Sourcing the right electronic component is often a complex, time-consuming task that requires comparing datasheets, navigating product catalogs, and manually verifying specifications. At Altium (acquired by Renasas), I led the design of an AI-powered component discovery assistant that streamlines the engineer’s workflow by delivering relevant parts with conversational ease and structured comparisons.
Use Cases
Engineers spend hours discovering and validating components, navigating multiple supplier websites and datasheets with outdated or inconsistent information. This results in:
Lost time in decision-making cycles.
Missed opportunities due to poor discoverability.
High cognitive load and repetitive manual checks.
Technical Stack
AI Models: OpenAI (for chat + search parsing)
Front-End: React, TailwindCSS
UX Design: Figma, Design Tokens, Rapid Prototyping
Data Sources: Supplier Catalogs, Internal Parts Database
Metrics & Testing: Heap, UsabilityHub, Stakeholder Reviews
Process
1. Conversational Search Experience (AI Copilot UX)
Users describe what they’re looking for using natural language, e.g., “humidity sensor, 3.3v, 5MHz”.
The AI assistant parses the request and instantly surfaces top-matching components.
Prompts like “show me options under $7” refine results contextually.
2. Structured Comparison Interface
Engineers see a side-by-side comparison of shortlisted components.
Highlights power usage, price/unit, accuracy, and other critical specs.
Uses visual affordances (icons, colors, groupings) to communicate recommendations clearly.
3. Embedded Intelligence for Relevance & Ranking
The AI assistant uses historical usage patterns and requirements to prioritize optimal parts.
Clearly labeled tiers like “Best Fit”, “Suitable Alternative”, and “Not Recommended” support confident decision-making.
4. User Experience Design Focus
Emphasis on reducing engineer effort: clear layouts, progressive disclosure, and conversational feedback loops.
Designed with real-world constraints in mind: datasheet clutter, fuzzy requests, evolving requirements.
Integrated warnings to avoid oversharing sensitive IP during AI interactions.
My Role: UX Strategy & Execution
Led the UX vision, complex workflows and strategic AI assistant design.
Partnered with PMs, AI engineers, and component experts to align on real engineer pain points.
Designed flows, mockups, and high-fidelity prototypes with AI affordances in mind.
Conducted usability tests, gathered behavioural insights, and refined for clarity and performance.
Performance & Results
70% faster part discovery for hardware teams.
Reduced reliance on spreadsheets and supplier websites.
Increased confidence in selection via clear AI explanation & scoring.
Onboarding time for junior engineers reduced significantly.
Impact
💡 Case Study: Accelerating R&D for Consumer Electronics Team
Before using the AI assistant, the team spent 3+ hours per sensor evaluation cycle. With Altium's AI assistant:
Discovery time dropped to under 30 minutes.
AI helped them prioritise by their relevant comparison criteria e.g power consumption and price instantly.
The tool became an onboarding staple for new team members.
Want to Elevate B2B Products with AI-Powered UX?
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