Summary
Claira
Claira is an AI-powered ideation tool that transformed how teams innovate. It reduced information search time by 34%, cut data processing time by 29%, and increased insight utilization by 17%. The result: teams spent more time on actual innovation, less on information management.
Skills
User Research
UIUX Design
Prototyping
Roles
Product Designer
Collaborators
Product Manager
Engineer
Duration
6 months
Context
Why do innovation teams struggle to leverage powerful AI tools safely?
Innovation teams need breakthrough insights that come from connecting internal company data with external research, but security policies prevent them from using powerful AI tools with proprietary information. This creates a fundamental tension between staying secure and accessing the most advanced analytical capabilities.
News screenshots about the dilemma
Problem
Research efficiency drops when information becomes fragmented.
Enterprise researchers spend 28% of their time hunting for information instead of actually innovating with it.
Fragmented Information
Critical insights scattered across internal databases, external websites, and research papers with no unified access
Security Barriers
Teams cannot safely use external AI tools with internal company data, limiting analytical capabilities
Manual Research Burden
Hours wasted manually copying, converting, and organizing data before analysis can even begin
Connection Blindness
Difficulty seeing relationships between different pieces of information leads to missed insights and opportunities
User interview
What we discovered by watching innovation teams work
In the flow of chatting with AI, sharing sensitive details feels natural and harmless.
Observations
Spends 2+ hours on data gathering
Juggles 20+ browser tabs daily
Loses track of information sources
Manually downloads/converts files
Can't use AI with company data
Quotes
“I spend more time managing information than thinking about it”
“Half my research lives in my browser, the other half in three different systems”
“By the time I organize everything, I've lost my train of thought”
“I end up doing the same search twice because I forgot where I saved something”
“We have amazing internal data but can't safely analyze it with the best tools”
“I know there are connections I'm missing, but I don't know how to find them”
“Search results don't show me the bigger picture”
"I bookmark things and then never find them again"
We discovered that researchers rely on linear search patterns (keywords → list → filter) that completely break down when they need to integrate trusted external sources with confidential company data, forcing them into chaotic workflows of juggling multiple tabs and losing track of information sources.
Design Principles
Bridging familiar search patterns with network thinking
Design decisions that help users transition from linear search to relationship discovery without losing their cognitive comfort zone.
Key Functions
Visual Relationship Discovery for Secure Enterprise Research
Based on the design principles, we came up with four key functions.
Flexible Source Selection
Choose any combination of internal systems (Drive, Slack) and external sources in a single query
Rapid AutoFetch
Parallel processing automatically gathers related information from multiple sources simultaneously
Thematic Insight Cards
Transform scattered research into organized topic themes that visually reveal how ideas connect
Visual Security Controls
Color-coded indicators (red/orange/green) make data sensitivity instantly recognizable during exploration
Workflow Pain Points
How Claira Should Transform User Experience
Hover to Zoom In
Researchers cycle through 8+ disconnected steps—visiting websites, downloading files, converting formats, uploading to systems, writing prompts, and iterating. This fragmented process consumes more time managing information than analyzing it.
Four connected steps replace the manual cycle: enter topic → select sources → review insights → create report. By handling source complexity behind the scenes, researchers focus on discovering patterns instead of juggling files.
Ideation & Concept Development
Exploring Relationship Visualization Patterns
I explored various formats for organizing insight cards and visualizing their relationships—testing radial graphs, linear lists with branches, and hierarchical trees. The breakthrough came from combining familiar vertical stacking with horizontal connection lines, allowing users to maintain their natural reading flow while discovering relationships between topics that would otherwise remain hidden.
Wireframe sketches
Solution
A unified platform that reveals hidden connections across secure data sources
Claira securely connects internal and external data sources, processes them in parallel to generate hierarchical topic clusters, and outputs structured insight cards with clear source attribution.
Internal Data
External Data
LLM:
Security: Access Control, Secure Processing, and Compliance
Thematic Insight Generator
Hierarchical Topic Clustering
Cross-Source Relationship Mapping
Progressive Insight Summarization
AutoFetch Engine
Semantic Connection
Parallel Retrieval
Source Attribution Classifier
Insight Cards
Source Map
Flexible Source Selection
Users choose any combination of internal systems and external platforms in a single search, eliminating the need to query multiple tools separately.
Parallel Information Gathering
Easily define your own detection patterns with regex, example data, and optional replacement text: no coding expertise required.
Hierarchical Relationship View with Security Labels
Topics stack vertically with lines connecting related information. Each connection shows a color label—red for confidential, orange for restricted, green for public—so researchers always know what's safe to share while exploring connections.
Reflection
Impact Metrics
These numbers represent more than efficiency gains—researchers discovered connections they previously missed and spent more time on actual innovation rather than information management.
34%
Reduction in time spent
searching for information.
29%
Decrease in time
needed to process
and understand data.
17%
Higher utilization rate of
insights in final reports.