Presentation products have gained much more room for growth after being combined with AI capabilities. In the past, users had to collect information, write slide content, and adjust formatting by themselves. Now this entire workflow can be handed over to AI and completed through automated workflows.
At the same time, the rise of multimodal large models has created many new product opportunities. Today, when users create a presentation, they only need to enter a topic and can directly receive the final result. AI brings an end-to-end product experience.
This article analyzes the trends and opportunities in AI Presentation products market from the perspective of technical architecture evolution. In the end, I will also share a practical AI product case: how I built and launched a Presentation Agent product by leveraging the unique capabilities of multimodal large models, the latest Presentation technical architecture, and emerging product opportunities.
1. The Evolution of Product Technical Architecture
The Presentation product category has long been dominated by Microsoft Office PowerPoint. Later, Google Slides and China’s WPS Presentation entered the market. But after the improvement of AI capabilities, the Presentation product market began to see new development opportunities.
Today, AI can not only call Deep Research capabilities to help users search and collect information, but also use the collected content to break down a Presentation outline. Finally, it can combine text-to-image models to generate a high-quality Presentation proposal with rich visuals, complete content, and smooth logic.
The value of AI lies in taking over the most complex 80% of the work: information collection, information processing, complex image generation, and content formatting. This saves users a large amount of time and energy, allowing them to focus more on topic selection and content quality.
First-Generation Architecture: Prompt-Based Content Generation
For users who often create Presentations, if most of their energy is spent adjusting formatting, they have much less time to polish the content itself. Therefore, the first generation of AI Presentation product architecture focused on using AI to quickly generate simple text content, image content, and formatting adjustments inside slides.
There were two main approaches.
The first was to let an LLM write and complete the content inside the Presentation based on system prompts, and then use an AI text-to-image model to generate images for the slides.
The second was to use HTML code to display the generated content in a Presentation-like style, allowing users to edit it online. This was essentially like building a webpage styled as a Presentation.
Second-Generation Architecture: Multi-Model Orchestration
In the early stage, ChatGPT’s text processing capabilities were ahead of other large language models, so AI Presentation products often used it to process written content. In the AI image field, the stronger options were mainly the open-source Stable Diffusion model and the closed-source Midjourney model.
But as large model capabilities continued to evolve, more and more AI models became available, each with its own strengths. As a result, products could adapt different models to different user needs and use them for text and image processing. This became the second-generation architecture: multi-model orchestration.
AI Presentation products dynamically select, combine, and schedule multiple models based on the user’s task, goal, and contextual constraints, allowing them to collaborate according to a predefined workflow. Simply put, different models perform the parts they are best at and work together to complete complex tasks.
For example, Gamma, a mainstream AI Presentation product, uses a multi-model orchestration architecture. When users choose to generate a Presentation in Gamma, they only need to enter a topic. The product then calls large language models to break down the topic, generate an outline, write copy based on the selected text density, and generate images while producing the Presentation.
Multi-model orchestration is not only about choosing the most suitable model based on capability. It also requires deciding when to use a model, how many times to call it, and how to pass the output to the next model so that the entire content workflow can be completed and eventually displayed to the user as a Presentation.
For example, Claude may be selected for copywriting, while ChatGPT may be used for outline breakdown. If the user wants images with stronger artistic expression, Midjourney may be called; if the user wants another style, such as realism, Flux may be used instead. During this generation process, the system also needs to consider how to output the copy generated by the language model as structured content and pass it to the AI text-to-image model for the next step.
Third-Generation Architecture: Agentic End-to-End Architecture
Presentation generation is a complex task that includes content understanding, structural planning, multimodal content generation, and layout composition. Current AI already has relatively mature text and image generation capabilities, and multimodal large models can often handle both. Therefore, the product architecture can further evolve into an Agent-based end-to-end execution model.
In this process, the Presentation Agent understands the input topic, breaks down the task, and automatically completes content structure planning, text generation, image generation, and layout work for each slide. In the default mode, the step where users confirm the Presentation outline can even be skipped, and the Agent can directly plan the entire Presentation based on the topic.
The Agent calls different models and tools to collaborate, ultimately delivering a complete Presentation document to the user and minimizing the user’s operation cost. Although users experience this as an end-to-end product flow, internally the system is still a decomposable, orchestratable, and controllable multi-step Agent execution process.
From the user’s perspective, they only need to enter a topic, and the rest can be handled by AI. Of course, after the Presentation is generated, users can still directly adjust details on the slides themselves. For slightly more complex content changes, they can also continue using AI to help complete the edits.
2. Competitive Architecture Analysis of AI Presentation Products
After mapping out the technical architecture of AI Presentation products, I also tested several mainstream AI Presentation products. On one hand, I wanted to understand their current technical architectures. On the other hand, I wanted to identify future opportunity points for this category.
1. Gamma
Gamma is a leading AI Presentation product overseas. Since its major product redesign last September, its architecture has been transitioning from the second generation to the third generation.
The product can generate Presentations through multi-model orchestration, and it can also call multimodal large models in Studio mode to generate integrated text-and-image slide content in one click. Users can use AI capabilities to modify generated content, but they cannot manually edit the details inside the Presentation.
Generated example:
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I entered a topic in Gamma and asked it to call the Nano Banana Pro model to automatically generate text and image content and display it as one Presentation page.
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After generation, I could also “direct” the AI through the chat box on the right to adjust the content.
2. Kimi Slides
Kimi is a leading large model company in China, and its product homepage also includes a Presentation generation feature. After trying it, I found that its product architecture belongs to the second generation.
The large model calls tools to help users search for information related to the topic. It can even call Deep Research capabilities to study the topic, first output a Presentation outline, and then generate the Presentation. The background image for each slide uses fixed content and formatting, while the large model generates corresponding copy inside the text modules.
Generated examples:
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Direct content generation: I asked Kimi to generate a Presentation based on the visual template I selected and the topic I entered. I could manually edit the text inside, but I could not modify the image content.
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Deep search first, then content generation: I asked Kimi to deeply search for information related to the input topic, organize it, and then generate the Presentation. Similarly, I could manually edit the text content, but the Presentation itself did not generate images.
3. AI Presentation
AI Presentation is a leading product in China, and it also has an overseas version. The Chinese version is transitioning from the second-generation architecture to the third-generation architecture. Users can enter a topic and select a visual style to generate a Presentation, or they can directly choose to let AI generate the content autonomously.
The overseas version is also transitioning from the second generation to the third generation. Users can continue using the previous content generation method, or use the Nano Banana model to generate Presentation content, including both images and text.
Generated example, China version:
- I entered a topic and asked it to directly call the Seedream 4.5 model to generate Presentation content.
- The overall visual effect was acceptable, but the generated text was not clear enough and users were not allowed to manually edit it.
Generated example, overseas version:
- I entered a topic and asked it to use the Nano Banana Pro model to generate Presentation content.
- The generated visual effect was too heavy and overemphasized visual expressiveness.
- Users can click into editing mode to edit the text on the page.
- However, some text appears differently in editing mode compared with the original effect, such as text color and font.
4. ListenHub AI
ListenHub is an AI audio content creation platform. Users can also first generate Presentation pages from text on the platform, then synthesize audio or video content. The Presentation generation capability uses the third-generation technical architecture. Users only need to enter text, and the following task understanding, analysis, and execution are all completed by the Nano Banana Pro model.
Generated example:
- I entered a topic and directly generated an integrated text-and-image Presentation page.
- The description text below the page is both the image prompt and the text content for audio and video.
- Users cannot modify the text inside the image, and they cannot ask AI to regenerate the image based on new requirements.
- Users can download the content as a Presentation file, but it still cannot be edited. Each page is displayed as a background image.
5. WPS AI Presentation
WPS has launched an AI Presentation feature that allows users to generate Presentations online with AI. Its product architecture is currently transitioning from the second generation to the third generation.
It has also introduced a Nano Banana Pro-based capability that converts images into Presentations, allowing users to further edit the content in Presentation format.
Generated example:
- After I uploaded an image, it directly converted it into an editable Presentation. The text inside the image became text boxes, so it could be edited on the web.
- Some text in the image was not recognized and converted into corresponding text boxes, but the overall effect was quite good.
Conclusion: leading AI Presentation products have all begun transitioning toward the third-generation architecture. Based on current product results, they can already generate integrated text-and-image content from topic-related prompts. However, the ability for users to freely edit the generated output still needs improvement.
3. Future Product Opportunities and Presentation Agent Product Demonstration
After researching the leading mainstream AI Presentation products, I found that the future opportunity lies in calling multimodal large model capabilities to deeply understand user intent, generate Presentations that match the logic of the content, and still allow users to edit the output afterward.
The product needs both an Agent execution process and an end-to-end user experience, while also giving users room to modify the results. It can even use the generated Presentation text-and-image content as a foundation for creating content in other modalities, such as educational animation videos and narration audio.
Based on this opportunity analysis, I tried to build a Presentation Agent product that helps users create text-and-image content in one click, while also allowing them to edit the content online.
1. Product Positioning
From a paid knowledge community, I learned that some knowledge creators and personal IP creators face a major problem when producing content: how to make their Presentation layouts more visually polished and more efficient to operate.
These knowledge creators are usually not very good at Presentation layout design or visual adjustment. They need to spend a lot of time and energy on it, and even then they may not achieve a satisfactory result.
These creators regularly organize their knowledge systems into Presentation format for daily livestream sharing and offline courses. Therefore, I independently developed a web product specifically designed for knowledge creators to generate the knowledge-map Presentations they need in one click.
Comparison with existing mainstream AI Presentation products:
| | | | | | | | |---|---|---|---|---|---|---| |Knowledge|Knowledge Map Presentation|Gamma|Kimi|AI Presentation Overseas Version|ListenHub|WPS AI Presentation| |Product positioning|AI-generated Presentation product|AI-generated Presentation product|AI-generated Presentation product|AI-generated Presentation product|AI audio podcast generation product|AI recognizes text in images and generates editable online Presentations| |Architecture stage|Third-generation technical architecture|Transitioning from second-generation to third-generation architecture|Second-generation technical architecture|Transitioning from second-generation to third-generation architecture|Third-generation technical architecture|Transitioning from second-generation to third-generation architecture| |Technical capability|Users can manually modify text content, use AI to modify it, or download it locally for further editing|Has Agent capabilities, but users cannot manually edit Presentation page content and can only modify it through AI|High-quality text inside the Presentation, but lacks image content|Has Agent capabilities, but the generated Presentation is visually too heavy; users can manually edit text|Generates images based on user prompts with good results, but users cannot modify the image content|Converts uploaded images into Presentation content and supports online editing, but cannot directly generate integrated text-and-image Presentations|
2. Product Technical Architecture
For the product architecture, I directly tried the third-generation architecture and implemented end-to-end content processing. In other words, I used an Agent approach to let the product directly output the final result based on the user’s input content.
Product technical architecture:
Of course, the entire process for handling user input copy is also a multi-model orchestration architecture. This process includes:
- Calling a large language model to accurately identify and understand the user’s intent, deeply understand the knowledge content and logic itself, and output a knowledge-map prompt.
- Calling the Nano Banana Pro text-to-image model to generate integrated text-and-image knowledge map images, with each image serving as one Presentation page.
- Calling another AI model to process the content and turn images with text into editable online Presentations, allowing users to modify the content on the webpage.
- Providing a Chatbot on the Presentation editing page, calling the Gemini 3 Pro multimodal large model to optimize the copy and images according to the user’s needs.
3. Effect Demonstration
Homepage:
- Users directly enter the knowledge content copy they plan to share and select a visual color mode.
Final result:
- Users can directly edit the text content on the page.
- Users can also enter requirements in the Chatbox at the bottom right and ask AI to help edit the content inside the Presentation.