Case Study
Design as the Integrator of IoT Engineering
Orchestrating design, engineering, and product development at Very Technology
01. Overview
Enterprise AI and hardware demand design clarity
Very is an award-winning AI and connected hardware company that builds custom enterprise platforms and applications. With over 14 years of experience, they deliver solutions spanning
AI agents, generative AI, data science, hardware design, and software development
for clients like HP, L’Oréal, Vizio, and P&G.
The challenge: translating deeply technical AI and IoT capabilities into intuitive product experiences that enterprise teams can adopt confidently—bridging the gap between cutting-edge technology and real-world usability.
Design AI-driven interfaces that surface actionable insights
Unify hardware and software product experiences
Scale design systems across diverse enterprise clients
Improve adoption through user-centered design
02. My Role
Design across the full product stack
Working with Very’s cross-functional teams to deliver design that spans
AI applications, connected hardware, and digital infrastructure
.
AI & Data Products
Designed interfaces for AI agent workflows and LLM-powered applications
Created data visualization patterns for machine learning insights
Built design patterns for conversational AI and generative experiences
- Established UX frameworks for complex data pipelines
Connected Hardware
Designed companion apps and dashboards for IoT device management
Created unified experiences bridging physical and digital products
- Prototyped embedded interfaces for hardware products
Collaborated on industrial design and UX for connected devices
Design Systems
Built scalable component libraries used across multiple client projects
Established design tokens and patterns for enterprise platforms
Created documentation and guidelines for design-engineering handoff
Maintained consistency across web, mobile, and embedded interfaces
03. Key Challenges
Making complex technology feel approachable
Challenge 1
AI interfaces that build trust, not confusion
AI-powered products often feel like black boxes. Enterprise users need to understand what the system is doing, why it made a recommendation, and how confident it is in the result.
The design needed to make AI transparent, predictable, and controllable—not just powerful.
Approach
Explainability layers that show how AI reached its conclusions
User controls for adjusting AI confidence thresholds
Audit trails and decision logs for compliance and review
Human-in-the-loop patterns for critical decisions
Challenge 2
Bridging hardware and software into one experience
Connected hardware products live across physical devices, mobile apps, web dashboards, and cloud infrastructure. Users shouldn’t have to think about which layer they’re interacting with.
Approach
Designed unified interaction models across device, mobile, and web
- Created real-time status and feedback patterns for hardware state
Built progressive disclosure for technical details—simple by default, deep when needed
Tested end-to-end flows from physical interaction to cloud response
04. Process
Integrated design across disciplines
Cross-Discipline Collaboration
Working alongside AI engineers, data scientists, hardware engineers, and product managers to ensure design decisions account for technical constraints and opportunities.
Design informed by engineering reality.
Rapid Prototyping
Fast iteration cycles with functional prototypes that test both interaction patterns and technical feasibility:
- Explore with low-fidelity concepts
- Validate with stakeholders and users
- Build interactive prototypes
- Test with real data and edge cases
Tooling
05. UX Strategy
Designing AI products people actually trust
Transparency over magic
Show users what AI is doing and why.
Control builds confidence
Let users adjust, override, and validate AI outputs.
Progressive complexity
Simple defaults with depth available on demand.
Cross-platform coherence
Consistent experience from device to cloud.
06. Business Impact
Design as a multiplier across the business
AI Products
Accelerated enterprise adoption through intuitive AI interfaces
- Reduced onboarding time for complex ML-powered tools
- Increased user confidence in AI-generated recommendations
Connected Hardware
- Unified device management experiences across product lines
- Streamlined hardware-software integration workflows
- Improved device setup and onboarding completion rates
Design Systems
Reusable component library serving multiple enterprise clients
- Faster project delivery through established patterns
- Consistent quality across diverse product verticals
07. Outcomes & Learnings
What Worked
Embedding design within engineering teams—not as a separate function
Treating AI explainability as a core design requirement, not an afterthought
Building design systems that flex across client projects and product types
Prototyping with real data to validate AI-driven interactions early
Lessons Learned
AI UX is fundamentally about managing user expectations and trust
Hardware constraints shape software design in ways that require deep collaboration
- Enterprise users value predictability over novelty
The best AI interfaces disappear—they make complexity feel natural
“The best AI products don’t just automate—they augment human judgment, making complex decisions feel manageable and trustworthy.”
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