Ava Liu

The Problem

Most AI products present a conclusion and ask the user to accept it. In a domain where a wrong or misread signal has real consequences, that's not good enough. The core question: how does a user know when to trust an insight — not "is the AI usually right," but "can this user, looking at this output, tell whether to act on it right now."

Approach

Owned end-to-end product strategy, UX, information architecture, and AI system design for an explainable AI financial data analytics platform. Split a single blended confidence score into three independent metrics — Data Quality, Analysis Reliability, and Evidence Alignment — so a user sees not just how confident to be, but confident about what.

What I Built

• A multi-page information hierarchy turning high-density data into a structured, interpretable path
• Interactive, zoomable visualizations — time-series charts and multi-metric breakdowns across 180+ data points
• A personalized feedback layer plus a companion admin console for platform-level oversight
• An application-based beta access flow, balancing controlled rollout with a low-friction path in

Built With

FastAPI, Next.js, Python, Canvas-based rendering — directed via Claude Code and Codex.

The Problem

A self-built, full-stack AI art product had a 38% generate-to-preview conversion rate paired with near-zero purchases. The easy read: an acquisition or pricing problem. Instead of assuming, I went to the users.

Diagnosis

User interviews and usability testing with non-converting users — not analytics alone — surfaced the real blocker: likeness. The AI-generated pets didn't look enough like the actual pet. This reframed the problem from "funnel" to "trust in the output."

Design Response

• An AI pre-processing step that converts each pet's distinguishing features into a personalized generation prompt
• Redesigned the preview flow from one image to three, engineering a parallel workflow so generation time didn't increase
• Iterated the loading experience from a generic bar to a step-by-step status display, keeping users patient through longer waits
• Built delivery logic to track and hand off the exact image a customer selected through payment

The Problem

Marketing teams needed to produce more campaign content across channels and formats, but relying on designers for every asset created production bottlenecks. Generative AI could increase speed, but without a structured system it also introduced inconsistent visuals, off-brand outputs, and unpredictable quality.

Approach

Designed an AI-enabled creative workflow that turned simple marketing briefs and variables into structured prompts, then supported image and video generation, brand-safe quality control, and automated adaptation across multiple formats. Built the workflow around reusable design rules and guardrails so non-designers could create campaign-ready assets with greater independence while maintaining brand consistency.

What I Built

• An AI creative workflow that translated simple marketing briefs and variables into structured generation prompts
• Repeatable image, video, and creative-generation steps with brand, layout, spacing, and quality rules built into the process
• An AI-powered multi-format resizing workflow (Weavy) that adapted creative for different marketing channels and formats
• Reusable templates and guardrails that enabled non-designers to generate campaign-ready assets independently
• Brand-safe prompting practices and quality benchmarks governing AI-generated visual assets
• Mentored the marketing team on prompting, workflow usage, quality control, and building practical AI-enabled creative processes

Outcome

Reduced dependency on designers for routine production by giving the marketing team a structured, brand-safe way to create and adapt campaign assets independently. Shifted AI from an individual creative tool into a repeatable team workflow.