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Search is the backbone of any helpdesk platform. Support agents rely on quick, accurate search to retrieve past conversations, emails, or tickets. The existing email client lacked an advanced search and filtering system that could handle complex queries, recognize multiple fields, and return relevant results efficiently. Prospects and users expected this functionality by default, similar to tools like Gmail or Outlook, creating a gap in user expectations and our current experience.
Project Details
Team Size :- Total ( 1 PM, 1 Senior PD, 1 Product Design Intern, 2 Engineers, 2 Devops)
Duration :- 3 months + Alongside other projects
My Role:- Product Design Intern
About Hiver
Hiver is an easy-to-use AI-powered customer service platform that helps teams manage all their customer communication—email, chat, phone, and WhatsApp—from a single inbox. It allows better collaboration, automates repetitive tasks, and provides helpful insights to improve support. Hiver makes delivering fast, personalized customer support simple and efficient right inside Gmail.
My Role:
I conducted market and competitor analysis to understand search patterns in tools like Gmail and Outlook, designed wireframes and interactive prototypes for the Advanced Search experience, and collaborated with the PM, senior designer, and engineers to refine flows, validate logic, and support implementation.
Problem Agents are Faced
Agents struggled to locate emails using basic keyword searches. The lack of field-specific filtering (e.g., search by “From,” “To,” “Tag,” “Status,” or “Assignee”) led to slower resolution times and frustration. They needed fine-grained control to find conversations efficiently, especially when handling high ticket volumes or cross-inbox assignments.
Pain Points:
No multi-parameter search capability (only keyword-based search)
Lack of prefix or partial matching in names, subjects, and email addresses
Search results were limited to exact matches, reducing discoverability
Inconsistent indexing of attachments or quoted texts
Missing sorting options (relevance or recency)
Inefficient navigation from search results back to conversations
No visibility indicators for personal vs shared inbox conversations
Business Goals:
Enable agents to search across multiple parameters intuitively
Reduce average time spent on searching and resolving tickets
Match user expectations aligned with standard email tools (Outlook, Gmail)
Improve discoverability and accuracy through prefix and tokenized matching
Increase product competitiveness by offering complete search parity with competitors
Build the foundation for future search enhancements like “Saved Filters” or “Folder-specific View Search”
Importance of the Project
Search is a critical engagement feature that defines the product’s efficiency. Introducing Advanced Search and Filters elevated the platform’s usability, improved customer satisfaction, and directly impacted SLA compliance metrics. It also closed a major gap cited during sales demos, serving as both a retention and acquisition lever.
How the Design Solves the Problem
The design introduced a modular search flow with two entry modes:
Quick commands such as “From:” or “Status:” for expert users
Intuitive dropdown-based Advanced Search for guided filtering
Each search parameter (From, To, Subject, Keywords, Status, Assignee, Tags) was built with validation rules and logical connectors (AND across parameters, OR within parameters). The results interface included:
Smart filter chips with “Clear All” control
Navigation state preservation (return to results after viewing a conversation)
Visual inbox chips (Personal or Shared) for context clarity
Relevance and recency sorting options (Most relevant, Most recent).
Prefix and token-based matching improved accuracy, while indexed attachment names made the system robust and familiar to agents switching from Gmail or Outlook.
Reflection and Lessons Learned
Designing search for enterprise-scale email required balancing cognitive simplicity and technical complexity. Key learning included:
Prioritizing “forgiveness” in user input (prefix/token matches) made search feel smarter.
Explicit search logic transparency (AND vs OR) prevented confusion during QA and onboarding.
Clear visual hierarchies between filters, results, and navigation improved discoverability.
Early alignment with engineers on data scope (archived vs shared mails) saved major rework.
