Vertical Studio
"The best code is, is no code at all"
The No-Code Studio is what Vertical is all about, built to make AI model customization accessible to everyone, regardless of technical expertise. It serves as the central interface where users can customize AI models through a user-friendly, no-code environment. Whether you're adjusting AI model behavior, adapting to a specific dataset or refining performance Vertical Studio lets you focus on outcomes, not infrastructure or code.
Customizing AI Models
Vertical Studio initially focuses on three widely used methods of AI model customization. Each method offers a different balance of speed, effort and control, so you can pick the approach that best fits your goal.
System Prompting
What it is: A high-level instruction that steers the model’s behavior at run time. No extra data or training needed.
When to use it: Quick tweaks such as tone adjustments, brand voice or basic chatbot rules.
Retrieval-Augmented Generation (RAG)
What it is: The model links to your own documents through a vector database, retrieves relevant passages on demand and answers based on them.
When to use it: Knowledge bases, product FAQs or any scenario that needs up-to-date or proprietary information.
Fine-tuning
What it is: Further training on a curated dataset so the model learns new terminology, patterns or styles. Requires more compute time but offers the most control.
When to use it: Domain-specific tasks, large accuracy gains or creating a fully branded voice.
All three methods run in the same no-code workspace. Choose your approach, follow the guided steps and deploy or monetize the result without writing a single line of code.
How it works
Our No-Code Studio is all about simplicity, efficiency and quality. The interface will offer a clean, visual workspace where users can manage their AI customization workflows easily. Our focus is to make it really accessible for everyone.
The steps will be as followed and as depicted in the video above
Choose your customization method: Start by picking the approach that fits your goal: System Prompting for instant behavior tweaks, RAG for document-aware and/or up-to-date answers or Fine-tuning for deep domain accuracy.
Select a foundation model: Choose any supported large language model, or upload your own as the starting point for your vertical model.
Add or create data: Upload structured files, import existing datasets or let our Dataset Creation Agent turn raw content into clean training material. This step is essential for Fine-tuning, optional for RAG and System Prompting. The quality of a "vertical" AI model depends on its data.
Set key parameters: Adjust settings such as temperature, context length or training epochs to steer quality and performance. The Studio offers sensible defaults, so beginners can skip fine-grained tuning if they prefer.
Select your computational power: Choose the GPU power your model. You can select traditional centralized clouds for training, inference, or hosting your AI models, or leverage our ground-breakingly cost-effective Vertical Cloud.
Test, iterate and launch: Test the model in real time, refine as needed, then deploy through an API endpoint or list it in the Vertical Stream to start monetizing your work.
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