Saveability
Empowering conscious consumers to shop sustainably with instant product verification
Overview
Saveability is a mobile app that helps conscious consumers make sustainable purchasing decisions through instant barcode scanning and AI-powered product verification. In a market full of greenwashing and information overload, Saveability provides transparent, trustworthy sustainability ratings at the moment of purchase.
This project explores how design can help people align their values with their shopping habits—turning complex research into a simple 3-second barcode scan.
Browse & Profile: Category navigation and user account management
The Problem
The Greenwashing Crisis
Consumers want to shop sustainably, but they can't trust brand claims. With 52% of shoppers encountering false or misleading sustainability marketing, and 23% wanting to buy sustainable but not believing brands, there's a critical trust gap in the market.
I want to do the right thing, but I don't have time to become an expert on every certification. I've been burned by greenwashing before—I need something I can actually trust. — User research participant
Key Insight
The problem isn't lack of information—it's inability to verify and act on information at the moment of purchase. Consumers need instant, trustworthy answers, not more research homework.
Research & Discovery
The Solution
Instant Sustainability Verification
Saveability transforms complex sustainability research into a simple 3-second barcode scan. Users get clear, trustworthy scores backed by third-party certifications, plus AI-powered alternatives when better options exist—all at the moment of purchase.
Certification guide: Educating users on sustainability standards and their meanings
Key Features
Instant Barcode Scanning
Point your camera at any product barcode and get results in under 3 seconds. No typing, no searching—just instant verification while you shop.
Clear Sustainability Scores
Overall score (0-100) with detailed breakdowns: Environmental Impact, Social Responsibility, Company Ethics, and Transparency. Letter grades make it instantly understandable.
AI-Powered Alternatives
When a product scores low, our AI suggests 3 better alternatives at similar price points—available at nearby stores or online with carbon-neutral shipping.
Verified Certifications
See all relevant certifications (B Corp, Fair Trade, Organic, etc.) with explanations of what they mean and why they matter.
Impact Dashboard
Track your sustainable shopping impact over time: plastic avoided, carbon saved, ethical brands supported. Real metrics, not vanity numbers.
Transparency Reports
"The Good" and "The Bad" for every product—honest, clear information about trade-offs so users can make informed decisions.
The Scanning Experience
The core interaction is the barcode scan. We designed it to be fast, forgiving, and reassuring—critical for in-store usage where users feel self-conscious scanning products in public. When a product scores poorly, we don't just tell users—we help them find better options.
1. Scan a product barcode
2. View sustainability report
3. Find better alternatives
End-to-end flow: Scan, evaluate, and discover sustainable alternatives in seconds
Design Insight
We show long-term cost comparison (e.g., "$32.95 vs $155.48 over 1 year") to reframe sustainable products from "expensive" to "cost-saving." This helps users justify the upfront investment.
Key Design Decisions
Success Metrics
Saveability is a 0→1 concept, so the numbers below are the targets the design was built against — not measured outcomes. They define what the experience would need to deliver to be considered successful at launch.
Scan history: Past scans and product reports
Impact dashboard: CO₂, plastic, and water savings
From barcode to camera
The shipped Saveability concept solves the verification problem with a scan-and-score model. The natural next step is to remove every friction point in that chain — the barcode dependency, the static numeric score, the one-size-fits-all judgment.
An AI-enhanced Saveability turns the camera into the only input the user needs, and the verdict into a personal explanation aligned to what each shopper actually cares about. Three AI capabilities layer onto the existing flow:
Point-and-shoot recognition
A packaging classifier removes the barcode dependency. Niche, store-brand, and unbranded products become scannable too — the long tail that breaks today's database lookups.
Personalized verdict
Replaces the numeric score with a plain-language explanation, weighted against each shopper's stated values. The "why" is legible in one read, not buried in a certifications list.
Better-alternative ranking
A recommendation model surfaces 3 alternatives ranked by predicted utility — filtered for price parity, in-stock locally, and aligned to the same value chips that drove the verdict.
Captured during onboarding (Low carbon · Recyclable · Fair labor) — every verdict is scored against them, so the same product can score differently for different shoppers.
Vision model identifies the product without barcode dependency. Confidence is surfaced explicitly so the user can correct a misidentification before acting on the verdict.
The 7.2 score sits in context: a one-paragraph explanation calling out where this product is strong on the user's values and where it's weak ("transit emissions pull the score down 1.2 for your East Coast region").
Tapping the affordance reveals the underlying certification data and how each factor was weighed. Thumbs feedback trains the personalization model. Failure state: drop to existing static score-and-list view.
Borrowed directly from Apple Visual Look Up's failure-mode framing. Asserting and being wrong is worse than acknowledging uncertainty — especially in a verification context where misidentification can drive a wrong purchase.
78% / 62% bars let the shopper pick the more-confident option themselves rather than forcing the model to choose. The system's uncertainty becomes the user's affordance.
When both candidates are wrong, the shopper isn't trapped. The escape is text-link weight (not a button) so it doesn't compete with the primary "Select" actions, but is one tap away.
A different shopper with carbon-only values would see 8.4, not 7.2. The "FOR YOU" pill makes personalization visible — score is a property of (product × shopper), not the product alone.
"Emissions are 40% lower than dairy. Fair labor is mid-tier; supplier audit is 2024 (2 years old). Transit pulls the score down 1.2 for your East Coast region." Full reasoning chain — the shopper can disagree with the weighting and the next chip update will reflect that.
Score is a means, not an end. Saveability earns engagement when it helps shoppers act, not when it produces a number. The CTA bridges from verification to decision.
- Yuka — the plain-language verdict pattern (Excellent / Good / Mediocre / Bad with one-line explanations) makes the rationale legible in 2 seconds. Layer personalization on top of that pattern.
- Google Lens shopping — the bounding-box-then-card flow is the cleanest template for "object recognition → information retrieval" without modal-overlay heaviness.
- Apple Visual Look Up — sets the bar for showing the AI was wrong gracefully ("Possible matches" rather than asserting one identity), which is critical for trust in a verification context.
Key Learnings
1. Intent-Action Gap is Real
85% of consumers want to shop sustainably, but only 26% do. The gap isn't motivation—it's friction. Reducing verification time from minutes to seconds can close this gap.
2. Context Beats Data
Users don't need more information—they need interpreted information. "This product contains PFCs" means nothing. "Contains PFCs (harmful chemicals that persist in environment)" drives understanding.
3. Affordability is Non-Negotiable
Suggesting $80 alternatives for a $30 product creates guilt, not behavior change. AI must filter for price parity or users lose trust in recommendations.
Reflection
This project reminded me that good design isn't about adding features—it's about removing friction. The biggest decision wasn't what to show users, but what NOT to show. By keeping things simple and prioritizing speed, Saveability turns a 30-minute research task into a 3-second decision.
The greenwashing problem isn't just about marketing—it's about how we present information. When 52% of consumers see false claims, adding more claims isn't the answer. Trusted verification is. Saveability shows that with the right design, we can help people act on their values in real time.
Impact Beyond the App
If 500K users each make just one more sustainable purchase per month, that's 6 million conscious decisions per year. At scale, verification tools like Saveability don't just help individual shoppers—they create market pressure for brands to become more sustainable or risk being exposed.