ULPI: Universal Language & Prompt Intelligence
- Executive Summary
- System Overview
- 1.1 Core Architecture
- 1.2 Architecture Components
- 1.3 Client Types
- Core Features & Implementation Phases
- Phase 1: Foundation
- Phase 2: Memory System
- Phase 3: Context Intelligence
- Phase 4: Universal Knowledge Platform
- Phase 5: Advanced Features
- Phase 6: Multi-Role Expansion
- Detailed API Specifications
- API Standards
- Database Schemas
- 5.1 MySQL Tables
- 5.2 MongoDB Collections
- Queue Jobs
- Client Integration Patterns
- Performance Requirements
- Security Requirements
- Success Metrics
- MVP Definition
- Technical Decisions
- Knowledge Management System
- Supported Knowledge Types
- Release Timeline
ULPI: One Hotkey, Any App, Every LLM Knows Everything You Know
ULPI (Universal Language & Prompt Intelligence) is an AI memory layer that transforms how professionals interact with Large Language Models, making every AI assistant instantly aware of your expertise, team knowledge, and industry best practices.
Knowledge workers waste 10-20 hours monthly rewriting the same prompts, onboarding team members, and searching for past solutions. Every time they switch between ChatGPT, Claude, or any LLM, they start from scratch—no memory of their expertise, team standards, or past decisions.
ULPI is an AI memory layer that sits between professionals and any LLM. One hotkey (⌥⌘M) in any app instantly injects:
- Your expertise: Personal style, past work, proven approaches
- Team knowledge: Company playbooks, decisions, collective wisdom
- Industry intelligence: Best practices, frameworks, competitor insights
- Role-specific patterns: Whether you're coding, writing, analyzing, or selling
- Hit ⌥⌘M anywhere → Email, ChatGPT, VS Code, Notion, Slack, anywhere
- ULPI detects context → Coding? Writing? Financial model? Marketing copy?
- Auto-injects intelligence → Your expertise + team knowledge + industry best practices
- Any LLM responds perfectly → Like your smartest colleague who knows everything
| Role | Without ULPI | With ULPI |
|---|---|---|
| Developer | "How do I implement auth?" | ⌥⌘M → Complete auth system using your stack |
| Product Manager | "Write a PRD for..." | ⌥⌘M → PRD in your format with your metrics |
| Marketer | "Create campaign copy" | ⌥⌘M → On-brand copy in your voice |
| Finance | "Build a revenue model" | ⌥⌘M → Model with your assumptions |
| Sales | "Draft a proposal" | ⌥⌘M → Proposal with your pricing & terms |
| Customer Success | "Respond to complaint" | ⌥⌘M → Response in company tone |
- In ChatGPT: Your personal + team knowledge auto-injected
- In Email: Responses match your communication style
- In Code Editors: Technical patterns + team conventions
- In Google Docs: Company voice + industry standards
- In Slack: Quick answers with full context
- Anywhere with text: One hotkey, infinite intelligence
- 180M+ professionals use AI daily across all industries
- $500B productivity loss from context switching and knowledge silos
- Every role needs this: Developers, PMs, marketers, finance, sales, support
- Enterprise demand: "We need consistent AI outputs across 10,000 employees"
- Platform agnostic: Works with ChatGPT, Claude, Gemini, and future LLMs
- Free: 50 enhancements/month, basic features (user provides API keys)
- Starter: $19/month - 500 enhancements, 100 memories
- Pro: $49/month - Unlimited enhancements, 1000 memories, BYOK option
- Team: $39/seat/month - Everything + team sharing (min 3 seats, BYOK required)
- Enterprise: Custom pricing with private deployment
BYOK (Bring Your Own Key): Users can connect their own LLM API keys to eliminate our API costs
| Stage | Timeline | Budget | Free Users | Cost/User | Infrastructure |
|---|---|---|---|---|---|
| MVP | Month 1-3 | $2K/mo | 500-1,000 | $0.30 | Minimal (free tiers) |
| Growth | Month 4-6 | $8K/mo | 5,000-10,000 | $0.15 | Scaled cloud |
| Scale | Month 7-12 | $10K/mo | 20,000-30,000 | $0.10 | Auto-scaling |
| Profitable | Year 2+ | Self-sustaining | 50,000-100,000 | $0.05 | Enterprise |
Free User Limits:
- 50 enhancements/month
- 10 memories maximum
- 30-day memory expiration
- Must provide own API keys
- No team sharing
Conversion Targets:
- Months 1-3: 10% → 50 paid users
- Months 4-6: 5% → 250 paid users
- Months 7-12: 3% → 600 paid users
- AI adoption exploded: 180M+ ChatGPT users but 73% report "context fatigue"
- LLM proliferation: GPT-4, Claude 3, Gemini, Llama - users need one interface
- Enterprise demand: Companies desperately need consistent AI outputs
- Universal: Works with ALL LLMs, not locked to one provider
- Instant: One hotkey in any app - no copy/paste/switching
- Intelligent: Learns from your team's actual work, not generic training
- MVP: 3-month timeline, Laravel backend ready
- Early Interest:
- 3 dev teams interested in pilot (50 users)
- 2 product teams exploring adoption (20 users)
- 100+ professionals on waitlist
- 5 companies requested enterprise demos
- Path to Break-Even:
- Month 6: 150 paid users = $8K MRR (near break-even)
- Month 12: 300 paid users = $16K MRR (profitable)
- Conservative 5% free-to-paid conversion
- Seeking: $500K pre-seed to:
- Finish MVP and launch (3 months)
- Run 6-month pilot program
- Reach break-even without additional funding
- Use of funds:
- 40% product development
- 30% LLM/infrastructure costs
- 20% user acquisition
- 10% operations
flowchart LR
A["👥 Knowledge Worker<br/>Developer, PM, Writer, etc."] --> B{"🎯 Trigger Options"}
B --> B1["⌨️ Global Hotkey<br/>YouTube scripts, PRDs, etc."]
B --> B2["💬 Chat Interface"]
B --> B3["🔌 App Integration<br/>VS Code, Notion, Figma, etc."]
B --> B4["🎙️ Voice Command<br/>Hotkey to speak intent"]
B --> B5["🤖 Auto-trigger<br/>on context/app change"]
B1 --> C["🧠 Memory Control Plane MCP"]
B2 --> C
B3 --> C
B4 --> C
B5 --> C
C --> C1["🔍 Classifies context & domain"]
C --> C2["📚 Injects your voice:<br/>Personal / Team / Domain"]
C --> C3["🧩 Adds preferences + patterns<br/>across all LLMs"]
C1 --> D["🤖 Any LLM<br/>Claude, GPT-4, Gemini, etc."]
C2 --> D
C3 --> D
D --> E["✅ Consistent YOU across all models"]
E --> F["🪄 Final Output:<br/>Auto-paste, insert, or clipboard"]
subgraph MemoryStore["Your Digital Voice"]
C2a["🧍 Personal Voice<br/>Writing style, preferences"]
C2b["👥 Team Voice<br/>Company tone, processes"]
C2c["🏷️ Domain Voice<br/>Laravel, Figma, Marketing, etc."]
end
C2 --> C2a
C2 --> C2b
C2 --> C2c
graph TD
A[ULPI System] --> B[API Layer - Laravel]
A --> C[Data Layer]
A --> D[Processing Layer]
A --> E[Intelligence Layer]
B --> B1[REST APIs]
B --> B2[WebSocket/SSE]
B --> B3[Webhook Handlers]
C --> C1[MySQL<br/>Users/Auth]
C --> C2[MongoDB<br/>Knowledge/Memories]
C --> C3[Qdrant<br/>Vector Search]
C --> C4[Redis<br/>Cache/Queue]
D --> D1[Horizon Queue Workers]
D --> D2[Knowledge Extractors]
D --> D3[Embedding Pipeline]
E --> E1[LLM Services<br/>OpenAI/Anthropic/Gemini]
E --> E2[Context Analyzer]
E --> E3[Knowledge Graph]
- ULPI API: RESTful API service (Laravel 10.x)
- Vector Store: Qdrant for semantic search
- Databases: MySQL (users/auth), MongoDB (knowledge)
- Queue System: Redis + Horizon for async processing
- LLM Providers: OpenAI, Anthropic, Google Gemini
- Cache Layer: Redis for performance optimization
- Desktop Apps: VS Code extension, JetBrains plugin
- Browser Extension: Chrome/Firefox/Edge
- Native Apps: macOS menu bar, Windows system tray
- Web Dashboard: Settings and memory management
- Mobile SDK: iOS/Android keyboard extensions
- CLI Tool: Terminal integration
- POST /api/auth/register - User registration with email/OAuth
- POST /api/auth/login - JWT-based authentication
- POST /api/auth/refresh - Token refresh
- GET /api/auth/user - Current user profile
- PUT /api/auth/user - Update user settings
- POST /api/auth/logout - Invalidate tokens
- POST /api/teams - Create team
- GET /api/teams - List user's teams
- GET /api/teams/{id} - Team details
- PUT /api/teams/{id} - Update team settings
- POST /api/teams/{id}/invite - Invite team members
- POST /api/teams/{id}/members/{userId} - Add/remove members
- POST /api/profiles - Create profile (Backend Engineer, PM, etc.)
- GET /api/profiles - List user's profiles
- GET /api/profiles/{id} - Profile details
- PUT /api/profiles/{id} - Update profile
- DELETE /api/profiles/{id} - Delete profile
- POST /api/profiles/{id}/activate - Set active profile
- POST /api/memories - Create memory
{ "content": "Always use Repository pattern", "domain": "laravel", "tags": ["architecture", "patterns"], "type": "personal|team|global" } - GET /api/memories - List memories (paginated, filtered)
- GET /api/memories/{id} - Get memory details
- PUT /api/memories/{id} - Update memory
- DELETE /api/memories/{id} - Soft delete memory
- POST /api/memories/search - Semantic search memories
- POST /api/memories/extract - Extract from conversation
- POST /api/memories/extract/code-review - Extract from PR
- POST /api/memories/extract/error - Extract from error resolution
- GET /api/memories/extractions - List extraction history
- POST /api/context/analyze - Analyze current context
{ "app": "vscode", "file_path": "/app/Jobs/UserJob.php", "selected_text": "public function handle()", "profile_id": "backend-engineer-profile-id" }
- POST /api/enhance - Main enhancement endpoint
{ "input": "How do I create a job?", "context": { "app": "vscode", "file": "UserController.php" }, "profile_id": "xxx", "mode": "prompt|expert", "llm_target": "openai|anthropic|google" } - POST /api/enhance/stream - SSE streaming enhancement
- GET /api/templates - List quick templates
- POST /api/templates/apply - Apply template to context
- POST /api/knowledge/ingest - Universal ingestion endpoint
{ "source_type": "repository|video|podcast|blog|forum|slack|confluence|api_docs|course|research_paper|social_media|newsletter|webinar", "source_config": { "url": "https://www.youtube.com/watch?v=...", "authentication": {...}, "extraction_rules": {...} }, "processing_instructions": { "extract_code_samples": true, "extract_diagrams": true, "transcribe_audio": true, "ocr_images": true, "extract_comments": true }, "metadata": { "author": "Kent C. Dodds", "expertise_level": "expert", "topics": ["react", "testing"], "quality_score": 0.95 }, "refresh_strategy": { "type": "webhook|polling|manual", "frequency": "realtime|daily|weekly", "check_updates": true } }
- POST /api/knowledge/communities/connect - Connect to knowledge communities
{ "platform": "stackoverflow|discord|slack|reddit|hackernews", "channels": ["laravel", "react-help"], "filters": { "min_score": 10, "verified_answers": true, "expert_users": ["taylorotwell", "calebporzio"] }, "capture": ["questions", "solutions", "discussions", "code_snippets"] } - POST /api/knowledge/streams/subscribe - Subscribe to live knowledge streams
- GET /api/knowledge/trending - Trending topics and solutions
- POST /api/knowledge/enterprise/connect - Connect internal systems
{ "systems": [ { "type": "confluence", "spaces": ["engineering", "product"], "sync_private": true }, { "type": "jira", "projects": ["CORE", "MOBILE"], "extract": ["bugs", "postmortems", "specifications"] }, { "type": "customer_support", "extract": ["solutions", "common_issues", "workarounds"] } ], "privacy": { "anonymize_pii": true, "access_control": "team_based" } }
- POST /api/knowledge/process/video - Extract knowledge from video
- POST /api/knowledge/process/diagram - Extract from diagrams/whiteboards
- POST /api/knowledge/process/code - Deep code analysis
{ "repository": "company/monorepo", "analyze": { "patterns": true, "architecture": true, "best_practices": true, "anti_patterns": true }, "cross_reference": ["documentation", "pull_requests", "issues"] }
- POST /api/knowledge/graph/connect - Create knowledge relationships
- GET /api/knowledge/graph/explore - Explore connected concepts
- POST /api/knowledge/graph/validate - Validate conflicting information
{ "concept": "state_management", "sources": ["redux_docs", "mobx_docs", "zustand_docs"], "resolution_strategy": "compare|synthesize|latest|most_trusted" }
- POST /api/knowledge/curate/auto - AI-powered curation
{ "topic": "microservices", "depth": "comprehensive|overview|quick_reference", "combine_sources": true, "remove_outdated": true, "skill_level": "beginner|intermediate|expert", "output_format": "tutorial|reference|cookbook" } - GET /api/knowledge/quality/audit - Quality audit of knowledge base
- POST /api/knowledge/deprecate - Mark outdated knowledge
- POST /api/voices/learn - Learn from any text source
{ "sources": [ {"type": "blog", "url": "sethgodin.com", "posts": 100}, {"type": "tweets", "handle": "@pmarca", "count": 1000}, {"type": "books", "author": "Paul Graham", "titles": ["all"]} ], "analyze": { "writing_style": true, "thought_patterns": true, "vocabulary": true, "persuasion_techniques": true }, "create_voice_profile": { "name": "Thought Leader PM", "combine_with": ["existing_pm_voice"] } } - POST /api/voices/blend - Blend multiple voices
- GET /api/voices/recommend - Recommend voice for context
- GET /api/analytics/usage - Usage statistics
- GET /api/analytics/savings - Time saved metrics
- GET /api/analytics/popular-memories - Most used memories
- GET /api/analytics/team-insights - Team patterns
- GET /api/integrations - List available integrations
- POST /api/integrations/{type}/connect - Connect integration
- POST /api/integrations/{type}/webhook - Integration webhooks
- POST /api/voice/transcribe - Voice to text
- POST /api/hotkeys/register - Register global hotkey
- GET /api/hotkeys - List user's hotkeys
- POST /api/roles - Define new professional role
{ "name": "CFO", "categories": ["financial_planning", "investor_relations", "compliance"], "default_voices": ["Fortune500_CFO", "Startup_CFO"], "knowledge_sources": ["financial_frameworks", "sec_guidelines"] } - GET /api/roles - List all supported roles
- GET /api/roles/{role}/knowledge - Role-specific knowledge
- POST /api/industries - Add industry vertical
- GET /api/industries/{id}/terminology - Industry glossary
- POST /api/industries/{id}/compliance - Add compliance rules
- POST /api/enhance/multi-role - Get perspectives from multiple roles
{ "input": "Q3 revenue projections", "roles": ["CFO", "VP_Sales", "Product_Manager"], "output_format": "comparison|synthesis|debate" }
POST /api/auth/register
Request:
{
"email": "[email protected]",
"password": "secure_password",
"name": "John Doe",
"timezone": "America/New_York"
}
Response:
{
"success": true,
"data": {
"user": {
"id": 1,
"email": "[email protected]",
"name": "John Doe"
},
"token": "jwt_token",
"refresh_token": "refresh_token"
}
}POST /api/auth/oauth/{provider}
- Providers: google, github, microsoft
- Handles OAuth flow and creates/links accounts
POST /api/profiles
Request:
{
"name": "Senior Backend Engineer",
"description": "Laravel expert with 5+ years experience",
"settings": {
"default_llm": "anthropic",
"auto_inject_domains": ["laravel", "php", "mysql"],
"enhancement_mode": "expert"
}
}
Response:
{
"success": true,
"data": {
"id": "prof_abc123",
"name": "Senior Backend Engineer",
"is_active": true,
"created_at": "2024-01-01T10:00:00Z"
}
}POST /api/memories
Request:
{
"content": "Always use Repository pattern for data access in Laravel",
"type": "personal", // personal|team|global
"domain": "laravel",
"tags": ["architecture", "patterns", "best-practices"],
"context": {
"source": "code_review",
"project": "api-v2",
"confidence": 0.95
}
}
Response:
{
"success": true,
"data": {
"id": "mem_xyz789",
"content": "Always use Repository pattern for data access in Laravel",
"embedding_status": "processing",
"relevance_score": 0.92
}
}POST /api/memories/search
Request:
{
"query": "laravel repository pattern",
"filters": {
"type": ["personal", "team"],
"domains": ["laravel"],
"min_confidence": 0.8
},
"limit": 10
}
Response:
{
"success": true,
"data": {
"results": [
{
"id": "mem_xyz789",
"content": "Always use Repository pattern...",
"relevance": 0.94,
"source": "code_review",
"used_count": 15
}
],
"total": 3
}
}Requirements:
- Dynamic Token Budgeting: Allocate token limits based on LLM model (GPT-4: 128k, Claude: 200k, etc.)
- Smart Prioritization: Score and rank context by relevance, recency, and usage frequency
- Hierarchical Injection: Load critical context first, then add optional context if space allows
- Context Summarization: Auto-summarize lengthy memories when approaching token limits
- Chunking Strategy: Split long content semantically (by function, section, or paragraph)
- Fallback Handling: Gracefully degrade when context exceeds limits
- User Control: Let users set context preferences (prefer recent, prefer personal, etc.)
Token Budget Allocation:
- User query: 10% (~12k tokens for GPT-4)
- Relevant memories: 40% (~50k tokens)
- Team context: 20% (~25k tokens)
- Domain knowledge: 20% (~25k tokens)
- System instructions: 10% (~12k tokens)
POST /api/context/analyze
Request:
{
"app": "vscode",
"file_path": "/app/Http/Controllers/UserController.php",
"file_content": "<?php\nnamespace App\\Http\\Controllers;\n...",
"cursor_position": 145,
"recent_files": [
"/app/Models/User.php",
"/routes/api.php"
],
"git_branch": "feature/user-api"
}
Response:
{
"success": true,
"data": {
"context": {
"framework": "laravel",
"component": "controller",
"likely_task": "implementing_user_endpoint",
"relevant_patterns": ["repository", "api_resource"],
"team_conventions": ["use form requests", "return json responses"]
}
}
}POST /api/enhance
Request:
{
"input": "How do I create a job?",
"context": {
"app": "vscode",
"file": "UserController.php",
"project": "laravel-api"
},
"profile_id": "prof_abc123",
"options": {
"include_examples": true,
"use_team_patterns": true,
"verbosity": "concise",
"target_llm": "gpt-4",
"max_context_tokens": 50000,
"prioritize": "recent|relevant|personal|team"
}
}
Response:
{
"success": true,
"data": {
"enhanced_prompt": "In Laravel 10.x, create a queued job for user data processing...",
"injected_context": [
"Team convention: Use app/Jobs directory",
"Pattern: Implement ShouldQueue interface",
"Recent example: ProcessUserDataJob.php"
],
"context_stats": {
"tokens_used": 45230,
"tokens_available": 128000,
"memories_included": 12,
"memories_excluded": 5,
"summarized": false
},
"confidence": 0.94
}
}POST /api/enhance/stream
- Server-Sent Events (SSE) endpoint for real-time streaming
- Same request format as
/api/enhance - Returns chunks as:
data: {"content": "partial text", "finished": false}\n\n
POST /api/context/preview
Request:
{
"query": "How to implement authentication",
"profile_id": "prof_abc123",
"target_llm": "gpt-4"
}
Response:
{
"success": true,
"data": {
"estimated_tokens": 45230,
"token_limit": 128000,
"breakdown": {
"system_prompt": 500,
"user_query": 50,
"memories": 25000,
"team_context": 10000,
"domain_knowledge": 9680
},
"will_fit": true,
"optimization_suggestions": [
"Consider summarizing memories older than 30 days",
"Remove duplicate Laravel auth patterns"
]
}
}POST /api/knowledge/ingest
Request:
{
"source_type": "video", // video|blog|repository|documentation|forum|slack
"source_config": {
"url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
"platform": "youtube"
},
"processing_instructions": {
"transcribe_audio": true,
"extract_code_samples": true,
"extract_timestamps": true,
"identify_speakers": true
},
"metadata": {
"author": "Laravel Daily",
"topics": ["laravel", "testing"],
"quality_rating": 0.9
}
}
Response:
{
"success": true,
"data": {
"ingestion_id": "ing_abc123",
"status": "processing",
"estimated_completion": "2024-01-01T10:05:00Z",
"webhook_url": "https://api.ulpi.ai/webhooks/ingestion/ing_abc123"
}
}POST /api/knowledge/graph/query
Request:
{
"start_concept": "laravel_queues",
"relationship_types": ["implements", "requires", "conflicts_with"],
"max_depth": 3,
"include_examples": true
}
Response:
{
"success": true,
"data": {
"nodes": [
{
"id": "concept_123",
"name": "laravel_queues",
"type": "framework_feature"
},
{
"id": "concept_456",
"name": "redis",
"type": "technology"
}
],
"edges": [
{
"from": "concept_123",
"to": "concept_456",
"relationship": "requires",
"strength": 0.8
}
]
}
}GET /api/analytics/usage
Response:
{
"success": true,
"data": {
"period": "last_30_days",
"enhancements": 1523,
"time_saved_hours": 127,
"most_used_memories": [
{
"id": "mem_123",
"content": "Laravel repository pattern",
"usage_count": 87
}
],
"knowledge_growth": {
"memories_created": 234,
"sources_connected": 12,
"team_contributions": 567
}
}
}POST /api/intelligence/predict
Request:
{
"user_context": {
"current_file": "PaymentController.php",
"recent_queries": ["stripe integration", "webhook handling"],
"time_of_day": "09:30",
"project_phase": "implementation"
}
}
Response:
{
"success": true,
"data": {
"predictions": [
{
"likely_need": "stripe_webhook_verification",
"confidence": 0.89,
"preloaded_knowledge": ["webhook security", "stripe signatures"]
}
]
}
}POST /api/enhance/multi-role
Request:
{
"input": "We need to improve API performance",
"roles": ["backend_engineer", "devops", "product_manager"],
"format": "synthesis" // comparison|synthesis|debate
}
Response:
{
"success": true,
"data": {
"perspectives": {
"backend_engineer": "Implement caching layers, optimize queries...",
"devops": "Scale horizontally, add CDN, monitor bottlenecks...",
"product_manager": "Consider feature flags for gradual rollout..."
},
"synthesis": "A comprehensive approach would involve..."
}
}- JWT tokens with 7-day expiry
- Refresh tokens with 30-day expiry
- API keys for CLI/desktop apps
- OAuth 2.0 for third-party integrations
- Authenticated: 600 requests/minute
- Unauthenticated: 60 requests/minute
- Enhancement endpoint: 30 requests/minute
- Knowledge ingestion: 10 requests/minute
{
"success": true,
"data": {},
"meta": {
"timestamp": "2024-01-01T10:00:00Z",
"version": "1.0"
}
}{
"success": false,
"error": {
"code": "VALIDATION_ERROR",
"message": "Invalid input",
"details": {}
}
}-- users
id, email, name, password, provider, provider_id, active_profile_id, settings, created_at, updated_at
-- teams
id, name, slug, owner_id, settings, created_at, updated_at
-- team_users
id, team_id, user_id, role, joined_at
-- api_keys
id, user_id, name, key_hash, last_used_at, expires_at, created_at
-- profiles
id, user_id, name, description, settings, is_active, created_at, updated_at{
_id: ObjectId,
user_id: Number,
team_id: Number,
content: String,
type: "personal|team|global",
domain: String,
tags: [String],
qdrant_points: [
{
point_id: String,
chunk_index: Number,
chunk_text: String
}
],
context: {
source: String,
confidence: Float,
extracted_from: String
},
usage_stats: {
used_count: Number,
last_used: Date,
effectiveness_score: Float
},
created_at: Date,
updated_at: Date,
deleted_at: Date // Soft deletes
}{
_id: ObjectId,
type: "video|blog|repository|forum|slack|documentation",
name: String,
source_url: String,
config: Object,
status: "active|processing|error",
sync_schedule: String,
last_sync: Date,
next_sync: Date,
statistics: {
items_processed: Number,
knowledge_extracted: Number,
quality_score: Float
},
metadata: {
branch: String,
paths: [String],
total_documents: Number,
total_chunks: Number
}
}{
_id: ObjectId,
name: String,
role: String,
experience_level: "senior|mid|junior",
characteristics: {
tone: String,
vocabulary: [String],
sentence_structure: String,
examples: [String]
},
training_data: [String],
usage_count: Number,
satisfaction_score: Float,
created_at: Date,
updated_at: Date
}{
_id: ObjectId,
pattern: String,
triggers: [Object],
applicable_contexts: [String],
success_rate: Float,
examples: [Object],
created_at: Date
}{
_id: ObjectId,
job_id: String,
trigger_type: String,
user_id: Number,
team_id: Number,
source_data: Object,
llm_request: {
provider: String,
model: String,
prompt_tokens: Number,
completion_tokens: Number,
cost: Float
},
extracted_memories: [
{
memory_id: String,
confidence: Float,
reason: String
}
],
status: String,
error: String,
processing_time_ms: Number,
created_at: Date,
completed_at: Date
}{
_id: ObjectId,
user_id: Number,
profile_id: String,
query: String,
llm_model: String,
optimization_strategy: String,
token_budget: Number,
token_usage: {
total: Number,
system: Number,
memories: Number,
team: Number,
domain: Number
},
excluded_content: [
{
type: String,
reason: String,
tokens_saved: Number
}
],
performance_ms: Number,
created_at: Date
}- IngestContentJob: Process videos, blogs, documentation
- ExtractMemoriesJob: Extract actionable knowledge from content
- GenerateEmbeddingsJob: Create vector embeddings for search
- SyncCommunityJob: Pull latest from Stack Overflow, forums
- UpdateKnowledgeGraphJob: Maintain relationship connections
- RefreshDocumentationJob: Weekly updates for all documentation sources
- PackageDiscoveryJob: Daily scan for new packages/versions
- AnalyzeContextJob: Deep context analysis for complex scenarios
- PredictUserNeedsJob: Pre-calculate likely next actions
- OptimizeKnowledgeJob: Prune outdated, merge similar knowledge
- TrainVoiceProfileJob: Learn from new content sources
- ProcessMemoryJob: Extract memories from conversations/events
- CleanupExpiredTokensJob: Remove expired JWT tokens
- CalculateAnalyticsJob: Generate usage statistics
- BackupVectorStoreJob: Backup Qdrant collections
- SyncTeamSettingsJob: Propagate team changes to members
// Hotkey triggered
const context = vscode.getContext();
const response = await ulpi.enhance({
input: selectedText,
context: {
app: 'vscode',
file: activeFile,
language: detectLanguage()
},
profile_id: activeProfile
});
vscode.insert(response.enhanced_output);// Right-click menu or hotkey
const response = await ulpi.enhance({
input: getSelectedText(),
context: {
app: 'chrome',
url: window.location.href,
field_type: detectFieldType()
}
});
replaceText(response.enhanced_output);# Terminal command
$ ulpi enhance "create user migration" --profile backend --app terminal
# Output: Laravel migration boilerplate// JavaScript SDK
import { ULPI } from '@ulpi/sdk';
const ulpi = new ULPI({ apiKey: 'your-api-key' });
const enhanced = await ulpi.enhance({
input: 'Create a REST API',
profile: 'backend-engineer'
});- Auth endpoints: < 100ms (p95)
- Memory search: < 200ms (p95)
- Context analysis: < 300ms (p95)
- Enhancement: < 500ms (p95)
- Streaming: First token < 200ms
- Knowledge ingestion: Async with webhooks
- Context optimization: < 150ms (p95)
- Token counting: < 50ms (p95)
- Concurrent users: 10,000
- API requests: 1M/day
- Vector searches: 10M/day
- Knowledge storage: 100GB per organization
- Uptime: 99.9% availability
- Redis caching for frequently accessed memories
- CDN for static knowledge content
- Database read replicas for scale
- Queue workers auto-scaling
- Vector index optimization
Per User Resource Consumption (Monthly):
| User Type | Storage | Vectors | API Calls | Bandwidth | Queue Jobs |
|---|---|---|---|---|---|
| Free | 10MB | 100 | 50 (BYOK) | 50MB | 20 |
| Starter | 50MB | 500 | 500 (BYOK) | 200MB | 100 |
| Pro | 200MB | 1000 | Unlimited | 500MB | Unlimited |
| Team | 100MB/seat | 2000/seat | Unlimited | 1GB/seat | Unlimited |
Infrastructure Scaling Triggers:
- When capacity > 80%: Scale up
- When capacity < 40%: Scale down
- When free user CAC > $1: Restrict signups
- When paid user latency > SLA: Prioritize resources
- OAuth 2.0 for third-party apps
- JWT with refresh token rotation
- API key management with scopes
- Multi-factor authentication support
- Role-based access control (RBAC)
- End-to-end encryption for sensitive memories
- Encryption at rest for all databases
- TLS 1.3 for all API communication
- Team-level data isolation
- PII anonymization options
- GDPR compliance with right to deletion
- SOC 2 Type II certification path
- HIPAA compliance ready
- Data residency options
- Audit logs for all data access
- Rate limiting and DDoS protection
- Anomaly detection for usage patterns
- Security event logging
- Vulnerability scanning
- Incident response procedures
- Daily Active Users (DAU)
- Memories created per user
- Enhancement requests per day
- Profile switches per week
- Knowledge sources connected
- Time saved per user (target: 10-20 hrs/month)
- Memory injection accuracy (> 85%)
- Context detection accuracy (> 90%)
- User satisfaction score (NPS > 50)
- API response times (< 500ms p95)
- Customer retention rate (> 90%)
- Team adoption rate (> 80%)
- Revenue per user
- Support ticket volume (< 5% of MAU)
- Expansion revenue from teams
- Free to paid conversion (> 3%)
- Cost per free user (< $0.50/month)
- Infrastructure efficiency (< 30% of revenue)
- Knowledge relevance score (> 85%)
- Prediction accuracy (> 75%)
- Voice matching quality (> 90%)
- Knowledge graph connections
- Memory reuse rate
- Context fit success rate (> 95%)
- Average token utilization (70-85% of limit)
- Context overflow incidents (< 1% of requests)
Month 1: Foundation
- ✅ Authentication & team management
- ✅ Basic profile system
- ✅ Memory CRUD operations
- ✅ Simple vector search
Month 2: Intelligence
- ✅ Context analysis engine
- ✅ Prompt enhancement
- ✅ Basic knowledge ingestion
- ✅ Team knowledge sharing
Month 3: Platform
- ✅ Multi-source ingestion (repositories, docs)
- ✅ Knowledge graph basics
- ✅ Voice profiles (2-3 variations)
- ✅ Analytics dashboard
- ✅ VS Code extension
- ✅ Web dashboard
Quarter 2
- Advanced predictive intelligence
- Real-time collaboration features
- Enterprise integrations (Confluence, Jira)
- Mobile SDKs
Quarter 3
- Multi-role perspectives
- Industry-specific packages
- Autonomous agents
- Knowledge marketplace
Quarter 4
- AI model fine-tuning
- Advanced analytics
- White-label options
- Global knowledge network
Future Expansion (Year 2)
- Multi-language support: Start with Spanish, French, German
- Localization: Cultural context handling, regional preferences
- Global knowledge bases: Country-specific regulations, practices
- Translation layer: Cross-language knowledge sharing
- Framework: Laravel 10.x
- Queue: Redis + Horizon
- Vector DB: Qdrant (self-hosted initially)
- LLM: OpenAI GPT-4 (primary), Anthropic Claude (fallback)
- Monitoring: Laravel Telescope + Sentry
- Deployment: Docker + Kubernetes
- Language Strategy: English-first for MVP, i18n-ready architecture
- Token Management: tiktoken for accurate token counting
Free Tier Management:
- Aggressive caching (24hr for common queries)
- Memory deduplication across users
- Auto-expire unused memories after 30 days
- Require BYOK for all free users
Infrastructure Efficiency:
- Start with free tiers (MongoDB Atlas, Qdrant Cloud)
- Use spot instances for non-critical workers
- Implement request coalescing for similar queries
- Progressive feature unlocking based on usage
Scaling Strategy:
- Stage 1: Monolith on single server
- Stage 2: Separate API and workers
- Stage 3: Microservices for heavy features
- Stage 4: Multi-region deployment
- RefreshDocumentationJob: Weekly updates for all documentation sources
- PackageDiscoveryJob: Daily scan for new packages/versions
- KnowledgeIndexingJob: Process and chunk new content into Qdrant
- VoiceTrainingJob: Update voice profiles based on usage patterns
{
"_id": "...",
"type": "repository|book|framework|package",
"name": "Laravel Documentation",
"source_url": "https://github.com/laravel/docs",
"framework": "laravel",
"version": "10.x",
"refresh_schedule": "weekly",
"last_refresh": "2024-01-01T00:00:00Z",
"next_refresh": "2024-01-08T00:00:00Z",
"status": "active|refreshing|error",
"metadata": {
"branch": "10.x",
"paths": ["*.md"],
"total_documents": 156,
"total_chunks": 3420
}
}{
"_id": "...",
"name": "Big Tech PM",
"role": "product_manager",
"experience_level": "senior|mid|junior",
"characteristics": {
"tone": "data-driven, strategic",
"vocabulary": ["OKRs", "north star metric", "flywheel"],
"sentence_structure": "complex, nuanced",
"examples": ["Based on our Q3 data..."]
},
"training_data": ["sample_outputs"],
"usage_count": 1523,
"satisfaction_score": 0.94
}{
"_id": "...",
"name": "VP_Engineering",
"department": "engineering",
"responsibilities": ["technical strategy", "team scaling"],
"knowledge_domains": ["system_design", "leadership", "agile"],
"default_voices": ["startup_vp_eng", "enterprise_vp_eng"],
"common_contexts": ["architecture_review", "team_1on1", "board_presentation"]
}# 1. Ingest conference talk
POST /api/knowledge/ingest
{
"source_type": "video",
"source_config": {
"url": "https://youtube.com/watch?v=laravel-beyond-crud"
},
"processing_instructions": {
"transcribe_audio": true,
"extract_code_samples": true,
"extract_diagrams": true,
"identify_timestamps": true
}
}
# 2. System processes
- Downloads video
- Transcribes with timestamps
- OCRs code shown on screen
- Extracts architecture diagrams
- Identifies key concepts by section
- Creates embeddings for each concept
- Links to speaker's other content
# 3. Usage
User: "How should I structure a large Laravel app?"
Result: Pulls exact timestamp + code examples from conference talk# 1. Connect to Laravel community
POST /api/knowledge/communities/connect
{
"platform": "stackoverflow",
"filters": {
"tags": ["laravel", "php"],
"min_score": 50,
"accepted_answers": true
},
"capture": ["questions", "solutions", "comments"]
}
# 2. Real-time monitoring
- Monitors new questions/answers
- Tracks solution patterns
- Identifies expert contributors
- Updates knowledge graph
- Deprecates outdated solutions
# 3. Usage
User: "Laravel API rate limiting error"
Result: Latest community solutions + official docs + team's past fixes# 1. Connect internal systems
POST /api/knowledge/enterprise/connect
{
"systems": [
{
"type": "confluence",
"spaces": ["Engineering Wiki", "Architecture Decisions"],
"sync_private": true
},
{
"type": "jira",
"extract": ["post_mortems", "architecture_decisions", "bug_resolutions"]
}
]
}
# 2. Continuous sync
- Syncs private team knowledge
- Extracts patterns from incidents
- Learns from architecture decisions
- Maintains access control
# 3. Usage
User: "How do we handle payment failures?"
Result: Company's specific handling + past incidents + industry best practices# 1. Learn Marc Andreessen's communication style
POST /api/voices/learn
{
"sources": [
{"type": "tweets", "handle": "@pmarca", "count": 5000},
{"type": "blog", "url": "a16z.com/author/marc-andreessen"},
{"type": "podcast", "show": "The a16z Podcast", "episodes": "all"}
],
"analyze": {
"writing_style": true,
"thought_patterns": true,
"argument_structure": true
}
}
# 2. Creates nuanced voice profile
- Sentence structure patterns
- Vocabulary preferences
- Reasoning approaches
- Cultural references
- Industry perspectives
# 3. Usage
Input: "We need to pivot our product"
Output: "Software is eating the world, and your current approach... [Marc's style analysis + actual business logic]"# 1. Conflicting information detected
System detects: "Redux vs Context API for state management"
# 2. Intelligent resolution
POST /api/knowledge/graph/validate
{
"concept": "react_state_management",
"sources": ["redux_docs", "react_docs", "kent_c_dodds_blog"],
"resolution_strategy": "synthesize"
}
# 3. Creates nuanced understanding
- "Use Context for small apps (< 50 components)"
- "Redux for complex state with time-travel needs"
- "Consider Zustand as modern alternative"
- Links to real-world examples from each approachSource → Extractor → Processor → Chunker → Embedder → Qdrant
↓ ↓ ↓ ↓ ↓ ↓
Monitor Validate Transform Index Vector Search
- Video: Whisper API → Transcript + Timestamps
- Code: AST Parser → Patterns + Architecture
- Forums: API/Scraper → Q&A Pairs
- Diagrams: OCR + Vision AI → Concepts
- Slack/Discord: Webhooks → Real-time Knowledge
Each piece of knowledge gets scored on:
- Authority: Who created it (expert vs novice)
- Recency: How current is it
- Validation: Community votes/acceptance
- Usage: How often it's been helpful
- Accuracy: Verified against other sources
- Frameworks: Laravel, React, Vue, Django, Rails, Spring Boot
- Languages: PHP, JavaScript, Python, Ruby, Java, Go
- Packages: Auto-discovered from Packagist, NPM, PyPI, RubyGems
- Update Frequency: Weekly for docs, daily for packages
- Product Management: Lean Product, RICE, Jobs-to-be-Done, OKRs
- Engineering Leadership: System Design, Team Scaling, Technical Strategy
- Marketing: StoryBrand, AIDA, Growth Hacking, Content Strategy
- Finance: Financial Modeling, Investor Relations, Compliance
- Sales: SPIN Selling, Challenger Sale, Territory Management
Each role supports multiple experience levels and contexts:
- Startup: Fast-paced, MVP-focused, resource-conscious
- Enterprise: Process-driven, compliance-aware, stakeholder-focused
- Agency: Client-focused, deadline-driven, creative
- Authority Score: Based on source credibility
- Recency Weight: Newer knowledge ranked higher
- Usage Analytics: Popular knowledge surfaces more
- Community Validation: Upvotes and verifications
- Conflict Resolution: Smart merging of contradictions
- Alpha: Weeks 1-8 (Foundation + Core Memory)
- Beta: Weeks 9-12 (Context Intelligence)
- Beta 2: Weeks 13-20 (Universal Knowledge Platform)
- GA: Week 24 (Advanced Features)
- V2: Week 28 (Multi-Role Expansion)
Document Version: 2.0
Status: In Development
Last Updated: January 2024
Product Owner: {{ product_owner }}
Engineering Lead: {{ tech_lead }}