How Global Memory Works

Figr’s memory system is what makes product-aware design possible. Unlike other AI tools that forget everything between sessions, Figr builds a permanent understanding of your product, team, and users.

What Makes Figr’s Memory Different

Traditional AI Tools

Session-based memory:

Forgets everything when you close the tool
Requires re-explaining context every time
Can’t build on previous design decisions
Generic outputs that ignore your product reality

Result: Constant repetition and generic designs

Figr's Global Memory

Persistent product understanding:

Remembers context permanently across sessions
Builds cumulative knowledge over time
References past decisions and learnings
Creates increasingly accurate designs

Result: Designs that improve and feel more native over time

Memory Architecture

Product Knowledge Layer
Experience Layer
Decision Layer

What Figr permanently stores about your product:

Design System Knowledge

{
  "colorPalette": {
    "primary": "#6366f1",
    "semantic": {
      "success": "#10b981",
      "warning": "#f59e0b",
      "error": "#ef4444"
    }
  },
  "typography": {
    "headings": "Inter, sans-serif",
    "body": "Inter, sans-serif",
    "scale": "1.25 (Major Third)"
  },
  "components": {
    "button": "rounded corners, 12px padding",
    "cards": "subtle shadow, 8px radius",
    "forms": "outline style, error states below"
  }
}

User Context

{
  "primaryUsers": [
    {
      "type": "marketing_manager",
      "painPoints": ["time-consuming reporting", "data silos"],
      "goals": ["quick insights", "campaign optimization"],
      "techSavviness": "intermediate"
    }
  ],
  "workflows": [
    {
      "name": "campaign_creation",
      "steps": ["audience_selection", "content_creation", "scheduling"],
      "painPoints": ["audience_overlap_confusion"]
    }
  ]
}

Technical Constraints

{
  "platform": "web_responsive",
  "frameworks": ["React", "TypeScript"],
  "browsers": ["Chrome", "Safari", "Firefox"],
  "accessibility": "WCAG_2.1_AA",
  "performance": {
    "maxLoadTime": "2s",
    "imageOptimization": true
  }
}

How Figr remembers design reasoning:

Design Decision: "Use inline validation for forms"
Reasoning: "User research shows 23% better completion rates"
Context: "Applied to signup, checkout, and profile editing"
Results: "Reduced support tickets by 31%"

Future Application: Figr automatically suggests inline 
validation for new forms based on this proven success.

Pattern recognition:

What design solutions work for your specific users
Which interaction patterns reduce friction
How different user types prefer different approaches
When to break conventions vs. follow them

How Memory Builds Over Time

Initial Setup

Day 1: Foundation buildingWhen you first set up Figr:

Upload design system assets
Share your screen to show product
Provide user research and context
Define technical and business constraints

Memory strength: 60% - Good foundation

Early Usage

Weeks 1-2: Pattern recognitionAs you create designs and provide feedback:

Figr learns your design preferences
Understands which suggestions you accept/reject
Builds patterns from your specific use cases
Refines understanding of your user needs

Memory strength: 80% - Strong context awareness

Deep Integration

Month 1+: Institutional knowledgeWith continued use and team collaboration:

Comprehensive understanding of your product ecosystem
Nuanced grasp of edge cases and exceptions
Predictive insights about user needs
Anticipation of design requirements

Memory strength: 95% - Near-human product understanding

Memory in Action: Real Examples

SaaS Dashboard Design

Memory Context:

Product: B2B analytics platform
Users: Marketing managers (non-technical)
Key insight: Users need quick scanning, not deep analysis

Month 1 Design: Standard dashboard with detailed charts Month 6 Design: Scannable cards with key metrics highlighted, drill-down on demandWhy the difference: Figr learned that users prefer overview → detail pattern from usage analytics and user feedback

E-commerce Mobile Flow

Memory Context:

Product: Fashion e-commerce app
Users: Mobile-first, visual browsers
Key insight: Purchase decisions driven by social proof

Initial Design: Product page with standard description Evolved Design: Social proof prominent (reviews, photos), purchase path streamlinedLearning source: User session recordings showing how customers actually browse and buy

Enterprise Admin Panel

Memory Context:

Product: HR management system
Users: HR administrators (expert users)
Key insight: Efficiency over simplicity

Early Design: Simplified interface with guided workflows Mature Design: Information-dense layout with keyboard shortcuts and bulk actionsMemory evolution: Learned that expert users value speed over hand-holding

Managing Your Memory

Memory Dashboard
Adding Context
Memory Sharing

View and manage what Figr knows:

Figr memory dashboard showing stored knowledge categories and strength indicators

Memory categories:

Design System (95% complete)
User Research (78% complete)
Product Context (89% complete)
Technical Constraints (67% complete)
Brand Guidelines (92% complete)

Memory Security & Privacy

Data Ownership

You own all memory data:

Export complete memory snapshot anytime
Delete specific memory components
Transfer memory between accounts
Maintain control over sensitive information

Privacy Controls

Granular privacy settings:

Memory Component: Design System
Sharing: Team only

Memory Component: User Research
Sharing: Designers + PMs only

Memory Component: Technical Constraints  
Sharing: Full team access

Enterprise Features

For organizations with strict requirements:

On-premise memory storage
Custom retention policies
Audit logs for memory access
Integration with existing security systems

Troubleshooting Memory Issues

Memory Seems Incomplete

Symptoms:

Designs don’t match your product style
Missing key constraints or requirements
Generic outputs despite uploaded context

Solutions:

Check memory dashboard completeness
Upload additional reference materials
Provide more detailed product walkthroughs
Use feedback system to correct misunderstandings

Conflicting Information

Symptoms:

Inconsistent design suggestions
Figr seems confused about requirements
Mixing patterns from different contexts

Solutions:

Review uploaded materials for conflicts
Clarify which guidelines take priority
Update outdated context information
Use memory management to resolve conflicts

Learn About Memory Persistence

Discover how your assets and context are retained and managed across projects and time.Assets & Retention →

How to Begin?

Canvas

Inputs and Research

Product Intelligence

Export and Share

Security & Legal

How Global Memory Works

How Global Memory Works

What Makes Figr’s Memory Different

Traditional AI Tools

Figr's Global Memory

Memory Architecture

How Memory Builds Over Time

Memory in Action: Real Examples

Managing Your Memory

Upload New Assets

Provide Feedback

Memory Security & Privacy

Troubleshooting Memory Issues

Memory Seems Incomplete

Conflicting Information

Learn About Memory Persistence

​How Global Memory Works

​What Makes Figr’s Memory Different

Traditional AI Tools

Figr's Global Memory

​Memory Architecture

​How Memory Builds Over Time

​Memory in Action: Real Examples

​Managing Your Memory

Upload New Assets

Provide Feedback

​Memory Security & Privacy

​Troubleshooting Memory Issues

Memory Seems Incomplete

Conflicting Information

Learn About Memory Persistence

How Global Memory Works

What Makes Figr’s Memory Different

Memory Architecture

How Memory Builds Over Time

Memory in Action: Real Examples

Managing Your Memory

Memory Security & Privacy

Troubleshooting Memory Issues