No more memorizing prompts, cutting platforms, or manual editing – CrePal stuffs more than a dozen models such as VEO, Keling, Midjourney, and Suno into the same agent. As long as you say “Give me a 20-second McDonald’s hot pot ad”, it will automatically write the script, produce the picture, match the music, do the editing, and output it into a piece in one stop. Creators can finally go from being a technical coolie to a pure creative.
In the past six months, a number of creator accounts mainly based on AI videos have emerged rapidly on platforms such as Xiaohongshu and Douyin. Behind the popular content is a realistic threshold that creators generally face: the threshold for creation seems to be low, but the actual operation is extremely cumbersome.
Typical creators come from traditional content positions, such as choreographers, self-media managers, brand marketers, or freelance writers. They have clear planning ideas, know how to position audiences, mobilize emotions, and have clear style orientation and communication goals. But when these expressions were implemented as AI videos, they found that the tools were not as “foolish” as they imagined, but more like an industrial process that required fine coordination.
Mainstream platforms such as VEO, Keling, and Conch continue to update, and the model capabilities continue to improve in terms of clarity, lens length, motion stability, lens language, and aesthetic performance. However, while these parameters have leapfrog, most users are still stuck in the state of “understandable but not used well”.
Behind this phenomenon is the superposition of multiple structural barriers:
1) The threshold for model use is high. Compared to AI painting or writing, video generated prompts are more complex and require users to have basic storyboard design and narrative arrangement skills.
2) Steep learning curve. It usually takes 1 to 2 weeks for newbies to familiarize themselves with the platform, from prompt writing and style adaptation to video generation, each step is scattered in different processes.
3) The cost of trial and error is high. In order to understand the functions of platforms such as VEO, Keling, and Conch, many users need to pay to open multiple members, with an initial investment of as little as 1,000 yuan or as much as 2,000 or 3,000.
A deeper obstacle is the lack of integrated tools with automated scheduling capabilities. At present, the video production process is still highly fragmented, and the problems of broken links, frequent switching, and version confusion are widespread, resulting in the difficulty of improving production efficiency.
The first video creation agent
CrePal was born to solve this pain point. It is not a single model tool, nor is it not just an integrator of multiple platforms, but an intelligent agent built for AI video content scenarios. Its positioning is clear: it allows content creators who do not understand technology to smoothly complete a complete video work. The target audience is not AI enthusiasts or developers, but creators with content planning capabilities and aesthetic judgment, but have been blocked by technical thresholds for a long time.
To achieve these three challenges, product managers will only continue to appreciate
Good product managers are very scarce, and product managers who understand users, business, and data are still in demand when they go out of the Internet. On the contrary, if you only do simple communication, inefficient execution, and shallow thinking, I am afraid that you will not be able to go through the torrent of the next 3-5 years.
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CrePal’s core capability lies in intelligently scheduling mainstream video generation models. When the user enters the creation goal and preference, the system will automatically calculate the optimal combination scheme based on multiple dimensions such as call cost, generation time, picture quality, motion stability, and training data distribution, and complete the model selection and parameter configuration. Users don’t need to understand technical details, face complex model lists, or memorize prompt syntax differences across platforms.
Just express your needs through natural language, such as “help me generate a McDonald’s hot pot advertisement in China, integrate the McDonald’s sign into the hot pot, about 20s”, the system can parse the semantics and disassemble it into sub-tasks such as script generation, picture construction, audio track matching, and lens scheduling, and the system will automatically complete the model scheduling and execution.
In the past, many creators gave up using models due to high thresholds, complex parameter debugging, and frequent platform switching. Nowadays, with CrePal, even technical novices can achieve high-quality creation without understanding the underlying logic or writing prompts, and complete practical operational goals such as account cold start and daily update.
The real threshold for AI video creation is no longer “whether there are tools” but “whether integration can be achieved”. This integration is not only interface integration, but also a cognitive disassembly capability based on task goals, which is the key capability that most platforms lack today.
Call all models in one sentence
CrePal has built an intelligent content orchestration and unified scheduling system that integrates graphics, audio, and video, covering the entire process from generation to presentation. In terms of video generation, it is connected to mainstream models including VEO, Conch, PixVerse, and Keling, supporting complex tasks such as mixed scene generation, multi-character motion control, and music style switching, and further improves the understanding and execution of complex instruction chains through follow commands.
In terms of image generation, CrePal integrates leading models such as Midjourney, GPT Image, Google Imagen 4, and Flux to meet the needs of stable style image generation. Audio generation integrates services such as Suno, 11labs, and Volcano Engine, and supports the generation of stylized soundtracks in Suno, while introducing a massive copyrighted song material library to comprehensively enhance the freedom and expressiveness of content creation. At the specific interaction level, CrePal’s UI details and operation experience are also continuously optimized, allowing content creators to get a more natural and smooth feedback experience in full-link creation.
CrePal does not emphasize the technical label of “self-developed model”, but focuses on building scheduling logic, becoming a central system that coordinates multi-model resources and runs through the entire process of video generation. Compared to tools that focus on a specific model’s capabilities, CrePal is more like a production coordinator with the ability to understand, schedule, and collaborate, rather than a single tool arm that executes orders.
Back to the example of “Help me generate a McDonald’s hot pot advertisement in China, integrating the McDonald’s sign into the hot pot for about 20s”, after entering the prompt, CrePal will first recognize the user’s intention – “advertisement”, and then start to formulate the entire advertisement story summary, art style, character setting, storyboard and other frameworks.
The script output by CrePal contains four coherent shots: shot 1 is the golden M logo slowly rising, bringing out the amber hot pot soup base, the camera slowly zooms in, and the close-up of the soup noodles rolling and the ingredients floating, first mobilizing the audience’s appetite; Then switch to shot 2, French fries, burgers, chicken nuggets, McDonald’s classic products become ingredients in hot pot, with steam special effects, showing the process of ingredients entering the pot; Shot 3 Turning to the customer’s perspective, four young people sit around the hot pot table, picking up the ingredients for dipping sauce and drawing silk, their expressions are natural interaction and happiness, highlighting the atmosphere of the dinner; Finally, shot 4 zooms out, and McDonald’s brand elements and hot pot scene are integrated.
Once the content framework is set, this is where CrePal starts calling the Midjourney Turbo model to generate a 3D character model:
CrePal then selects the most suitable CrePal 2.1 to complete the generation of Shot 1 based on the visual design and script content. The system automatically plans details such as camera positions, camera movements, and steam lighting effects for scene transitions to coordinate and enhance the overall 3D animation texture:
In the image material generation stage of Shot 2, CrePal calls GPT-Image to output high-consistency visual elements including McDonald’s elements, hot pot scenes, and character movements for subsequent shot stitching and animation binding:
Coming to Shots 3 and 4, CrePal further refined the storyboard execution with Byte’s Seedance and dynamically adjusted the pacing and atmosphere:
After the four shots are completed, the system calls Suno to generate stylized background music, automatically recognize the ad tone, and output warm tones and bright rhythmic BGM:
During the editing stage, CrePal automatically completes the rhythm control and camera connection according to the script and storyboard logic. The system intelligently recognizes the emotional ups and downs of each shot, and automatically adds smooth transitions, dynamic zoom, and steam lighting effects, making the picture feel natural and breathing.
CrePal will precisely align the rhythm of the screen according to the rhythm of the background music, ensuring that the music, narration, and the screen rise and fall in unison. Subtitles and brand logos are also automatically generated and superimposed in the right place, and the overall clip outputs a direct delivery of the finished film, eliminating the need for users to make secondary adjustments. The entire editing process is completely automated by CrePal’s scheduling system, and users can also optimize clips with instructions.
Finally, after accepting a one-sentence prompt, CrePal automatically and accurately completed a video splicing, subtitle addition, audio and video synchronization that met the needs, and directly output the finished advertisement that could be delivered:
CrePal’s collaborative model essentially transforms the traditional tool-creator relationship. The user is no longer the executor of the model call, but the creative subject in collaboration with the agent, and the entire video generation process is no longer a black box operation.
From a more objective perspective, if an AI tool still requires users to master complex call methods and parameter configurations, it is still just a tool; Only when the user focuses on expressing their creativity and the system takes over the rest of the process does the AI truly become an agent, which is the essential difference between CrePal and traditional AI video tools on the market.
Good content comes back to creativity itself
From the perspective of industry trends, CrePal represents not a technical leap in a certain model capability, but a deep evolution of the organization of content production. Agents are gradually replacing point tools as a new execution structure that connects ideas to finished products.
In the field of image generation, Leonardo.ai first proposed the creation mechanism of preset + prompt, which allows users to stably output images based on preset styles. In a programming scenario, Devin demonstrates how to break down a target task into a chain of processes and invoke multiple toolchains to collaborate on the task. In video content generation scenarios with longer links and more complex processes, CrePal is the first product to attempt to package the entire process into the agent system, which not only undertakes the calling task, but also has end-to-end understanding and scheduling capabilities.
This is not only an upgrade of the form of tools, but also a shift in creative logic. Creators no longer rely on a single model’s expertise to repeatedly migrate materials and formats between platforms, but only need to express content goals, and the system automatically completes path design, model calling, and finished video output.
This trend is also penetrating into the upstream and downstream links of video. Many companies have begun to explore providing script planning and editing solutions through the agent system. There is also a practice on YouTube where bloggers use GPT-4o to drive “automatic editing of sub-channels” to test the efficiency of content reuse and channel diffusion. MCN teams in China have tried to use AI Agent to generate brand “daily change materials” to serve multi-account matrix operations.
These signs suggest that the next-generation infrastructure for AI video will not be a strong model or vertical tool, but a complete set of intelligent execution systems. The creator delivers the goal, and the agent executes the path, completing the complete link from content planning to video completion.
CrePal is a pioneer in this system path. By building scheduling logic, task chain structure and node linkage mechanism, it integrates scattered execution modules such as scripts, pictures, dubbing, and editing into integrated intelligent tasks that can be talked to and modified, becoming an entry platform with low threshold and high control for content creators.
AI should not replace the expression intent itself, but it can reconstruct the implementation path of expression. Agents will not become new creators, but they are becoming the “second brain” of creators – turning ideas into execution paths, taking over complex processes, and freeing up creators’ time and attention.
CrePal is iterating in this direction: not what is generated, but understanding what users “want to generate”. That’s the real value of Agent products.
