Fidelity Investments: Core Accounts & Clients Search
Search built around advisor intent
Jan 2025 – Present
"Search is how advisors access every client conversation. When it fails, everything else slows down."
— User research insight
Task
Redesign search to handle thousands of accounts with clarity and speed.
Advisors search across accounts, clients, and relationship groups at a scale where a single query could return up to 30,000 results. This case study follows the work as context, constraint, decision, trade-off, and outcome.
Overview
Designing clarity for complexity
Instead of returning everything that matched a query, we focused on capturing advisor intent and presenting only what mattered.
We introduced intuitive pathways to search across different entity types — accounts, clients (client-investor and client-relationship), and groups — improved result hierarchy, and made critical information scannable at a glance. Every design decision was validated through feasibility discussions with engineering.
Core search across accounts, clients, and groups
30K
Result ceiling
4
Entity types
2
Research rounds
Project at a Glance
Product
Core account search used primarily by front-office financial advisors.
Role
User Experience Designer
Team
Product Owner, Delivery Manager, Project Manager, Developers, Business Analyst
Scope
Redesign core search so advisors can find the right entity quickly, understand what they're looking at, and know what to do next.
What it supports
A single, unified search that allows advisors to:
- •Find accounts, clients (client-investor and client-relationship), and groups.
- •Understand what type of entity they're viewing and why it appears in results.
- •Create new client relationships and new groups directly from search.
- •Continue work without context switching or leaving the search flow.
- •Use unified lookups shared with other teams for their workflows.
What I owned — the end-to-end search experience, from problem definition through design decisions:
- •Collaborated with the research team on search studies and observed sessions to understand how advisors actually search.
- •Partnered with stakeholders to define the problem space and success criteria.
- •Designed search behaviors that stay clear and usable across small and large result sets.
- •Collaborated with engineering to balance technical constraints, feasibility, and usability.
Draft
The whole case study in five beats.
Context
Advisors work across accounts, clients, and relationship groups at a scale of tens of thousands of records. Discovery research, an experience assessment, and two rounds of unmoderated study all pointed the same way: search neither scaled to real data volumes nor made clear what an advisor was looking at, so filtering was compensating for weak search behavior.
Constraint
A single query could return up to 30,000 results — a ceiling in the underlying system, not something the redesign could remove. Large sets were slow to filter and hard to interpret, entity types were hard to tell apart, and advisors took multiple corrective steps just to narrow what came back.
Decision
Rebuild search as one guided experience, layered foundation → core → advanced and prioritized around what advisors need most: capture intent at the query rather than after results load, make entity type and reason-for-match legible in the result itself, and let advisors create client relationships and custom groups without leaving search.
Trade-off
Rather than truncate silently at the ceiling, search defaults advisors into Advanced Account Search once a query crosses 30,000 results. That costs a second surface to learn and a handoff mid-task, but it beats leaving advisors to trust a result set that isn't complete — a confidently wrong answer is more expensive here than an extra step.
Outcome
Advisors can tell why a result appears without re-running a search, distinguish accounts, clients, and groups at a glance, and reach the next action in fewer steps. Narrowing happens upfront, results stay scannable at scale, and less time goes to managing results and more to advising.
Context
The existing system doesn't scale to real-world data volumes and lacks clear organization, which leaves advisors frustrated and slows their day-to-day work. Filtering was being used to compensate for weak search behavior, but filtering alone could not solve the problem.
Research & validation
Discovery research
We set out to understand:
- •How advisors think about accounts, investors, clients, and groups.
- •What advisors expect to happen immediately after running a search.
- •Where confusion, friction, and breakdowns occur in the current experience.
Experience assessment
An experience assessment by the research team surfaced key pain points, including unclear entity differentiation, inefficient filtering behavior, and limited result scannability.
The four questions the experience assessment set out to answer
Close collaboration with the research team uncovered these usability issues during the early stages of the project, which let us address them proactively and iterate on design improvements before development.
Democratization of research
Two rounds of unmoderated study let us observe how advisors navigated the search experience independently, uncovering friction points and measuring the effectiveness of key design improvements.
Key findings & design recommendations
Research identified the biggest sources of friction:
- •Filter organization
- •Search discoverability
- •Result relevance
Recommendations were prioritized by impact, so the most critical usability issues were addressed first.
Issues paired with the recommendation each one produced
Constraints
As a financial advisor, I need to find the right accounts, clients, or groups quickly, but large result sets and unclear distinctions between entities make search overwhelming.
What we were working within
- •Searches could return up to 30,000 results in a single query.
- •Large result sets made filtering slow, and difficult to interpret.
- •Advisors were required to take multiple corrective steps just to narrow results.
- •Entity types were difficult to distinguish from one another.
The 30,000-result ceiling was a property of the underlying system, not something the redesign could remove. We validated every direction against it in feasibility discussions with engineering, so the design had to work with the limit rather than around it.
What hitting the ceiling looked like: an alert, and no way forward
Decisions
Structuring the search experience
We built search from the ground up, prioritizing what advisors need most when searching across accounts, clients, and relationship groups. The experience is layered, so the essentials work before anything advanced is introduced.
Each layer only works because the one beneath it does
Foundation
Entering search terms, retrieving relevant accounts, investors, clients, and groups, and understanding which entity type a result is.
Core
Result clarity, refinement against criteria, and completing the workflow the search was started for.
Advanced
Filtering, narrowing and adjusting, creating relationships and groups, and holding workflow context across all of it.
Ideation
Advisors don't need more options, they need clearer guidance toward the right result and next step.
I facilitated a cross-functional ideation workshop to explore how search could better support advisor intent, and evaluated concepts against user needs, system constraints, and downstream workflows.
Workshop output, grouped by the question each idea answered
Prototype
We redesigned the search experience to provide intuitive pathways across accounts, clients, and groups, so advisors can quickly find and distinguish the right entities. The prototype introduces a clearer result hierarchy, improved information architecture, and enhanced data visibility, making critical details easier to scan and evaluate at a glance.
What happens after the search
- •Defined a clear navigation path from search results to account, client, or group summary pages.
- •Ensured advisors could move from finding an entity to taking action without losing context.
Creating and managing client relationships from core search
This integration lets advisors:
- •Search for relevant accounts and clients.
- •Immediately create new client relationships or custom groups.
- •Organize information before calls without switching contexts, and prepare for meetings more efficiently.
Creating a client relationship without leaving search
Advanced Account Search
Alongside core search, I translated advisor workflows and business requirements into a structured advanced search experience — defining the criteria model, interaction patterns, and save and rewrite flows, and validating them through usability testing and feasibility work with engineering. That work is its own case study: Advanced Account Search.
The criteria model, with searches that can be saved and rerun
Trade-offs
Defaulting into Advanced Account Search past 30,000 results
The 30,000-result ceiling was real, and it wasn't moving this cycle. That left a choice about what should happen at the boundary. Rather than let advisors run a query, hit the limit, and work backwards from an answer the system couldn't complete, we default them into Advanced Account Search once a query crosses the threshold — where the criteria model is built for narrowing before results are retrieved.
What it bought
Advisors stop spending steps on a query that cannot resolve, and reach relevant results faster on the searches that were always going to need structure.
What it cost
A second surface to learn, and a handoff in the middle of a task — the advisor is moved somewhere they didn't choose to go.
We accepted that cost because the alternative was worse. Quietly truncating at 30,000 would leave advisors trusting a result set that isn't complete, and in this product a confidently wrong answer is more expensive than an extra step.
Outcomes
Advisors can now
- •Identify why a result appears without re-running searches.
- •Differentiate entity types (account, client, group) at a glance.
- •Take next actions faster, with fewer navigation steps.
Before
- ×Large result sets felt overwhelming
- ×Advisors repeatedly refined searches to find what they needed
- ×Search slowed down client interactions
After
- ✓Search supports clearer narrowing upfront
- ✓Results are easier to scan, even at scale
- ✓Critical context is surfaced upfront
- ✓Less time spent managing results, more time advising
Learnings
Research shared early changes what gets built. The experience assessment and two rounds of unmoderated study surfaced entity confusion and filtering friction before development started, so the fixes were design decisions rather than rework.
Filtering cannot rescue a weak search. Advisors were using filters to compensate for search that didn't capture their intent — which meant the fix belonged at the query, not after results had already loaded.
Advisors need to know what they're looking at, not just what matched. Entity type and reason-for-match are part of the result itself, not metadata to go hunting for.
Structure beats options. Laying the experience out as foundation, core, and advanced gave the team a shared vocabulary for what was essential and what could be deferred, which made scope conversations far shorter.
Scale is a design constraint, not a backend detail. A 30,000-result ceiling shapes what the interface has to say to the advisor as much as it shapes the infrastructure behind it.
Next Steps
Validate the guided flow at real data volumes, with particular attention to the handoff into Advanced Account Search at the 30,000-result threshold — the point where the design asks the most of the advisor.
Extend the entity-clarity patterns to the remaining lookups shared with other teams, so an account, client, or group reads the same way wherever it appears.
Close the loop on the prioritized recommendations — filter organization, search discoverability, and result relevance — and re-run the unmoderated study to measure how far each one moved.