Prototyping
Concept-to-Page
Go from product idea to working landing page in under 3 minutes.
The Business Case
Founders often spend weeks on design and copy before validating an idea. We needed a way to go from ‘product concept’ to ‘visual prototype’ in minutes.
The goal: Reduce the opportunity cost of testing new ideas.
The Approach
A multi-agent pipeline where specialized AI agents handle research, design, and code generation in sequence.
- Research Agent gathers competitive intelligence.
- Design Agent creates layout structure.
- Developer Agent generates the code.
By specializing the agents, we get a result that isn’t generic—it’s grounded in market context.
The Solution
A single-input interface. Paste a product URL, competitor link, or just describe what you’re building. The pipeline returns a complete, deployable product page with:
- Headline and subhead copy
- Feature sections with benefits-focused language
- Social proof placement
- Call-to-action structure
- Responsive HTML/CSS ready to customize
Think of it as a first draft generator. The output isn’t final, just a starting point that would have taken hours to create manually.
Key Decisions
1. Speed over polish
I optimized for the “sketch” use case, not production-ready sites. The goal is validation: does this structure make sense? Does the copy direction feel right? Founders can iterate from a draft faster than from a blank page.
2. Research-first architecture
The Research Agent runs before anything else. It gathers competitor positioning, common feature language, and market context. That research informs the Design and Developer agents, so the output isn’t generic. It’s contextually relevant.
3. Haiku for speed
The pipeline uses Claude Haiku 3.5 for all agents. Faster models mean the whole process completes in under 3 minutes, making it practical for iterative use.
Results
- Input to output: Under 3 minutes for a complete page draft
- Use cases tested: E-commerce products, local restaurants, service businesses, SaaS landing pages
- Research parity: Research Agent findings matched ~90% of what manual competitive analysis would surface
Interested in something similar?
Let's explore how systems like this could work for your team.