Wobot.ai: Designing AI-Powered Video Intelligence from the Ground Up
Company Details
Company:
Wobot.ai
Industry:
AI-Powered Video Intelligence, SaaS
My Role:
End-to-End Product Design (UI/UX)
Scope:
UX Research, User Interviews, Personas, Wireframes, UI Design, Design System
Target Users:
Operations Managers, Franchise Owners, Compliance Teams
Overview
Wobot.ai is a cutting-edge AI-powered video intelligence platform that transforms traditional CCTV infrastructure into a source of real-time, actionable business insights. Designed for industries like QSR, Retail, Drive-Thru, and Car Wash, it helps operations teams detect issues, ensure compliance, and optimize workflows across multiple locations.
As the UI/UX Designer, I was responsible for designing the platform from scratch — from user flows and research to final UI, analytics, and scalable design systems. The mission was clear: simplify complex AI and video analytics into a seamless, intuitive experience that drives business value.
Results & Impact
- 📈 3x increase in user engagement on dashboard tools
- 🔁 40% reduction in onboarding time for camera setup
- ✅ More intuitive analytics → better adoption of AI features
- 🧠 Reduced mental load → improved task management
- 🏆 Adopted by 100+ multi-location businesses within months
Problem Statement
Although Wobot.ai had powerful backend AI models, its platform lacked usability and a clear interface. End-users found the experience fragmented and overwhelming, leading to poor adoption of critical features like task detection and analytics.
The key challenge: Translate complex AI-powered functionalities into a user-friendly product without losing technical depth or customizability.
User Pain Points
- Onboarding was complicated, especially for non-technical users setting up CCTV cameras and NVRs.
- Low trust in AI outputs due to lack of transparency and feedback loops.
- Inefficient navigation made users switch constantly between modules (Cameras, Tasks, Reports).
- Analytics lacked clarity — insights were hard to interpret or act upon.
- Inconsistent visual design across modules affected user confidence.
Discovery & Research
I conducted in-depth discovery sessions with internal stakeholders and over 10 users from target industries to map workflows, expectations, and frustrations.
Key Research Activities:
- Competitor audit (Verkada, Rhombus, Eagle Eye Networks)
- Cross-industry user interviews
- Journey mapping for QSR and Retail
- Internal feedback loops with Product and Engineering
User Personas
Information Architecture & UX Strategy
I restructured the platform architecture to be more intuitive, modular, and user-aligned—streamlining access and reducing cognitive load.
Wireframes & UI Design
We started with low-fidelity wireframes and iterated through stakeholder feedback before moving to high-fidelity UI in Figma.
User Onboarding Flow
Major Modules Designed
1. WoCam – Video Management System
- Live Stream & Playback
- Camera Offline Alerts
- Timeline Scrolling for Recorded Events
- Multi-location Support
- Multiple Recording Modes (motion, default, timelapse)
2. AI-Powered Tasks & Checklists
- 50+ AI-powered pre-trained tasks
- Draw detection zones (e.g., Billing Counter, Kitchen)
- Assign tasks to team members
- Automate scheduling of recurring tasks
- Sync with camera-based compliance
3. Analytics & Insights
- Real-time dashboards with customizable KPIs
- Heatmaps and bottleneck identification
- Location-wise reports
- Export incidents, task logs, and camera events
Design System
To maintain visual consistency across such a modular platform, I created a comprehensive design system in Figma with:
- Atomic components
- Color and type scales
- Shadows, spacing, responsive behavior
- Micro-interactions and motion design specs
Implementation & Handoff
- Delivered pixel-perfect UI specs via Figma + Zeplin
- Worked with developers on staging testing
- Provided interaction prototypes for edge cases
- Participated in agile sprints, daily reviews, and QA testing
Testimonials
“Highly recommend Devesh for his outstanding visual design skills and adaptability. He delivers not only beautiful but deeply functional results.”
Zalak UpadhyayUX Consultant at Wobot & Accenture AI
“As a mentor, Devesh helped me grow into a thoughtful designer. His feedback and encouragement were key to my transition from engineering to UX.”
Nikita SanerUI/UX Design Intern
Key Learnings
- Simplicity is not about dumbing down — it’s about clarity and structure.
- In AI systems, trust must be designed, not assumed.
- Design systems save time, especially when building enterprise-scale platforms.
- Real-world context and real-time data must drive design decisions.
Final Thoughts
This project was a milestone in my product design career — not only for its complexity but also for its impact. From onboarding to insights, I transformed a data-heavy AI engine into a usable, intuitive experience that empowered real-world decisions.
Wobot.ai now offers a clean, modern interface that makes AI video intelligence actionable and approachable — helping businesses stay compliant, efficient, and customer-focused.