AI Powered Wealth Advisor

Project Goal

Design an AI powered wealth planning platform that helps advisors and high net worth clients turn complex financial data into clearer insights, smarter decisions, and more personalized planning.

Personal Goal

Design this product while using AI not just as the subject of design, but as an active collaborator throughout the process.

My Role

Lead Product Design
End-to-End UX & UI Design
Product Strategy
AI-Assisted Design Workflow

Collaboration With

Financial Domain Experts
Product Managers
Engineers
Executive Leadership
Sales

Challenges

Three core challenges shaped this AI product. Here's how I resolved each.

Black Box Recommendations from AI

Advisors are accountable for every client outcome. AI output they cannot verify is output they will not use.

Automation vs. Advisor Control

Automate too much and the advisor becomes a rubber stamp. Automate too little and the AI is not worth adopting.

Two Audiences, One Data Set

Advisors need depth and control. Clients need clarity and confidence. The same financial data has to serve both.

Solved via

Show the Reasoning

Every recommendation surfaces the inputs behind it, so advisors can trace, verify, and defend the logic to clients.

AI Proposes, Advisor Decides

AI drafts the analysis and the narrative. The advisor edits and approves before anything reaches the client.

One Engine, Two Views

Shared data models render differently by user types: analytical density for advisors, narrative simplicity for clients.

Black Box Recommendations from AI

Advisors are accountable for every client outcome. AI output they cannot verify is output they will not use.

Solved via

Show the Reasoning

Every recommendation surfaces the inputs behind it, so advisors can trace, verify, and defend the logic to clients.

Automation vs. Advisor Control

Automate too much and the advisor becomes a rubber stamp. Automate too little and the AI is not worth adopting.

Solved via

AI Proposes, Advisor Decides

AI drafts the analysis and the narrative. The advisor edits and approves before anything reaches the client.

Two Audiences, One Data Set

Advisors need depth and control. Clients need clarity and confidence. The same financial data has to serve both.

Solved via

One Engine, Two Views

Shared data models render differently by user types: analytical density for advisors, narrative simplicity for clients.

Key Features

Four product questions, four answers.

Building an intelligent advisor meant answering four things: where the AI gets its data, how it reads a client's health, how clients get in without friction, and how it points to what comes next.

Q. Where does the AI get its data?

The Foundation Every AI Decision Reads From

Bulk-upload documents and the AI extracts and structures them into a single financial statement, verified with human review. It gives advisors and clients one unified view of assets, liabilities, income, and expenses to quickly spot gaps, risks, and opportunities.

Q. How do we read a client's financial health?

A Score to Measure It

Client Score turns complex financial indicators into one easy-to-understand number. The AI builds a profile from each client's data, benchmarks them against people with similar lifestyles and financial profiles ("people like me"), and reads the result as a score and a risk matrix.

A Story to Explain It

Client Story turns that analysis into a personalized narrative. Instead of isolated numbers, it explains goals, risks, and opportunities in language that makes the full picture clear, whether it's read by a client or discussed with an advisor.

Q. How do clients get in without friction?

Onboarding That Meets Clients Where They Are

Clients enter through whatever fits: chat with the AI, bulk-upload documents, connect accounts, or enter data manually. The AI extracts and structures whichever path they choose, cutting onboarding friction while improving data completeness for planning.

Q. What comes next?

Fund the Life, Plan the Money

Clients define life goals, then fund them from real account balances and future cashflow.

Grounded in each client's current position, the AI helps set realistic targets and shows why, with the reasoning and a simulation behind every number. The hard math runs on the AI; the advisor validates.

The Money Plan tracks which goals are on track and which are drifting, and AI surfaces strategies to close the gap.

Plus: advisor control (Agent Library)

Advisors Choose Their AI

A browsable library of AI agents advisors activate per client: meeting prep, document monitoring, goal drift alerts, coverage gaps. Every agent is opt-in, so advisors decide what the AI watches and what it leaves alone.

Design Process

Designing an AI Product with AI

Human judgment remained central; AI accelerated exploration.

I used AI as a design collaborator to accelerate exploration, validate ideas, and iterate faster across the product lifecycle. The biggest advantage was rapid prototyping, which helped test and compare ideas far more quickly than traditional workflows.

Below is

What I Did vs

What AI Did in my design process.

Discovery & Alignment

Align on problems, opportunities, and product direction.

Collaborate with stakeholders, leadership, product owners, and sales to identify business goals and user needs.

Research benchmarks and competitor patterns to surface insights and best practices. Flesh out the feature definition.

Rapid Ideation with Prototyping

Turn ideas into testable prototypes with the biggest productivity gain from AI.

Define MVP scope, key flows, and success criteria to guide exploration based on business requirements.

Generate interactive prototypes quickly, compare multiple directions, and validate ideas.

Deep Feature Exploration

Design complex modules and workflows at scale.

Make product decisions, define experience principles, and refine workflows for final design assets.

Explore feature logic, module flows, and edge cases to stress test scenarios and expand solution possibilities.

Visual & Content Iteration

Accelerate design variations and UX writing.

Curate visual direction, prioritize options, and ensure consistency across the product.

Generate layout variations, visual concepts, feature naming, and microcopy to accelerate iteration.