Sasha Studio Product Requirements Document (PRD)
Generated: 2025-01-03 UTC
Version: 2.1
Last Updated: 2025-08-06 UTC
Purpose: Comprehensive product specification for Sasha Studio AI Knowledge Management Platform
Audience: Product team, developers, stakeholders, investors
🎆 Recent Updates
- January 2025: TinyLlama 1.1B integrated as automatic fallback model
- January 2025: Conversation history with real-time streaming implemented
- August 2025: Local LLM models fully integrated (Llama 3.2, Phi-3, TinyLlama)
- Status: Production-ready with multi-model support operational
Executive Overview
Sasha Studio transforms how organizations interact with their institutional knowledge by providing a business-friendly, web-based AI assistant that makes decades of expertise instantly accessible through natural conversation, powered by LLxprt's unified multi-model capabilities.
Product Vision
Create the "Apple Mac of AI Knowledge Management" - a beautifully simple interface that hides powerful complexity, making AI-powered knowledge work accessible to every team member regardless of technical expertise.
Core Value Propositions
Instant Expertise Access
Transform 10+ years of organizational knowledge into conversational AI in minutes, not months
Enterprise Security
Choose between cloud AI, on-premise models, or hybrid deployments based on data sensitivity
Cost Optimization
Reduce AI costs by 70% through intelligent model routing and local TinyLlama fallback ( Implemented)
One-Click Publishing
Transform AI insights into beautiful, shareable websites instantly with doc-builder integration
Product Goals & Success Metrics
Primary Goals
- Democratize AI Access: Enable non-technical users to leverage advanced AI capabilities
- Preserve Institutional Knowledge: Capture and make searchable decades of organizational expertise
- Optimize AI Costs: Reduce operational expenses through intelligent model selection
- Ensure Data Security: Provide enterprise-grade security with flexible deployment options
- Accelerate Decision Making: Reduce research time from days to minutes
Success Metrics
| Metric | Target | Measurement Method |
|---|---|---|
| Time to First Value | < 5 minutes | Onboarding completion tracking |
| User Adoption Rate | > 80% in 30 days | Active user analytics |
| Task Completion Rate | > 90% | Session success tracking |
| Cost Reduction | 60-80% vs alternatives | Model usage analytics |
| User Satisfaction | > 4.5/5 stars | In-app feedback system |
| System Uptime | > 99.9% | Monitoring dashboards |
| Knowledge Retrieval Accuracy | > 95% | Quality assurance metrics |
User Personas
Primary Personas
1. Knowledge Worker - "Sarah"
- Role: Marketing Manager, Consultant, Analyst
- Tech Level: Intermediate (uses Office, Slack, basic tools)
- Pain Points:
- Spends hours searching for past project information
- Struggles to find specific methodologies or templates
- Needs to quickly create client-ready presentations
- Goals:
- Find relevant past work instantly
- Generate insights from historical data
- Create professional deliverables quickly
2. Executive Decision Maker - "Michael"
- Role: CEO, VP, Department Head
- Tech Level: Basic (delegates technical tasks)
- Pain Points:
- Needs quick answers for strategic decisions
- Wants insights without technical complexity
- Concerned about data security and compliance
- Goals:
- Get executive summaries instantly
- Make data-driven decisions confidently
- Ensure organizational IP is protected
3. IT Administrator - "David"
- Role: IT Manager, Security Officer
- Tech Level: Advanced
- Pain Points:
- Managing multiple AI tool subscriptions
- Ensuring data doesn't leave the organization
- Monitoring usage and controlling costs
- Goals:
- Centralize AI capabilities
- Maintain security compliance
- Control and optimize AI spending
Secondary Personas
- New Employee - Needs rapid onboarding and context
- External Consultant - Requires controlled, temporary access
- Compliance Officer - Needs audit trails and data governance
Core Features & Requirements
1. Web-Based AI Chat Interface
Functional Requirements
| Feature | Description | Priority | Release | Status |
|---|---|---|---|---|
| Natural Language Chat | Conversational interface supporting context-aware dialogue | P0 | MVP | 100% |
| Real-time Streaming | Token-by-token response streaming for immediate feedback | P0 | MVP | 100% |
| Multi-turn Conversations | Maintain context across entire session | P0 | MVP | 100% |
| Conversation History | Persistent chat history with auto-generated titles and icons | P0 | MVP | 100% |
| Welcome Message | One-time Sasha introduction with capabilities | P0 | MVP | 100% |
| File Upload & Analysis | Drag-drop support for PDFs, Excel, Word, images | P0 | MVP | 40% |
| Code Generation | Generate and explain code with syntax highlighting | P1 | MVP | 75% |
| Voice Input | Speech-to-text for hands-free operation | P2 | v1.1 | 20% |
| Multi-language Support | Interface localization for global teams | P2 | v1.2 | 0% |
Technical Requirements
- WebSocket support for real-time streaming
- Maximum file size: 50MB per upload, 500MB per session
- Support for 25+ file formats
- Response time: < 100ms to first token
- Concurrent sessions: 1000+ users
User Experience Requirements
- Single-click to start new conversation
- Auto-save all conversations
- Keyboard shortcuts for power users
- Mobile-responsive design
- Dark mode support
2. LLxprt Multi-Model Integration
Functional Requirements
| Feature | Description | Priority | Release | Status |
|---|---|---|---|---|
| Model Selection | Choose between Claude, GPT, Llama models | P0 | MVP | 100% |
| Automatic Routing | Smart selection based on task type and cost | P0 | MVP | 60% |
| Local Model Support | Run Llama models on-premise for security | P0 | MVP | 100% |
| Cost Tracking | Real-time cost monitoring per model/user | P1 | MVP | 70% |
| Custom Models | Support for fine-tuned or specialized models | P2 | v1.1 | 0% |
| Model Comparison | Side-by-side response comparison | P2 | v1.2 | 0% |
Model Routing Rules
routing_strategy:
sensitive_data:
primary: local_llama_70b
fallback: tinyllama_1.1b # ✅ Implemented
complex_analysis:
primary: claude_3_opus
fallback: gpt_4_turbo
simple_queries:
primary: claude_3_haiku
fallback: gpt_3.5_turbo
cost_sensitive:
primary: tinyllama_1.1b # ✅ Implemented - $0 cost
fallback: claude_3_haiku
3. Intelligent Guide Management
Functional Requirements
| Feature | Description | Priority | Release | Status |
|---|---|---|---|---|
| Guide Library | Centralized repository of methodologies | P0 | MVP | 70% |
| Context Detection | Auto-select relevant guides based on query | P0 | MVP | 40% |
| Guide Editor | Create/edit guides with preview | P1 | MVP | 0% |
| Version Control | Track guide changes and history | P1 | v1.1 | 0% |
| Guide Analytics | Usage tracking and effectiveness metrics | P2 | v1.1 | 0% |
| Community Guides | Share guides across organizations | P3 | v1.2 |
Guide Categories
- Knowledge Management: Documentation, organization, archival
- Research Methodologies: Market analysis, competitor research
- Development Standards: Coding practices, architectural patterns
- Business Processes: Workflows, procedures, best practices
- Compliance Frameworks: Industry-specific requirements
4. One-Click Publishing
Functional Requirements
| Feature | Description | Priority | Release | Status |
|---|---|---|---|---|
| Instant Website | Convert chat to professional website | P0 | MVP | 50% |
| Custom Domains | Deploy to organization's domain | P1 | MVP | 30% |
| Access Control | Password protection, email whitelist | P1 | MVP | 0% |
| Theme Selection | Multiple professional themes | P2 | v1.1 | 0% |
| Analytics Integration | Google Analytics, visitor tracking | P2 | v1.1 | 0% |
| PDF Export | Generate downloadable reports | P1 | MVP |
Publishing Workflow
graph LR
A[Select Content] --> B[Choose Template]
B --> C[Preview Site]
C --> D[Configure Access]
D --> E[Deploy]
E --> F[Share URL]
style A fill:#e3f2fd
style C fill:#f3e5f5
style E fill:#e8f5e9
5. Enterprise Security
Functional Requirements
| Feature | Description | Priority | Release | Status |
|---|---|---|---|---|
| SSO Integration | SAML, OAuth, Active Directory | P0 | MVP | 0% |
| Role-Based Access | Granular permissions system | P0 | MVP | 25% |
| Data Encryption | At-rest and in-transit encryption | P0 | MVP | 100% |
| Audit Logging | Complete activity tracking | P0 | MVP | 60% |
| IP Whitelisting | Network access restrictions | P1 | MVP | 0% |
| 2FA Support | Multi-factor authentication | P1 | MVP | |
| Data Retention | Configurable retention policies | P1 | v1.1 |
Compliance Requirements
- GDPR compliant with data portability
- HIPAA ready with BAA available
- SOC 2 Type II certification planned
- ISO 27001 security standards
6. Operational Analytics
Functional Requirements
| Feature | Description | Priority | Release | Status |
|---|---|---|---|---|
| Real-time Dashboards | Live system monitoring | P0 | MVP | 45% |
| Usage Analytics | User activity and patterns | P1 | MVP | 45% |
| Cost Analytics | AI spending by team/project | P1 | MVP | 50% |
| Performance Metrics | Response times, success rates | P1 | MVP | 50% |
| Custom Reports | Scheduled email reports | P2 | v1.1 | 0% |
| SIEM Integration | Export to Splunk, Elastic | P1 | MVP | 0% |
Dashboard Components
- Executive Overview: Active users, total sessions, cost trends
- Operational Health: System status, mount health, sync operations
- User Analytics: Top users, popular queries, session patterns
- Knowledge Metrics: Most accessed guides, document usage
- Security Monitor: Failed logins, suspicious activity, access patterns
7. User Experience Features
Functional Requirements
| Feature | Description | Priority | Release | Status |
|---|---|---|---|---|
| Onboarding Tour | Interactive first-use guide | P0 | MVP | 0% |
| Contextual Help | Inline tooltips and guidance | P1 | MVP | 20% |
| Keyboard Shortcuts | Power user productivity | P1 | MVP | 40% |
| Custom Themes | Personalization options | P2 | v1.1 | 15% |
| Workspace Switching | Multiple project contexts | P1 | MVP | 0% |
| Collaboration | Share sessions with team | P2 | v1.1 | 0% |
Implementation Progress Summary
Overall Status: 55% Complete
| Component | Progress | Status |
|---|---|---|
| Chat Interface | 90% | Core functionality complete with conversation history, file upload in progress |
| AI Integration | 85% | Full local LLM support with Ollama, multi-model routing operational |
| Guide System | 55% | Library built, context detection in development |
| Publishing | 40% | Basic publishing works, advanced features pending |
| Security | 35% | Encryption done, SSO and RBAC in planning |
| Analytics | 40% | Basic metrics working, dashboards in development |
| UX Features | 25% | Basic UI complete, advanced features planned |
| Database | 100% | SQLite integration complete with message persistence |
Already Implemented in WebSocket Prototype:
- Real-time WebSocket communication with streaming
- Multi-provider AI support (OpenRouter, OpenAI, Anthropic, Google)
- Conversation persistence with SQLite database
- Message history with full metadata tracking
- Full-text search functionality with FTS5
- Session management and metrics tracking
- ULID-based message IDs for sortability
- Token usage and cost calculation
- Database analytics and statistics
- Persona selection system
- Basic guide templates
- Token-by-token streaming responses
- Error handling and auto-reconnection
- Model switching during conversation
- Cost tracking foundation
- Security headers and CORS configuration
- One-time welcome message with deduplication
- Dynamic model status display
- Line spacing optimizations for readability
Currently In Development:
- File upload and processing
- Advanced guide context detection
- Web publishing integration
- Analytics dashboards
- Advanced routing algorithms
- Tool system architecture (research phase)
Next Phase (Docker Deployment):
- Containerization of all services
- Docker Compose orchestration
- Environment-specific configurations
- Volume management for persistence
- Production-ready deployment scripts
Technical Architecture
Local LLM Implementation
Full Local Model Support (August 2025)
Available Models via Ollama:
- Llama 3.2 (3B): Best quality for complex tasks, 4-6 second response time
- Phi-3 Mini (3.8B): Microsoft's efficient model, good for technical content
- TinyLlama 1.1B: Ultra-fast responses, 50-60 tokens/sec with 4-bit quantization (637MB)
- Gemma 2 (2B): Google's efficient model, balanced performance
Key Features:
- Automatic Fallback: When cloud providers unavailable, seamlessly switches to local models
- Zero Cost Operation: All queries handled locally when configured ($0 API costs)
- Smart Routing: Automatically selects appropriate model based on query complexity
- Docker Ready: Single container deployment with embedded Ollama service
- Conversation History: Full conversation persistence with automatic title generation
- Real-time Streaming: Token-by-token streaming for responsive user experience
- Node.js Compatible: Fixed streaming issues for seamless WebSocket integration
System Components
graph TB
subgraph "Frontend Layer"
A[React SPA]
B[WebSocket Client]
C[File Upload Manager]
end
subgraph "API Layer"
D[Express API Server]
E[Authentication Service]
F[WebSocket Server]
end
subgraph "AI Layer"
G[LLxprt CLI]
H[Model Router]
I[Guide Engine]
P[TinyLlama Fallback ]
Q[Ollama Service ]
end
subgraph "Data Layer"
J[SQLite/PostgreSQL]
K[Redis Cache]
L[File Storage]
end
subgraph "Infrastructure"
M[Docker Container]
N[Nginx Proxy]
O[Monitoring Stack]
end
A --> D
B --> F
D --> G
G --> H
H --> P
P --> Q
D --> I
D --> J
D --> K
style A fill:#e3f2fd
style G fill:#f3e5f5
style J fill:#e8f5e9
style M fill:#fff3e0
Technology Stack
| Component | Technology | Justification |
|---|---|---|
| Frontend | React 18 + TypeScript | Modern, type-safe development |
| UI Library | shadcn/ui | Customizable, accessible components |
| Backend | Node.js + Express | JavaScript ecosystem consistency |
| AI Interface | LLxprt CLI | Unified multi-model support |
| Database | SQLite (MVP) / PostgreSQL (Scale) | Zero-config start, scalable future |
| Message IDs | ULID | Sortable, globally unique identifiers |
| Search | SQLite FTS5 | Full-text search capability |
| Cache | Redis 7 | High-performance caching |
| Container | Docker | Simplified deployment |
| Monitoring | Prometheus + Grafana | Industry-standard observability |
Performance Requirements
- Response Time: < 100ms to first token
- Throughput: 10,000+ requests/minute
- Concurrent Users: 1,000+ simultaneous
- Availability: 99.9% uptime SLA
- Data Processing: 1GB files in < 30 seconds
Scalability Considerations
- Horizontal scaling via container orchestration
- Database read replicas for query performance
- CDN integration for static assets
- Queue-based processing for heavy operations
- Auto-scaling based on load metrics
🚦 Implementation Roadmap
Phase 1: MVP (8 weeks)
Goal: Launch core chat interface with multi-model support
Features:
- Web-based chat interface
- LLxprt integration (Claude, GPT, Llama)
- SQLite database with full chat persistence
- Message search and analytics
- File upload and processing
- Basic authentication
- Simple publishing to web
- Essential monitoring
Success Criteria: 10 beta users actively using the platform
Phase 2: Enterprise Features (6 weeks)
Goal: Add enterprise security and management
Features:
- SSO integration
- Advanced RBAC
- Audit logging
- Guide management system
- Cost analytics
- SIEM integration
Success Criteria: Pass security audit, onboard first enterprise client
Phase 3: Intelligence & Scale (8 weeks)
Goal: Enhance AI capabilities and performance
Features:
- Advanced guide intelligence
- Model fine-tuning support
- Collaboration features
- Advanced analytics
- API for integrations
- Multi-region deployment
Success Criteria: 100+ active organizations, 95% user satisfaction
Business Model & Pricing
Pricing Tiers
| Tier | Price | Users | Features | Target |
|---|---|---|---|---|
| Starter | $49/user/mo | 1-10 | Core features, 1000 queries/mo | Small teams |
| Professional | $39/user/mo | 11-50 | All features, 5000 queries/mo | Growing companies |
| Enterprise | Custom | 50+ | Unlimited, SSO, SLA | Large organizations |
| On-Premise | $50k+/year | Unlimited | Full control, local models | Security-focused |
Cost Structure
- Infrastructure: $5-10/user/month
- AI API Costs: $10-20/user/month (reduced via TinyLlama fallback - Active)
- Development: $500k initial, $200k/year maintenance
- Support: 20% of revenue
Revenue Projections
| Year | Users | ARR | Growth |
|---|---|---|---|
| Year 1 | 1,000 | $500k | - |
| Year 2 | 5,000 | $2.5M | 400% |
| Year 3 | 20,000 | $10M | 300% |
Competitive Analysis
Direct Competitors
| Competitor | Strengths | Weaknesses | Our Advantage |
|---|---|---|---|
| ChatGPT Enterprise | Brand recognition | Single model, expensive | Multi-model, cost-effective |
| Claude for Business | Advanced reasoning | Limited deployment options | Flexible deployment |
| Perplexity for Business | Good search | No local options | Complete platform |
| Custom Solutions | Tailored features | Expensive, slow | Fast deployment |
Unique Differentiators
- Unified Multi-Model Platform: Only solution offering Claude, GPT, and local models
- One-Click Publishing: Transform insights into websites instantly
- Guide Intelligence: Pre-built methodologies for consistent quality
- Flexible Deployment: Cloud, on-premise, or hybrid options
- Cost Optimization: 70% cheaper through intelligent routing
Risk Analysis & Mitigation
Technical Risks
| Risk | Impact | Probability | Mitigation |
|---|---|---|---|
| API Rate Limits | High | Medium | Multiple provider fallbacks, TinyLlama auto-fallback |
| Model Deprecation | Medium | Low | Abstract model interface, regular updates |
| Data Breach | Critical | Low | Encryption, security audits, insurance |
| Scaling Issues | High | Medium | Cloud-native architecture, load testing |
Business Risks
| Risk | Impact | Probability | Mitigation |
|---|---|---|---|
| Slow Adoption | High | Medium | Freemium tier, strong onboarding |
| Competition | High | High | Rapid feature development, partnerships |
| Compliance Changes | Medium | Medium | Flexible architecture, legal counsel |
| Cost Overruns | Medium | Low | Usage limits, monitoring, optimization |
Success Criteria & KPIs
Launch Success (Month 1)
- 100 beta users onboarded
- 90% complete onboarding
- < 5% churn rate
- 4.5+ user satisfaction
Growth Success (Month 6)
- 1,000 active users
- $50k MRR
- 3 enterprise clients
- < 2% monthly churn
Scale Success (Year 1)
- 5,000 active users
- $500k ARR
- 20 enterprise clients
- Industry recognition
Appendices
A. Detailed User Stories
Available in separate document: sasha-studio-user-stories.md
B. Technical Specifications
See: sasha-studio-implementation-guide.md
C. Security & Compliance
Reference: sasha-studio-security-framework.md
D. API Documentation
Coming in: sasha-studio-api-reference.md
Next Steps
Immediate Actions
- Finalize MVP feature set with stakeholders
- Secure development resources
- Begin UI/UX mockup creation
- Set up development environment
Week 1 Deliverables
- Complete UI mockups for review
- Finalize technical architecture
- Create development sprint plan
- Onboard development team
Month 1 Goals
- Alpha version deployed
- Core features functional
- Internal testing complete
- Beta user recruitment started
This PRD represents the complete product vision for Sasha Studio. For questions or clarifications, contact the product team.
Document Status: Complete
Last Review: 2025-01-03
Next Review: 2025-02-01