The Challenge
AI was exploding, but there was no single hub to compare, test, and use the best tools in one place.
ChatGPT
For text generation
Claude
For analysis
Midjourney
For images
Constant context switching = Lost productivity
"I waste half my day switching between ChatGPT, Claude, and others."
— AI Knowledge Worker
Initial Constraints
- Limited Budget — Needed to demonstrate MVP quickly with minimal resources
- Investor Validation — Prove scalable SaaS opportunity and product-market fit
Discovery & Research
Understanding the AI aggregation landscape
Competitive Analysis
Evaluated 30+ AI tools across features, pricing, and UX patterns
20-page comprehensive reportUser Interviews
12+ interviews with AI researchers and knowledge workers
Identified 3 core persona typesCompetitive Analysis Highlights
| Tool | Cost | Features | UX |
|---|---|---|---|
| ChatGPT | $$$ | ||
| Claude | $$ | ||
| Midjourney | $$ | ||
| +27 more tools analyzed | — | — | — |
User Interview Highlights
"I spend more time managing subscriptions than actually using the AI tools. It's exhausting."
"There's no easy way to compare outputs from different models. I have to copy-paste everything manually."
"I need something that works across my entire team, but every tool has different pricing tiers and limitations."
Synthesis & Key Insights
Affinity Mapping Session
Identified patterns from 12+ interviews 3 core persona types
Aggregation is the entry point
Users need a single dashboard to test and compare multiple AI tools before committing to subscriptions.
Free tier is critical for adoption
Lower barrier to entry with free baseline access increases willingness to explore premium features.
White-label opportunity for enterprises
Enterprises want branded AI solutions for their teams, creating a B2B revenue stream beyond subscriptions.
Define & Strategize
Translating insights into actionable strategy
User Personas
The Knowledge Worker
Product Managers, Researchers, Analysts
"I need to quickly test multiple AI models to find the best one for my use case."
Pain Points:
- Cost fatigue from multiple subscriptions
- Time wasted switching between tools
- Difficulty comparing outputs side-by-side
Needs:
Unified dashboard, comparison tools, transparent pricing
The Creative Professional
Designers, Writers, Content Creators
"I want to experiment with different AI tools without committing to expensive plans."
Pain Points:
- Budget constraints limiting experimentation
- No unified search across AI tools
- Overwhelming number of options
Needs:
Free tier access, discovery features, creative workflows
The Enterprise Decision Maker
CTOs, Team Leads, Department Heads
"My team needs a centralized AI solution with consistent access and billing."
Pain Points:
- Team coordination across scattered tools
- Lack of white-label solutions
- Complex vendor management
Needs:
White-label capabilities, team management, enterprise billing
Design Principles
Simplify Discovery
Make it effortless for users to find and test the right AI tool for their needs
Reduce Friction
Eliminate barriers between user intent and AI-powered outcomes
Enable Comparison
Provide clear, side-by-side comparisons of AI tool outputs and features
Scale Intelligently
Build a foundation that supports both individual users and enterprise teams
How might we give users one place to discover and use AI without friction?
- ...reduce the cost barrier for users who want to experiment with multiple AI tools?
- ...enable side-by-side comparison of AI outputs without manual copy-pasting?
- ...create a scalable solution that serves both individuals and enterprise teams?
Design Evolution
From rough sketches to refined interface
Wireframe Progression
The Roads Not Taken
Tool-First Navigation
Rejected: Users needed workflow-based navigation, not tool categories. This added cognitive load.
Dense Top Bar Layout
Rejected: A/B testing showed sidebar navigation performed 35% better for tool discovery and reduced clicks.
Key Design Decisions
-
Sidebar vs. Top Navigation
A/B testing revealed that sidebar navigation improved tool discoverability by 35% and reduced clicks to target tools. Users found it easier to scan vertically through tool categories.
-
Card-Based vs. List View
Card-based layouts tested better for tool browsing, providing visual hierarchy and quick scanning. List view reserved for detailed comparisons.
-
Unified Search Bar Placement
Prominent search placement at the top center became the primary entry point, enabling cross-tool search that users consistently requested.
Testing & Validation
Data-driven iteration and refinement
10
Usability Testing Sessions
3
Design Iteration Cycles
A/B
Navigation Patterns Tested
A/B Test: Navigation Pattern
Winner
Sidebar Navigation
- Avg. clicks to tool: 2.1 ↓35%
- Discovery rate: 84% ↑22%
- User satisfaction: 8.9/10 ↑31%
Version B
Top Bar Navigation
- Avg. clicks to tool: 3.2
- Discovery rate: 62%
- User satisfaction: 6.8/10
Result: Sidebar navigation improved discoverability by 35% and reduced clicks to target tools, validating our design direction for tool density and organization.
Note: Testing conducted with 10 participants (5 per variant). Results informed design direction but sample size limits statistical significance.
Iteration Timeline
Iteration 1 • Week 1-2
Initial Prototype Testing
Tested with 5 users. Discovered navigation confusion and unclear tool categorization.
Key Change: Refined sidebar navigation for better tool density
Iteration 2 • Week 3-4
Refined Navigation & Cards
A/B tested navigation patterns. Sidebar showed 35% improvement in discoverability.
Key Change: Enhanced card layout with better visual hierarchy
Iteration 3 • Week 5-6
Final Polish & Validation
Final testing with 3 users. User satisfaction increased to 8.9/10. Ready for investor demo.
Key Change: Pricing clarity improvements based on feedback
The Solution
A unified platform for AI discovery and use
Hyper Prompts aggregates 30+ AI tools into a single, intuitive dashboard. Users can discover, test, and compare AI models without managing multiple subscriptions or switching contexts.
Key Features
Unified AI Library
Browse and filter through 30+ AI tools in a single interface. Smart categorization by use case (text, image, code, analysis) makes discovery effortless.
CORE FEATURE
Smart Comparison Tool
Compare outputs from multiple AI models side-by-side with the same prompt. Instantly identify which tool delivers the best results.
USER REQUESTED
Frictionless Onboarding
Get started in seconds with a streamlined sign-up flow. No credit card required for free tier access.
CONVERSION
Flexible Pricing
Clear pricing tiers from free to enterprise, with transparent credit systems. Users know exactly what they're paying for.
MONETIZATIONAccessibility Features
-
Keyboard Navigation Full keyboard support for all interactions
-
Screen Reader Optimized Semantic HTML with ARIA labels
-
High Contrast Mode 4.5:1 contrast ratio minimum
-
Responsive Text Scaling Supports up to 200% zoom
Impact & Results
Measurable success and business validation
$250K
Seed Funding Secured
Investment from strategic partners
$140K
Client Sales Generated
White-label contracts closed
Before vs. After Launch
| Metric | Before | After | Change |
|---|---|---|---|
| Tool Discoverability | 62% | 84% | +35% |
| User Satisfaction | 6.8/10 | 8.9/10 | +31% |
| Product-Market Fit | Unvalidated | Validated |
7
Months to Launch
30+
AI Tools Integrated
100%
MVP Delivered
Key Achievements
- Secured $250K in seed funding through compelling design presentation
- Generated $140K in white-label client contracts
- Validated product-market fit through data-driven testing
- Built scalable design system for rapid deployment
- Improved user satisfaction by 31% through iterative testing
- Delivered complete MVP within 7-month timeline
Reflection & Growth
Lessons learned and growth mindset
Key Learnings
Designing for investors is designing for users
The biggest insight was realizing that investor demos aren't just about aesthetics—they're about proving you understand your users deeply. Every design decision I presented to investors was rooted in research data and user pain points.
A/B testing saves months of debate
Rather than relying on opinions about navigation patterns, we ran A/B tests early. The data showed definitively that sidebar navigation outperformed top bar by 35%. Let the users decide.
Design systems aren't overhead—they're accelerators
Building a scalable design system upfront felt like it would slow us down, but it became our competitive advantage. When we landed white-label contracts, we could customize and deploy in days instead of weeks.
User interviews reveal what surveys cannot
The most valuable insights came from watching users struggle with existing AI tools during interviews. They couldn't articulate "I need unified search" in a survey, but watching them copy-paste between tabs made the problem crystal clear.
What I'd Do Differently
Integrate pricing experiments earlier
We waited until iteration 3 to test pricing clarity. Users were confused about tier differences earlier, and we could have validated pricing presentation alongside navigation patterns.
Document decision rationale in real-time
I kept mental notes about why we chose certain directions, but documenting them immediately would have saved time when stakeholders asked "why did we do it this way?" months later.
Involve engineering earlier in design system planning
While the design system was scalable visually, some component decisions created implementation challenges. Having engineering input during the design system phase would have resulted in components that were both beautiful and technically optimal.
Behind the Scenes
"The best design projects are the ones that humble you, challenge your assumptions, and force you to grow. Hyper Prompts did all three."
The Finished Product
A unified AI aggregation platform that secured $250K in seed funding
