Sasha Product: High-Level Design Architecture

Status: Draft
Generated: 2025-08-01 11:30 UTC (Updated: 2025-08-01 15:45 UTC)
Purpose: Technical product design for Sasha AI knowledge management platform
Source: Design discussion transcript from 2025-08-01 LS-RM call + WRU analysis session
Target Users: Product development team, technical stakeholders, implementation partners

Product Vision

Transform Claude Code from a developer tool into an enterprise-ready AI knowledge management platform that democratizes decades of institutional intelligence.

Core Value Proposition

Sasha is an AI-powered knowledge management system that converts years of organizational documentation into an intelligent, conversational interface. Unlike traditional document management systems, Sasha doesn't just store information—it understands context, applies business logic, and provides expert-level analysis based on accumulated institutional knowledge.

The "Apple Mac vs Unix" Philosophy: Provide the full functionality of command-line Claude Code with the simplicity of a consumer-friendly interface that business users can adopt without technical training.

System Architecture Overview

Technical Architecture

Core Technology Stack

Foundation Technologies:

• Claude Code: LLM interface and file system integration
• Visual Studio Code: Development environment (open source base)
• Markdown: Native document format for AI processing
• Git: Version control and change tracking
• Node.js: Runtime environment for extensions

Proposed Customizations:

• Simplified UI: Custom VS Code interface removing development-specific features
• File System Integration: Direct local file access without cloud dependencies
• Publishing Pipeline: Automated website generation from markdown
• Security Layer: Enterprise compliance and audit capabilities

User Interface Layers

1. Business User Interface (Target Product)

Characteristics:

• Slack-like chat interface for natural language interaction
• File browser integrated into conversation context
• Preview mode for document visualization
• Publish button for one-click website generation
• Drag-and-drop document upload and organization

Key Features:

• No command-line exposure
• Contextual help and guided workflows
• Role-based interface customization
• Mobile-responsive design

2. Power User Interface (Current State)

Characteristics:

• Visual Studio Code with Claude Code integration
• Full file system access and developer tools
• Command palette for advanced operations
• Extension ecosystem for specialized workflows

Transition Strategy:

• Maintain full VS Code functionality for technical users
• Add business-friendly shortcuts and wizards
• Provide guided workflows for common business operations

3. Command Line Interface (Developer/Admin)

Characteristics:

• Direct Claude Code CLI access
• Automation scripting capabilities
• System administration and maintenance
• Advanced configuration and troubleshooting

Intelligence Architecture

Guidelines & Policies System

Core Concept: Replace generic AI responses with organization-specific, context-aware intelligence through comprehensive instruction libraries.

The "Secret Sauce" - Proprietary Guidelines Architecture

From transcript: "That's the secret sauce is all that customizations context, the beautify all of that stuff. so they can, they can use it but cant take it."

Implementation Structure:

/docs/private/guides/
├── knowledge-management/
│   ├── beautiful-documentation-design-guide.md
│   ├── transcription-file-naming-guide.md
│   ├── web-publishing-guide.md
│   └── collaborative-file-sharing-guide.md
├── development/
│   ├── coding-standards.md
│   ├── testing-framework-guide.md
│   └── deployment-pipeline-methodology.md
├── business/
│   ├── case-study-creation-guide.md
│   ├── risk-assessment-framework.md
│   ├── vendor-analysis-methodology.md
│   └── financial-analysis-framework.md
├── visualization/
│   ├── chart-creation-guide.md
│   ├── dashboard-design-patterns.md
│   └── data-presentation-standards.md

Keyword-Triggered Intelligence System

From transcript: "All of those words. Actually means something... Beautiful publish analyze. Organize. Research study"

Enhanced Operational Model:

• "beautify" → Apply professional formatting, design standards, mobile-responsive layouts
• "publish" → Security verification, builds website, deploys with safeguards, authentication checks
• "analyze" → Create metadata summary, convert to markdown, apply domain-specific business context
• "organize" → Apply file naming conventions, directory structures, cross-reference linking
• "research" → Execute multi-phase investigation, compile sources, generate executive summaries
• "study" → Create case study format, apply lessons learned framework, generate actionable insights

Subscription-Based Access Model

From transcript: "Because I've paid for a subscription, I'm allowed to go and get the Sasha. Knowledge management. Context Guidelines."

The guidelines represent the core intellectual property - organizations pay for access to years of refined methodologies and proven frameworks, not just the software interface.

Specialized Agent System

Role-Based Intelligence Modules:

• Digital Intelligence Researcher: Web research and competitive analysis
• Financial Business Analyst: Spreadsheet analysis and financial modeling
• Code Review Security: Technical documentation and security assessment
• Product Strategy Analyst: Market analysis and product planning
• Project Risk Planner: Risk assessment and project planning

Agent Characteristics:

• Each agent has specialized knowledge base and methodologies
• Automatic role selection based on task context
• Cross-agent collaboration for complex analyses
• Consistent output formatting and quality standards

Advanced Visualization Engine

Real-World Performance Validation

From transcript: "Oh my God, look at this. Oh my God... If it's right, it's phenomenal."

Demonstrated Capabilities:

• Chart.js Integration: Professional, animated financial visualizations
• Automated Dashboard Creation: From spreadsheet data to interactive charts in minutes
• Multi-format Export: HTML embedded charts, markdown integration, PDF reports
• Complex Data Analysis: 200+ data point line graphs with supplier comparisons

Speed-to-Insight Metrics:

• Traditional analysis: "This is what I want to spend the next two weeks, putting it together"
• Sasha delivery: "Five minutes later. Yeah, he said, right?"
• Time Compression: 2000x faster delivery (2 weeks → 5 minutes)

Technical Implementation:

Chart.js + HTML iframes → Markdown embedding → Website publishing

• Professional Quality: "The computers are taken in every data visualization that's ever been produced on the Internet and figured out what's good"
• Automated Design: Selects optimal chart types (scatter, waterfall, line graphs)
• Enterprise Standards: Consistent branding, professional color schemes

Business Impact:

• Consultancy-Grade Output: "40 grand. Up consultancies to do tools at you, which require loads of training"
• Instant Expertise: Complex financial analysis without specialized software training
• Client Presentation Ready: "He will bloody... fall off his chair"

Knowledge Management System

File Organization Philosophy

Direct File System Access: Unlike cloud-based solutions, Sasha operates directly on local/network file systems without requiring data migration or cloud upload.

Structured Knowledge Architecture:

/project-root/
├── docs/                          # User documentation
│   ├── case-studies/              # Business cases and examples
│   ├── analysis/                  # Research and analytical documents
│   └── guides/                    # Process documentation
├── recordings/                    # Meeting transcripts and audio
├── private/                       # Internal guidelines and policies
│   ├── guides/                    # Process instructions
│   ├── policies/                  # Business rules and standards
│   └── prompts/                   # AI instruction templates
└── data/                          # Spreadsheets, raw data, exports

Knowledge Accumulation Process

Progressive Intelligence Building:

1. Document Ingestion: Sasha reads and processes existing documentation
2. Context Building: Creates markdown summaries and cross-references
3. Pattern Recognition: Identifies recurring themes and methodologies
4. Knowledge Application: Applies learned patterns to new requests
5. Continuous Learning: Documents new insights and approaches

Multi-Phase Analysis Methodology

Validated 27-Step Process Framework

From transcript: "Application Builder... is these plans? This is 27 of these plans stitched together in sequence, that's all it is."

Demonstrated Multi-Phase Approach:

1. Update Analysis Plan - Review and refine methodology based on new requirements
2. Execute Analysis - Apply structured analytical framework
3. Generate Documentation - Create markdown reports with embedded visualizations
4. Publish Results - Deploy analysis to web with authentication controls
5. Create Guidelines - Document methodology for future reuse

Proven Implementation Pattern:

Phase 1: Data Ingestion & Validation
Phase 2: Structured Analysis Execution  
Phase 3: Visualization Generation
Phase 4: Report Compilation
Phase 5: Stakeholder Publication
Phase N: Methodology Refinement

Self-Learning Architecture:

From transcript: "After this it's done. I'm going to say update the prompt with what we've learned. In this process."

• Each analysis improves the methodology
• Guidelines automatically updated with lessons learned
• Pattern recognition across similar engagements
• Continuous refinement of analytical frameworks

Example Workflow:

User: "Analyze these vendor proposals"
Sasha: 1. Creates vendor-analysis-v3-YYYY-MM-DD.md
       2. Applies procurement methodology from guidelines
       3. Generates comparative financial visualizations
       4. Cross-references similar past analyses
       5. Builds on institutional knowledge of vendor patterns
       6. Updates methodology guide with new insights

Collaboration Architecture

Multi-User File System Sharing

Challenge: Enable multiple remote users to work simultaneously on the same knowledge base without conflicts.

Validated Solution: Google Drive File Stream

From transcript analysis: "We will just operate in the shared drive on this projects" and "Google Drive File Stream would be perfect"

Implementation Strategy:

1. Google Drive File Stream as primary collaboration method

   • Mounts shared drives as local file systems
   • Real-time synchronization without conflicts
   • Enterprise security and access controls
   • Cross-platform compatibility (macOS, Windows, Linux)

2. Project Structure on Shared Drive:

/Google Drive File Stream/Shared drives/ClientProject/
├── customer-data/          # Read-only client documents
├── sasha-workspace/        # AI analysis and outputs
├── guidelines/             # Project-specific instructions
└── collaboration/          # Team coordination files

Security Architecture from Transcript:

"We would ask for access to the corporate shared drive. Read, only access."

• Client Data: Read-only access to corporate knowledge
• Sasha Workspace: Read-write access for analysis outputs
• Separation of Concerns: Clear boundaries between client IP and generated analysis

Operational Benefits:

• 10x Collaboration Speed: "This is just going to be such a 10x because it'll be two of us going"
• Instant Access: No data migration or setup complexity
• Enterprise Ready: Built-in backup, versioning, and compliance features

Security & Compliance

Enterprise Requirements:

• Data Encryption: At rest and in transit
• Access Control: Role-based permissions and audit trails
• Compliance: GDPR, HIPAA, SOX compliance as required
• Secure Deployment: On-premises and private cloud options
• No External Dependencies: Optional air-gapped deployment

Authentication & Identity Management

From transcript: "We need to absolutely know who's logged in" and "In enterprising world, we would plug into the identity provider of the Customer"

Enhanced Security Architecture:

1. Identity Provider Integration

   • Active Directory / LDAP integration
   • Single Sign-On (SSO) capabilities
   • Role-based access to guidelines and private content

2. Multi-tier Access Control:

   Corporate User → AD Authentication → Role Assignment → Context Filtering
   

   • Public Context: General business methodologies
   • Private Context: Proprietary guidelines and sensitive analysis
   • Client-Specific: Project-restricted access

3. Audit Trail Requirements:

   • Track all file access and modifications
   • Log AI interaction sessions with user attribution
   • Maintain compliance records for regulatory review

Claude Code Security Features:

• Disable Logging: Turn off Anthropic data collection
• Local Processing: Keep sensitive data on premises
• Secure Configuration: Enterprise security settings at startup

Business Model & Pricing

Target Market Segmentation

Validated Pricing Model from Real Client Engagement

From transcript: "Three, four grand. Let's look at this. Does it go somewhere? Great. Let's make a bigger piece of work now."

1. Proof of Concept Engagement

• Product: Sasha POC (2-week demonstration)
• Price Point: $3,000-6,000 fixed price
• Value Prop: "We are the reason that he passed his probation" - career-defining impact
• Success Metric: Client retention and upsell to full engagement

2. Professional Services Teams (2-15 people)

• Product: Sasha Professional (Subscription + Guidelines)
• Price Point: $997-2,997/month + setup
• Value Prop: "Two hours of Sasha time but it's actually four weeks worth of effort" - 10-80x time compression
• Target: Consultancies, strategy teams, business analysts

3. Enterprise Organizations (15+ people)

• Product: Sasha Enterprise (Custom Integration)
• Price Point: $5,000-15,000/month + implementation
• Value Prop: "Someone will need to understand this stuff" - knowledge transfer and capability building
• Features: Identity provider integration, private guidelines, dedicated support

Value-Based Pricing Justification:

• Traditional Consulting: $40,000+ for complex analysis ("40 grand. Up consultancies to do tools")
• Sasha Delivery: Same quality in hours vs weeks
• ROI: 70-90% cost savings with higher quality and consistency

Revenue Streams

Primary Revenue:

• Software Licensing: Per-user subscription model
• Implementation Services: Setup, customization, training
• Ongoing Support: Maintenance, updates, consultation

Secondary Revenue:

• Specialized Guidelines: Industry-specific instruction libraries
• Custom Agent Development: Bespoke AI capabilities
• Integration Services: Connect with existing enterprise systems

Implementation Roadmap

Phase 1: MVP Development (3-4 months)

Core Features:

• Simplified VS Code interface with business-friendly UI
• Guidelines & policies instruction system
• Basic file system sharing (cloud storage)
• Publishing pipeline integration
• Security configuration options

Success Criteria:

• Non-technical users can create case studies and analysis documents
• Publishing works with single command
• Multi-user collaboration functional

Phase 2: Enterprise Features (6-8 months)

Advanced Capabilities:

• Custom real-time collaboration platform
• Advanced security and compliance features
• Role-based access control and audit trails
• Integration with enterprise identity systems
• Advanced analytics and usage monitoring

Success Criteria:

• Enterprise security compliance validated
• Scalable to 50+ concurrent users
• Customer success case studies completed

Phase 3: Market Expansion (12+ months)

Growth Features:

• Industry-specific instruction libraries
• Marketplace for custom agents and guidelines
• API ecosystem for third-party integrations
• Mobile applications for remote access
• Advanced AI capabilities and automation

Success Criteria:

• Market leadership in AI knowledge management
• Sustainable revenue growth and profitability
• Strong partner ecosystem established

Key Success Factors

Product Differentiation

Unique Value Propositions:

1. No Data Migration: Works with existing file systems and structures
2. Institutional Memory: Builds on decades of organizational knowledge
3. Context-Aware Intelligence: AI that understands your specific business
4. Enterprise Security: Full control over data and deployment
5. Proven Methodologies: Built-in best practices from successful consulting

Competitive Advantages

vs. Traditional Document Management:

• AI-powered analysis and insight generation
• Conversational interface vs. search-based retrieval
• Contextual understanding vs. keyword matching

vs. Generic AI Tools:

• Organization-specific knowledge and context
• Proven business methodologies built-in
• Enterprise security and compliance
• No external data sharing or cloud dependencies

vs. Custom AI Solutions:

• Rapid deployment (weeks vs. years)
• Proven framework and methodologies
• Ongoing updates and improvements
• Cost-effective compared to custom development

Risk Assessment & Mitigation

Technical Risks

Business Risks

Next Steps & Dependencies

Immediate Actions (Next 30 days)

1. Market Validation: Interview 10+ potential customers about requirements
2. Technical Proof of Concept: Build simplified UI prototype
3. Security Assessment: Evaluate enterprise security requirements
4. Partnership Strategy: Identify potential implementation partners

Medium-term Objectives (3-6 months)

1. MVP Development: Complete core feature development
2. Beta Testing: Launch with 3-5 pilot customers
3. Go-to-Market Strategy: Develop sales and marketing approach
4. Team Building: Hire key technical and business personnel

Long-term Vision (12+ months)

1. Market Leadership: Establish dominant position in AI knowledge management
2. Platform Ecosystem: Build thriving partner and developer community
3. Global Expansion: Scale to international markets and enterprise clients
4. Continuous Innovation: Maintain technology leadership through R&D investment

Conclusion: The Future of Institutional Intelligence

Sasha represents a fundamental shift from traditional document management to intelligent knowledge partnerships. By combining the power of Claude Code with enterprise-ready user experience and proven business methodologies, Sasha transforms decades of institutional knowledge into a competitive advantage.

The Vision: Every organization should have access to an AI colleague that remembers everything, applies proven methodologies, and never forgets a detail. Sasha makes this vision reality through:

• Immediate Value: Works with existing documentation and processes
• Enterprise Ready: Security, compliance, and scalability built-in
• Continuous Learning: Gets smarter with every interaction
• Human-Centric: Enhances human expertise rather than replacing it

Success will be measured not just in revenue, but in the transformation of how organizations leverage their accumulated wisdom to make better decisions, faster.

📞 Contact & Development

Product Lead: Lindsay Smith
Technical Architecture: Based on Claude Code and VS Code ecosystem
Market Focus: Enterprise knowledge management and business intelligence
Development Status: Concept validated, technical proof-of-concept in progress

This design document serves as the foundation for transforming Sasha from a technical proof-of-concept into a market-ready enterprise AI knowledge management platform.