AI Assistant: Fabrie Write

In 2023, after a year of launching the Fabrie whiteboard and completing a major technical refactor, the team started experimenting with AI features. Early tests (v1.0) included basic image-related capabilities such as text-to-image, image-to-image, and super-resolution. These experiments validated user interest, but the features remained lightweight and loosely connected to the core whiteboard experience.

As Midjourney and ChatGPT gained massive traction, many tools expanded toward “AI + whiteboard” scenarios. For Fabrie, the whiteboard was already a natural canvas for AI-generated content, presenting an opportunity for feature expansion and product differentiation.

However, most AI features on the market were limited to chat-style, timeline-based interactions, focusing mainly on text or image generation. Fabrie’s real strength lies in spatial collaboration. Simply embedding a chat window into the whiteboard would neither create differentiation nor bring meaningful value to our users.

I analyzed several AI tools (ChatGPT, Notion AI, Gamma, Tome, Miro, etc.) to understand differences in feature types, interaction patterns, and integration with core products.

• Mainstream interactions are concentrated in chat windows or command-based sidebars.
• Most features focus on content generation, with limited integration into spatial objects.

Based on this research, AI capabilities can be broken down into two steps:

Within Step 2: Content Generation, we identified two distinct modes:

• Create from scratch: generating entirely new objects, such as sticky notes and mind maps.
• Enhance existing: modifying current objects, such as changing color or adjusting style.

Traditional commands rely on fixed buttons, such as pressing “Back” to return to the previous page.

AI commands rely on understanding natural language, breaking the one-to-one mapping between command and task. This offers greater flexibility, but also higher uncertainty.

We approached this challenge from a function-first perspective.

Feature scope by category:

• Image Generation: generate images based on prompts.
• Text Processing: summarize, continue writing, extract tasks, expand details, split paragraphs, translate, refine text.
• Image Editing: background removal, OCR text recognition, upscaling, AI-assisted edits.
• Template Generation (Canvas GPT): mind maps, brainstorming, pros & cons, business model canvas, stakeholder maps, timelines, data tables.

From these functions, I categorized interaction needs into two types:

1. Low-customization needs, such as translation and background removal, should retain button-triggered actions.
2. High-customization needs, such as generating complex illustrations and custom templates, should introduce natural language input.