Connie
Connie is an AI shopping assistant browser extension that combines Consumer Reports' lab data, real community sentiment, and the user's own values into personalized product insights, helping people make more confident purchasing decisions. It was built over a 7-month capstone in collaboration with Consumer Reports, a nonprofit with a 90-year legacy of independent, unbiased product testing and a mission to hand power back to consumers.
Role
Product Manager
Timeline
7 months (Jan–July 2026)
TEAM
2 Product Managers
2 Product Designers
1 Design Engineer
Skills
Product Management
Design Strategy
User Research
PROBLEM
Maya is buying her first stroller, but she doesn't know what she should be looking for. Even with a dozen tabs and a hundred reviews open, she still doesn't know what to trust.
Late 2024 marked the first real wave of AI-powered shopping. Traffic to retail sites from AI chatbots spiked 2,000% over the holidays as consumers turned to AI for gift ideas and product research. That shift has since evolved into agentic commerce, where algorithms can autonomously fill carts and complete purchases. But built the way the rest of the internet already works, AI shopping risks becoming just as warped by ads and persuasion.
How can Consumer Reports evolve in the era of AI and continue fulfilling their mission of protecting and empowering consumers?
As more consumers turn to AI to research products and make purchasing decisions, how can they trust that what's being recommended isn't paid placement or persuasion in disguise? Consumer Reports challenged us to imagine the alternative: consumer-first AI, designed with loyalty and intention to serve the person over the platform and help consumers buy in line with what really matters to them.
RESEARCH
Consumer Reports has earned trust, but still needs to gain the presence necessary to meet the needs of consumers today.
While 58% of consumers have used AI to shop, 74% would not trust AI to make a purchase on their behalf. Rather, they want AI to help them research and narrow down options. Shoppers 45+ still lean on traditional search like Google or Amazon, then validate their decisions with Consumer Reports or other trusted review sites, but 18–34 year-olds turn to social media and online communities for real lived experiences.
EXPLORATION
The most important shift in our process was reframing the problem from "buy it for me" to supporting discovery.
Our team started this project thinking we would design and build an autonomous purchasing agent, but our research proved that consumers do not trust AI to do that just yet. This reframe was the key to solving CR's presence problem: how do you meet modern shoppers where they already are? A confident purchase doesn't come from someone, or something, else making the final call. It comes from transparent reasoning earlier during research: understanding exactly why a product is being suggested, and how it does or doesn't fit your specific life.
Every concept we explored was tested against the same question: how does this solve for presence? Where would it show up? How would it bring more people into the CR ecosystem, and would it stay true to CR's mission?
A social proof concept pitted CR's lab data against social sentiment, testing whether transparent reasoning could keep users from leaving to research elsewhere.
A digital discovery concept explored CR as an ambient trust layer riding alongside a user's browsing rather than a destination they have to seek out.
A human-in-the-loop concept explored how much control people want to hand off to an AI shopping agent, and whether it can learn their values through behavior instead of explicit setup.
SOLUTION
Connie meets shoppers where they already are, combining CR's lab data with real online sentiment and the user's specific values and life context to help them shop with confidence.
FEATURES
Connie highlights top-recommended products directly on the retailer page. Selecting one surfaces a card combining CR's lab testing with real owner sentiment, filtered through preferences set at onboarding.
Highlighting a claim on a product page prompts Connie to check it against CR's lab data and real owner reports, flagging it as misleading, verified, or unverifiable. While objective scores are helpful, shoppers also need to know if what they're reading is true.
Instead of a long preference survey, Connie opens with the priorities that typically matter for a given product category, then refines them through a few contextual questions (living situation, mode of transportation, etc.).
Once Connie understands what matters to the user, it narrows hundreds of options into a shortlist, each with plain-language rationale and links to supporting evidence.
Connie checks in the next time the user shops a related category, asking about a past product. Lived experiences after months of use is as valuable as an expert review upfront. This directly addresses a coverage gap, since there are many product categories not yet tested by CR that people ask about. Each check-in feeds better guidance to the next shopper.
IMPACT
Connie meets consumers where they already are, delivering personalized product insights and community perspectives across any platform they shop on and helping them navigate misinformation with a source of truth while they're shopping. This project was well received by our client and sparked a broader conversation about how CR could expand its presence in new ways.