In a world of infinite choice and diminishing trust, Q let you ask the people who know you best for personalized recommendations on anything. By turning everyday questions into collaborative, context-rich lists, Q became a “search engine for your friends’ brains,” built on trust, relevance, and real human understanding.
The Set Up
At the end of 2019, I left Amazon to work on Q with three former colleagues. Our mission was to build a simple app that made it effortless to gather recommendations from friends and family for anything in life (restaurants, movies, shows, books, music, and more) by streamlining and capturing the natural word-of-mouth process.
I invested a small amount of seed capital, and we began developing the product. In January 2020, we released an invite-only alpha to the App Store, reaching about 2,000 initial users to gather feedback and validate the concept.
My Role
Title: Co-Founder / Head of Product
Responsibilities: Q was a passion project born from an idea I had been exploring for years. My role was to assemble the team, define the product vision, lead user experience design, and guide how we got the app into users’ hands. My three partners handled development and design execution.
The Product
Problem:
In a world of infinite choice, finding the right thing—at the right moment, matching your personal needs, tastes, and values—had become nearly impossible. Recommendation systems, ads, and search results all blurred together, leaving people longing for authentic, trustworthy guidance.
Solution:
Empower genuine, high-fidelity word-of-mouth recommendations within your social circle by creating a simple, fun, and comprehensive tool that lets you easily query the people who know you best.
1. Context as the Missing Ingredient
Q’s power came not just from who you asked, but how precisely you could describe what you needed. Because your friends (not an algorithm) were parsing your request, you could add rich personal context no search engine could understand:
“Having movie night with Lilly, she wants a funny romantic comedy with a wedding. Any ideas?”
“Looking for a restaurant in Santa Monica that’s quiet, has a patio, and feels right for a first client meeting.”
“Vicki and I are building a playlist for our beach reception, give us your best suggestions!”
That context turned Q into a medium for deeply personal discovery, grounded in shared understanding. Long before LLMs could interpret nuance, Q was built on the idea that the people who know you understand your context best.
2. Prioritize the Ask
We focused on the asker (the person looking for something) as the primary driver of engagement. Their motivation to find answers would naturally pull others into the experience.
3. No Ads, Ever
Authenticity was sacred. Q was designed as a space for genuine recommendations with no ads, no sponsored content, no noise.
Results
Built and launched a functional alpha version with the full core workflow and feature set.
Privately released to 2,000 users for concept testing and workflow validation.
Early metrics showed strong engagement and high satisfaction, with users describing “magic moments” as recommendations appeared in real time from across their networks.
Development was ultimately halted by the COVID-19 pandemic, which disrupted funding and team availability.
The project validated the core concept and caught the attention of a former Amazon SVP, leading to an acquihire by Saks Fifth Avenue in 2021.
Looking Back
Q was a deeply personal project, an opportunity to step away from big-company infrastructure and return to the raw, creative energy of building something from scratch. After years of enterprise-scale products, it was refreshing to design an experience that my friends and family could immediately understand and love. Working again with my original Roambi team, we explored the intersection of technology and human connectionusing digital tools to make relationships, not algorithms, the engine of discovery.
When COVID hit, venture markets closed and the project had to be shelved, but the experience was far from a loss. We saw flashes of real magic in user testing and learned invaluable lessons about where the product resonated, and where it didn’t. Even without reaching market, Q made a lasting impact, ultimately bringing our team into Saks to help build their next generation of digital experiences.
How it worked:
1. Ask Anything
Users could create a natural prompt like, “Looking for a great sci‑fi movie to watch with my 10‑year‑old tonight.”
2. Share Effortlessly
Q sent the request to your friends on the app with a simple notification. You could also share with anyone via text, email, or posting to your social media, allowing them to respond through a mobile web app even if they weren't a user.
3. Collect and Rank
All responses were merged into a single ranked list, showing which recommendations appeared most often. Every participant could vote, comment, and see who suggested what.
4. Enrich the Content
Each item had its own detail card with metadata, comments, and direct actions (watch, buy, reserve etc) depending on it’s type.
5. Social Graph and Profiles
Friends could follow one another, and earn status like Film Buff, Foodie, or Music Maven. Profiles showed both given and received recommendations.
6. Viral Growth Loop
Each new request propagated Q through natural sharing. A friend helping you find a sushi spot today might use Q next week to ask about hiking trails.
7. Roadmap Vision
Over time, the growing dataset of human recommendations would power an AI‑driven discovery experience allowing users to browse the total body of recommended items by context, recommender, or category.