Bloom
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Role
Tools
Team
Duration
Context
Investing can feel overwhelming, especially for new investors. With thousands of stocks and ETFs to choose from, most either freeze up or chase whatever is trending on TikTok, YouTube, or Reddit. Bloom is built to change that by guiding Gen Z through the process, helping them learn how to invest and understand the reasoning behind their choices.
We saw an opportunity to move beyond simple discovery and help users actually understand why certain investments make sense. That insight led to AI-powered stock portfolios, themed collections of stocks and ETFs that users can create, edit, and explore with AI guidance.
Problem Statement
Despite their interest in investing, many young adults feel like they are guessing in the dark. They open an app ready to start but quickly get lost in too many choices and too little clarity. The challenge is not just the number of options but the lack of guidance they can actually understand.
Each square represents a stock in the S&P 500. For a new investor, it’s a lot to take in.
And that’s just one index — there are over 5,000 publicly traded stocks in the U.S. alone.
What I Learned From Users
New investors told us they often check TikTok, Youtube, and Reddit before making decisions, because “investing apps throw a lot of stocks at you without context.”
Others admitted they had tried stock collections on competing apps but abandoned them because “it feels like a black box, I don’t get why those companies are in there.”
Some wanted more of a starting point: “I just want something that matches what I care about, like sustainability, without spending hours researching.”
New investors don’t need more stocks, they need context.
Research
Research Overview
- Interviewed users about how they currently make investing decisions. Many admitted to relying on social media hype.
Benchmarked competitors like Robinhood, Alinea, Stock Screener, ChatGPT, and Perplexity. Each had strengths but also key gaps.
We ran feedback sessions on early AI explorations, where users shared how they approached stock decisions and what felt confusing or useful. The sessions revealed that while people were open to AI support, they valued clarity and speed.
Competitive Analysis
To understand how Bloom could stand out, I ran a competitive analysis of leading investing platforms and AI tools. While each platform had strengths, I noticed consistent gaps: playlists without reasoning, overly complex AI outputs, or surface-level news with little personalization.
This showed a clear opportunity for Bloom to combine AI-powered explanations with beginner-friendly design, making stock discovery both transparent and approachable for new investors.
Four Core Insights Shaped the Design
1. Trust through transparency — Users won’t act on AI suggestions unless they understand the “why” behind them. Explanations needed to be short, clear, and tied to real sources.
2. Clarity at a glance — Investors make decisions quickly. Explanations had to be scannable in seconds, not paragraphs of jargon.
3. Personalized entry points — Playlists tied to user interests (like Clean Energy or AI Leaders) drove far more engagement than generic stock bundles.
4. Confidence over complexity — Experienced users appreciated depth, but new investors needed guardrails to prevent overwhelm. Bloom had to balance both.
Research has shown that users are more likely to trust and adopt AI when its predictions come with clear, understandable explanations. (Ribeiro, Singh, & Guestrin, 2016).
Ideation
Guiding Principles
User Flow
I started by mapping out user flows that would take someone from zero to a curated playlist in under a minute. The focus was on reducing friction and making sure the path to value was fast and intuitive.
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Sketches
After finalizing the flows, I translated them into low-fidelity sketches to explore the core interactions. These sketches gave shape to the experience and helped the team visualize how the AI would show up inside the app.
These early sketches allowed me to quickly test ideas, align with developers on feasibility, and get feedback from the team before moving into higher fidelity prototypes
Iterations Based on Feedback
Early versions left users with a blank chat box, which felt overwhelming and unhelpful. After feedback from the CEO, I redesigned the flow to include guided prompts. Contextual prompts tied directly to the current playlist (e.g., “Why was Nvidia recommended?”) while general prompts encouraged exploration (e.g., “Build me a dividend-focused playlist”). This shift made the AI feel less like a tool and more like a coach, creating a smoother experience for beginners while still supporting advanced users.
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Proposal
Key Features
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Reflection
Working at Bloom gave me my first real taste of designing in a fast-paced startup environment. Deadlines were tight, priorities shifted quickly, and I had to get comfortable moving fast. What made it valuable was the fail-forward culture. I was encouraged to prototype quickly, present ideas often, and learn from feedback instead of aiming for perfect first drafts. That rhythm pushed me to explore more directions than I normally would and taught me how to turn feedback into clear next steps.
Another big part of the experience was realizing how much my perspective as a Gen Z designer mattered. Since Bloom is built for Gen Z investors, my instincts and lived experience brought real value to the team. I could call out when something felt too complex or robotic, and that input directly shaped the product. It showed me that design is not just about flows and visuals, but also about representing the users you are designing for.
I also had to adapt my process to match the way the team worked. Bloom moved quickly, tested constantly, and relied on tight collaboration between design, engineering, and product. I learned that being a good designer is not only about strong ideas, but about integrating into the team’s pace so those ideas can actually ship.
This project taught me how to thrive in a fast-moving environment, how to use feedback as fuel for better design, and how to adapt my process while still bringing my own perspective to the table.
