SaaS AI.
Timeline
2023 - 2024
Team
CTO, VP of Development, Technology
Role
Vision, Research, Product Design, Design Systems
NDA Disclaimer 🚧
As part of my work with Simpliciti AI, I am bound by a Non-Disclosure Agreement (NDA), ensuring the confidentiality of all proprietary information related to our AI-driven SaaS platform, including its design, technical architecture, and business strategies during its alpha stage of development.
From AI Complexity to Clarity: The Simpliciti Story
Enterprise AI was stuck in a rut - powerful but painful to use. As Head of Design, I saw an opportunity to change that. We weren't just building another AI platform; we were reimagining how organizations could harness AI without needing a PhD to use it.
I led our team with a clear mission: make enterprise AI as intuitive as your favorite app, but powerful enough to transform how businesses work with data. During our critical alpha phase, we laid the groundwork for something special:
✦ Created frameworks that turned complex AI operations into simple workflows
✦ Built validation methods that actually showed us how users interact with AI
✦ Designed a path to market that balanced innovation with user needs
Understanding the Market
Spotted a critical gap in enterprise AI - powerful tools that were painful to use. Deep market analysis revealed where existing solutions fell short.
Standing Out from the Crowd
Built a platform bridging AI complexity and human usability - something competitors hadn't cracked.
User Focus
Mapped personas from data scientists to analysts, creating features that worked for everyone while keeping the experience unified.
Growth Plan
Designed clear phases from alpha to launch, each building on learnings from the last.
Seizing the AI Opportunity
Enterprise AI had a problem: powerful capabilities trapped behind complex interfaces. While companies had the tools, most employees couldn't use them effectively.
Our vision was clear - create an AI platform that's both powerful and accessible. Through deep competitive analysis and stakeholder interviews, we built a strategic roadmap that aligned market needs with technical feasibility. Our foundation focused on making advanced capabilities available to everyone, not just technical experts.
✦ Deep market analysis revealed competitive gaps
✦ Stakeholder insights shaped key differentiators
✦ User personas guided feature priorities
✦ Technical roadmap ensured feasible execution
Vision & Leadership
We tackled enterprise AI complexity in three key ways:
Enterprise-Grade Simplicity
Built a modular design system that scaled with user expertise. Created a scoring system to measure interface clarity, ensuring both beginners and experts could work efficiently.
AI Transparency Framework
Developed a confidence scoring system showing how certain AI was about decisions. Like a trust meter for AI, this helped users understand and trust automated recommendations.
Scalable Design Operations
Created clear career paths and rapid feedback loops. This let us iterate quickly while maintaining quality, keeping our team aligned as we scaled.
Exploration: Building Our Foundation
At the heart of our alpha phase were two key focuses: innovation and validation. We built an architecture that could grow with us, featuring a progressive UI that adjusts to user expertise and makes complex AI operations feel natural. Our component system was built for rapid scaling while maintaining quality.
We measured success through clear metrics: time-to-value for new users, workflow completion rates, and AI interaction confidence. Cross-functional teams aligned around clear goals, while user feedback shaped our roadmap. Rigorous testing built confidence for enterprise-scale deployment.
