Determining an exact cost can be challenging, as pricing depends on various factors. At our generative AI company, project costs typically start at $10,000 and increase based on specific requirements.

Pricing varies depending on the complexity of the solution whether you're consuming existing models or customizing them the nature of your business case, and the type of service (consulting vs. development). Since we build on top of established large language models like GPT and DALL·E, and tailor them to your unique needs, our approach is more cost-effective than training models from the ground up.

Generative artificial intelligence is a branch of machine learning where algorithms are built to create new data or content based on existing training data. This technology can generate various types of content, including text, audio, and video.

A common example is large language models neural networks with billions of parameters trained on massive amounts of unlabelled data to produce intelligent outputs.

Businesses leverage generative AI across a wide range of use cases, from internal operations to customer-facing services, driving both efficiency and innovation.

Generative AI use cases span the following areas:
• Generative AI customer service
• Marketing and sales
• Administrative tasks
• Recruitment
• Business and customer analysis
• Fraud detection
Leading generative AI companies use language models to automate customer support and deliver personalized interactions. Many also leverage the technology to process large volumes of data, generate reports and alerts, and manage documentation efficiently.

These models also power chatbots, summarize content for enhanced social listening, and monitor consumer sentiment across multiple channels.

The technology made inroads in many industries, including retail, healthcare, banking, education and manufacturing.

• Generative AI healthcare – patient-facing assistants, enhanced clinical decisions, easier data management.
•Banking – automated customer service, optimized loan origination, fraud detection.
• Retail – real-time personalization, self-service capabilities, streamlined product research and analysis.
• Education – personalized learning, curated materials, supplementary solutions.
•Manufacturing – product defect analysis, supplier management, supply chain management.

To generate accurate results, a large language model requires training on terabytes of data. Using neural networks, machine learning, and natural language processing, it learns patterns and structures within the data to create new content. What sets these models apart is their ability to incorporate various learning techniques, making them highly adaptable tools for addressing a wide range of business challenges and supporting diverse applications.

Yes, Chat GPT is a type of generative artificial intelligence called a large language model.