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AppliScan

Track Chosen

Renewables

AppliScan advances renewable energy adoption by addressing inefficient household electricity demand. By making appliance-level energy use, long-term cost, and carbon impact visible at the moment of purchase, AppliScan helps consumers choose energy-efficient products that reduce baseload demand and enable grids to rely more heavily on renewable sources instead of fossil fuels.

Problem Statement

In U.S. homes, nearly 40% of electricity consumption comes from household appliances and electronics, yet appliance purchases are still made primarily based on upfront sticker price. The true long-term cost—electricity bills, carbon emissions, regional utility rates, and missed efficiency incentives—is rarely visible when consumers are making purchasing decisions.

As a result, millions of inefficient appliances are purchased each year, increasing household energy costs and reinforcing higher baseline electricity demand—making it harder for grids to transition to renewable energy and increasing reliance on fossil fuel generation.

Ideation & Development Process

Ideation

We identified appliance purchasing as a high-impact decision point where consumers lack accessible energy information. Since renewable adoption depends not only on clean generation but also on efficient consumption, we focused on building a tool that makes long-term energy consequences clear at the exact moment a purchase decision is made.

Development

Frontend & UX

Next.js, React, and TypeScript for a scalable web app
Tailwind CSS for a clean, mobile-first interface
Live camera access using the Browser MediaDevices API
Persistent, global voice assistant across all views

Backend & Data

ENERGY STAR API for certified appliance energy consumption data
ZIP code → state mapping for region-aware electricity rate modeling
Data validation pipelines for accurate calculations

AI & Voice

VAPI for low-latency, hands-free voice interaction
ElevenLabs API for natural text-to-speech
Domain-specific prompts for appliance shopping and energy literacy
Emotion-aware responses (excited for savings, cautious for high costs)
Web Speech API fallback for reliability

Core Logic

OCR-based model number extraction with appliance-specific pattern correction
10-year total cost of ownership (TCO) calculations
Carbon emissions estimation using state-level grid carbon intensity

Challenges

OCR accuracy in real retail environments (lighting, angles, fonts)
Sharing product context across pages for the voice assistant
Browser autoplay restrictions for voice output
Balancing live camera performance with background OCR processing
Translating complex energy and carbon data into understandable metrics

Solution Proposed and Intended Impact

Solution

AppliScan is an AI-powered appliance scanning and analysis tool that helps consumers evaluate energy-consuming products in real time.

Key Features:

📷 Smart label scanning using OCR to identify appliance models
💰 10-year total cost of ownership calculations
🌱 Lifetime carbon emissions estimates
🔁 Recommendations for more energy-efficient alternatives
🗣️ Hands-free voice assistant for real-time questions during scanning

Rather than presenting static numbers, AppliScan uses conversational AI to explain tradeoffs, highlight long-term consequences, and keep users engaged throughout the shopping process.

Intended Impact

AppliScan enables consumers to make informed, sustainable purchasing decisions without technical expertise. By combining real-time data with an engaging, human-centered AI assistant, it lowers the barrier to energy literacy at a critical decision point.

At scale, AppliScan reduces household energy costs, lowers carbon emissions, and supports renewable-heavy grids by decreasing inefficient electricity demand—aligning everyday consumer behavior with long-term clean energy goals.