Generative AI

Generative AI

The next generation of AI systems has arrived, and with this arrival comes an increase in productivity for knowledge workers. Generative AI does not just refer to an isolated technology like ChatGPT; Generative AI software can be integrated with other technologies to achieve new capabilities for your company. 

At Wizeline, our expert AI and Data teams can help you make the most out of cutting-edge advancements in Generative AI. As a leader in Generative AI consulting, we are taking an active role in helping our clients productionalize Generative AI to take their business to the next level. Let our experts help your business design and develop brand-specific use cases to accelerate the adoption of applied Generative AI.

Our Experts Have Experience With the Most In-Demand Generative AI Software

Wizeline Generative AI Map

Top 50 AI Tools

We created the Wizeline Generative AI Map to help businesses understand the AI product landscape and use cases, categorizing the tools by content type (text, image, video, coding, etc.), business functions (marketing, people operation, software development, finance, design, etc.), and tool type described below.

  • Generative AI Models: A class of artificial intelligence systems designed to create new data, content, or designs based on input data or specified parameters. These models generate various outputs, such as text, images, music, and complex designs, by learning patterns and structures from massive datasets. Examples of popular generative AI models include GPT (Generative Pre-trained Transformer), DALL-E, and StyleGAN.
  • Generative AI Cloud Services: Publicly available services or APIs granting access to generative AI models. One example is ChatGPT, which provides access to GPT models owned by OpenAI through a chatbot interface or API.
  • Generative AI Mashups: Services connecting multiple models and enabling complex workflows. One such example is Toolformer.
  • AI-Native Products and Services: Products or applications with generative AI embedded within their features and workflows. The user benefits from the models without seeing the implementation details. Examples include Notion.AI, Uizard, and GitHub Copilot.
  • Data and Integration Services: A combination of data services, APIs, and middleware services that enable applications to interact with the rest of the components in the AI landscape. This layer facilitates the construction of business logic and data storage, allowing applications to control how data flows into generative AI services and produce the best experiences for end users within the applications.



Benefits of Generative AI

Accelerate & Customize Content Generation

Accelerate & Customize Content Generation

Generative AI is changing creative work processes. This technology has the capacity to create text, images, and videos. Gen AI software allows companies to automatically generate and customize content, such as blog posts, program codes, or social media posts.

Enhance Customer Experience

Enhance Customer Experience

Generative AI can help your company identify customer needs and provide personalized solutions. From automated customer support to faster product development, Gen AI can revolutionize your customer's experience.

Save Labor Costs

Save Labor Costs

Generative AI can help businesses reduce costs by streamlining manual and repetitive processes, reducing manual labor, optimizing resources, and identifying areas for improved business efficiency.

Generative AI models don’t have a personality; however, they are trained to learn patterns from tons of data, and the data it has access to is reflected in the output. Just as humans learn from experience and school, a generative AI model learns only from the data it can access. It can create amazing things from images, videos, augmented reality, and much more.

— Hayde Martinez, Wizeline Senior Technology Program Lead

Wizeline’s Generative AI Solutions


Product Design and Development

Leverage Generative AI software to automate certain aspects of the process, such as generating wireframe options, creating simulations, and optimizing designs for specific goals.


Software Development Lifecycle Automation

Integrate Generative AI software into different stages of the SDLC to help automate certain tasks, such as generating code or optimizing performance, or to analyze data and generate insights that can help inform project scope and requirements.


Business Process Optimization

Incorporate Generative AI to optimize and streamline business processes, making them more efficient and effective. By automating analysis and workflows, the software can process data and information to identify patterns and key takeaways and improve decision-making.

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