GovTech Information Architecture Startup

Immigrant Services Resource Discovery Engine

A faceted search and content structure system designed to make immigrant services easier to navigate, understand, and scale as the resource base grows.

Overview

This project addressed a common but high-impact service design problem: newcomers seeking career and support services were faced with too much information, unclear categorization, and limited support for finding what was actually relevant to their needs. Rather than treating this as only a search issue, I approached it as an information architecture problem shaped by user mental models, service complexity, and future content growth.

The goal was to create a structure that helped users find critical resources faster while giving the organization a foundation that could scale over time.

Problem Statement

Users searching for immigrant services often encountered information overload, inconsistent labels, and unclear navigation paths that made it difficult to identify the right resources. This was especially challenging for internationally trained professionals navigating career transitions while also dealing with unfamiliar systems, limited English proficiency, and overlapping service categories.

The design challenge was to create an architecture that reduced cognitive burden in the moment while remaining flexible enough to support a growing and evolving content ecosystem.

Key Challenges

  • Organizing complex service information in ways that matched users' expectations rather than internal assumptions
  • Designing an architecture that improved findability immediately while remaining scalable for future categories, filters, and content expansion

Research Process

Step 01
Audit of existing structure and user failure points
I began by examining how service information was currently organized and where users were getting lost. The issue was not simply that there was too much content, it was that the underlying structure did not align with how newcomers expected to search, compare, and interpret available resources. Early research showed that what looked like a UI problem was actually a deeper information architecture failure.
Step 02
Discovery sessions and database review
Through interviews, discovery sessions, and hands-on review of the existing database, I identified major structural issues: inconsistent tags, overlapping categories, missing metadata, and unclear boundaries between internal and user-facing language. This revealed a mismatch between how the system stored information and how users made decisions.
Step 03
Card sorting to build a user-informed taxonomy
I used collaborative card sorting to help reclassify the service database around users' mental models rather than organizational logic. This process informed a clearer content taxonomy, more consistent labels and definitions, and a stronger set of rules for structuring resources going forward.
Step 04
Faceted, mobile-first search design
Once the content foundation was improved, I worked with the broader team to shape a faceted, mobile-first search experience that supported simpler decision paths, lighter language, and multilingual-friendly interaction patterns. Instead of forcing users through one rigid navigation path, the new structure supported multiple ways to discover relevant support.

Impact

Search Relevance

Improved search relevance by 60% through a clearer, user-informed content structure and faceted search optimization.

Scalable Foundation

Established a scalable architecture that supported future growth without increasing navigation complexity, solving not only for the current database but for the organization's long-term content needs.