Honeywell's Forge Assist (AI Assistant)
This prototype demonstrates a dual-AI interaction model where both the user and the assistant leverage AI. Built using ProtoPie’s voice trigger method, it enables a warehouse maintenance technician to initiate tasks hands-free. The AI voice assistant provides guided repair instructions for a conveyor system, while the phone’s camera and computer vision detect mechanical issues in real time and offer context-sensitive assistance.
I created the following prototype using AI-generated voice synthesis for both the user and the assistant, with my own voice serving as the user input. Most of the experience was built in Figma and ProtoPie, combining voice interaction and motion design to simulate a natural maintenance dialogue. Today, I would likely extend this workflow using Lovable, Cursor, or Figma Make to further accelerate AI-driven prototyping.
AI Assistant (Maintenance Assist)
Maintenance Assist is Honeywell’s advanced Generative AI digital assistant, designed to simplify complex systems and empower personnel to maintain, troubleshoot, and repair critical assets easily and efficiently.
As part of Honeywell’s Enterprise Worker & Asset Safety (EWAS) initiative, my research and design prototypes directly informed the product’s conversational UX, context models, and information architecture. I collaborated with engineers to explore LLM integrations, data pipelines, and React-based UI components that would later define Maintenance Assist’s core experience.
Key Contributions
• AI Conversational UX – Prototyped the natural language assistant that guides maintenance technicians step-by-step through troubleshooting and repair workflows.
•. Information Architecture + Interaction Design – Defined how contextual insights (alarms, manuals, diagnostics) appear dynamically in conversation, reducing cognitive load.
• Design System Integration – Collaborated with engineers to implement the conversational assistant within Honeywell’s React + Figma design system, ensuring accessibility (WCAG 2.1).
• User Validation – Conducted field VOCs and usability sessions with maintenance personnel and partners (KD Analytics, Virgin Media, AllSafe) to evaluate comprehension, efficiency, and trust in AI outputs.
•. Cross-Product Alignment – Extended the AI assistant framework to other Honeywell digital products like Fleet Copilot and Operations Assist for predictive and preventive maintenance.
The below concept is using maintenance assist application on a tablet which is the same as the mobile app we viewed earlier.
Vertex Assist
This a concept for a high tech gas detector that is placed in a semiconductor FAB to prevent any issues during the making of silicon chips in facilities managed by Samsung, Intel and others. I created the touch screen application and presented these concepts that would use generative AI to provide recommendations and insights based on data from the gas analyzers including alarms and faults in the optics, pump, filter or chem cassette.
Key Features (Product Outcomes)
• Generative AI Technology – Leverages Honeywell’s proprietary data and documentation for step-by-step maintenance and repair guidance.
• Natural Language Processing – Understands user queries in plain language and provides context-aware responses.
• Conversational Interface – Simplifies complex maintenance tasks through an intuitive chat-style interface.
• Extensive Knowledge Base – Connects to Honeywell’s ecosystem of manuals, service tickets, and knowledge articles.
• Interactive, User-Friendly Design – Built for both desktop and mobile, requiring minimal user training.
Vertex Digital Twin (AI-generated guided steps for maintenance)
This is the diagnostics application for Vertex One which is Honeywell's next generation Vertex machine used in semiconductor FABs. The system would use AI to generate custom maintenance instructions for maintenance.
Intended Benefits
• Simplifies troubleshooting, speeding up repair times and decision-making.
• Cost Savings - Streamlines maintenance workflows, reducing operational overhead.
• Enhanced Productivity - Provides actionable guidance that boosts technician effectiveness.
• Training Efficiency - ccelerates onboarding and continuous learning for new technicians.
• Reduced Downtime - Enables predictive servicing and faster issue resolution.
Vertex Predictive Insights and Recommendations
Applies AI and machine learning to forecast potential equipment issues before they occur. The system generates preventive maintenance recommendations with a confidence score to indicate prediction reliability. By providing early warnings and guided corrective actions, the application helps prevent costly downtime in semiconductor FAB environments, improving overall equipment efficiency and reliability.
AI-generated Insights and Widgets
I designed the AI-Generated Insights and Widgets dashboard which includes a customized data visualizations and enables users to create AI-generated widgets for specific insights into issues affecting performance and safety. This tool empowers users The platform acts as a central hub for large industrial factories as well as first responders to actively manage safety and integrate data from connected safety sensors gas detection devices, and connected workers. The AI model would bubble up trends, predict risks, and recommend preventive actions before dangerous incidents occur.
My role
Enterprise Worker & Asset Safety (EWAS)
Role: Lead UX Designer | 2024–2025
Scope: AI-enabled gas-detection and compliance management platform
• Designed the next-generation worker-safety ecosystem integrating connected gas detectors, fleet management, and predictive analytics.
• Mapped workflows and information architecture for device setup, check-in/out, and compliance reporting.
• Co-created AI-driven dashboards for opintel and safety outcomes.
• Integrated a "Fleet Copilot", a conversational AI assistant that delivers insights (“Which workers had the most alarms today?”) and generates automated reports.
ª Directed UX research across global partners and competitor analysis
• Helped build design system updates in React + Figma, including accessibility and mobile responsiveness.
