types and generate a different output type. CLIP takes images and text to generate subtitles. DALL-E generates images based on textual descriptions.
Code Generating Models
Models like GPT-Code and Deep Coder are specifically designed for code generation. These models can generate code snippets, functions, or even entire programs based on prompts or task specifications.
Text-Based Models
Large Language Models (LLMs) generate contextually relevant text from prompts or partial sentences and perform summarization, translation, and question-answering. Small Language Models (SLMs) are computationally less intensive. They can handle specialized language tasks and knowledge management while safeguarding intellectual property.
Video Models
Variational Autoencoders for Video (VAE-Video) models such as Video Pixel Networks and MoCoGAN can learn motion representations and generate realistic and diverse video content. Often they are used along with CNNs.
Audio Models
Audio Generative Adversarial Networks (Audio-GANs) vary in their capability to handle different types of audio such as speech, music, special effects, etc. Examples include GANSynth and HiFi-GAN.
3D Models
3D Generative Adversarial Networks (3D-GANs) generate three-dimensional objects, complete 3D shapes etc. Some leading models are EG3D and AtlasNet.
Image Models
Deep Convolutional Generative Adversarial Networks (DCGANs) are widely used for image generation and editing. Progressive GAN and BigGAN are some popular examples.
Multimodal Models
Multimodal models, such as CLIP and DALL-E, take one or more input types and generate a different output type. CLIP takes images and text to generate subtitles. DALL-E generates images based on textual descriptions.
Code Generating Models
Models like GPT-Code and Deep Coder are specifically designed for code generation. These models can generate code snippets, functions, or even entire programs based on prompts or task specifications.
Retrieval-Augmented Generation (RAG)
RAG integrates LLMs with external knowledge systems, retrieving information from databases or document libraries to provide contextually accurate responses. RAG Fusion takes this further by generating multiple queries per input and merging the results, improving relevance, especially for complex or ambiguous queries.
Moving RAG prototypes to production involves challenges such as managing hallucinations and ensuring consistency. At QBurst, we specialize in building production-grade RAG-based solutions using optimized vector database generation and advanced prompt techniques. By creating robust pipelines, our data engineering team ensures the delivery of high-quality data to the model.
Generative AI Use Cases
Generative AI can work effectively with smaller amounts of data or examples, making it accessible to organizations that may not have large datasets readily available. Similarly, APIs are available to streamline the integration process. These reduce the barriers to entry and allow organizations to start leveraging AI capabilities sooner.
Domain-Specific
AI Assistant
AI agents can autonomously interact with users, execute commands, and provide advanced decision-making capabilities. As they are trained on domain-specific data, they can be tailored for any task or industry, delivering more accurate responses and improved decision-making support.
Video-Based
Monitoring
Generative AI enhances video-based monitoring by reconstructing events from multiple camera feeds and detecting threats, improving response times and situational awareness. It also stitches footage for panoramic views, ensuring comprehensive coverage of large areas like stadiums.
Image
Synthesis
Generative AI enables the creation of realistic and diverse visual content for VR applications, movies, video games, etc.
Video
Editing
Scene segmentation, object removal, or color grading are a few use cases where generative AI reduces manual effort and makes post-production easier.
Voice
Generation
As generative AI can generate natural-sounding human speech, it can be used to create voice assistants, audiobooks, and synthetic voices for people with speech impairments.
Translation and
Transcription
Generative AI can automate language translation, content localization, and transcription of audio and video content.
Data
Augmentation
Synthetic data can be generated to expand training sets, improve model performance, and simulate rare scenarios for testing and validation.
Robotics
Generative AI facilitates co-design between humans and machines. It can be applied to generate motion trajectories, control policies, or behavior models for autonomous robots.
Customer
Service Chatbots
Generative AI can power quality conversational agents through better inference-making from user inputs and generating human-like responses.
Domain-Specific AI Assistant
AI agents can autonomously interact with users, execute commands, and provide advanced decision-making capabilities. As they are trained on domain-specific data, they can be tailored for any task or industry, delivering more accurate responses and improved decision-making support.
Video-Based Monitoring
Generative AI enhances video-based monitoring by reconstructing events from multiple camera feeds and detecting threats, improving response times and situational awareness. It also stitches footage for panoramic views, ensuring comprehensive coverage of large areas like stadiums.
Image Synthesis
Generative AI enables the creation of realistic and diverse visual content for VR applications, movies, video games, etc.
Video Editing
Scene segmentation, object removal, or color grading are a few use cases where generative AI reduces manual effort and makes post-production easier.
Voice Generation
As generative AI can generate natural-sounding human speech, it can be used to create voice assistants, audiobooks, and synthetic voices for people with speech impairments.
Translation and Transcription
Generative AI can automate language translation, content localization, and transcription of audio and video content.
Data Augmentation
Synthetic data can be generated to expand training sets, improve model performance, and simulate rare scenarios for testing and validation.
Robotics
Generative AI facilitates co-design between humans and machines. It can be applied to generate motion trajectories, control policies, or behavior models for autonomous robots.
Customer Service Chatbots
Generative AI can power quality conversational agents through better inference-making from user inputs and generating human-like responses.