Case Study

AI Enterprise Global Search

Client

US Bank Single Point

Role

Lead Experience Designer

Timeline

6 Months

My Contribution

Role & Approach

Led the end-to-end UX and information architecture for SinglePoint Global Search and Help Explorer, facilitating design thinking workshops, cross-functional collaboration, and UX research to uncover user needs and search challenges, by leading 2 teams.

Conducted ideation and brainstorming sessions, moderated user research, mapped user flows and wireframes, and partnered with product, engineering, and content teams to create a scalable, intuitive enterprise search experience.

Guided junior designers through critiques, design reviews, and cross-functional collaboration to strengthen team outcomes and consistency.

Problems

Problem Statement

Enterprise users at US Bank struggled finding treasury information quickly due to complex navigation and inconsistent search behavior. The existing search experience lacked scalability and relevance, creating high cognitive load and reducing efficiency across the platform.

✔ Users struggled locating treasury actions quickly

✔ Navigation hierarchy created confusion

✔ Search terminology mismatched user expectations

✔ High cognitive load reduced task completion rates

Research

Research & Discovery

Conducted extensive user research including 13 usability testing sessions, stakeholder interviews, and behavioral analysis. Identified key pain points and workflow patterns that informed the design strategy.

✔ 13 usability testing sessions with enterprise users

✔ Stakeholder interviews across 5 departments

✔ Behavioral heatmap analysis

✔ Competitive analysis of enterprise search patterns

Design Process

Followed a structured UX process from discovery through delivery. Developed 4 design iterations, each tested and refined based on user feedback and performance metrics.

Step 1

Discovery & Research

Step 2

Information Architecture

Step 3

Wireframing & Prototyping

Step 4

User Testing & Iteration

Step 5

Final Implementation

Competitive Analysis

Exploration

Types of Searches

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Basic Search

User enters a keyword or phrase in the global search bar
System auto-suggests results (typeahead/autocomplete)
User selects a suggestion or hits enter
Results page displays categorized results
User clicks on a result to view details
Optional: User refines results using filters

Example

Enter keyword or phrase: A user types “Apple Inc.” into the global search bar.
Select suggestion or submit query: Autocomplete suggests “Apple Inc. (Equity)” or “Apple Inc. (Client Profile)”
View categorized results: Results are grouped under tabs like “Companies”, “Transactions”, “Documents”
Refine results (optional): User filters by “Last 30 days” or “Region: North America”

Advanced Search

User clicks “Advanced Search” or expands filters
User selects multiple criteria
User submits the query
Results are displayed with applied filters
User can save the search or export results

Example

Open “Advanced Search” or expand filters: User clicks “Advanced Search” next to the search bar
Select criteria: Chooses “Transaction Type: Equity”, “Amount > $10M”, “Date Range: Q2 2025”
Submit query: Clicks “Search”
View results with filters: Sees only equity transactions over $10M in Q2
Optional: Saves this search as “High-Value Equity Deals Q2”

Role-Based Search

System identifies user role
Search bar adapts to show relevant sources or filters
User performs search
Results are prioritized based on role relevance
Optional: Role-specific actions

Example

System identifies user role: Admin logs in
Search bar adapts: Shows client-related filters like “Username”, “Authentication”
User performs search: Searches “Manage services”
Results prioritized: Top results show users with active engagements
Role-specific actions: Can click “Modify users” or “Set up user limits”

Semantic/NLP Search

User enters natural language query
NLP engine parses intent and entities
System translates to structured query
Results are displayed with explanation of interpretation
Optional: User refines or corrects interpretation

Example

Enter natural language query: User types “Show me all deals over $5M closed last month”
Parse intent and entities: NLP engine identifies “deals”, “$5M+”, “closed”, “last month”
Translate to structured query: Converts to SQL or API call
View results: Displays matching deals with a note: “Interpreted as: Deals > $5M closed in September”