Leeloo
AI Feedback & Continuous Learning System
Advanced AI system that learns from corrections and builds a domain-specific knowledge graph. Detects when users correct previous statements, tracks authority, and continuously improves AI responses with real-time knowledge updates.
The AI Knowledge Problem
Current AI systems have a fundamental limitation:
- •Static knowledge - AI doesn't learn from corrections within conversations
- •No domain expertise tracking - All users treated equally, experts vs novices not distinguished
- •Repeated mistakes - Same errors across conversations, no institutional memory
- •No truth detection - Can't distinguish objective facts from opinions or guesses
- •Lost corrections - Users correct AI, but next session starts from zero again
Leeloo: Real-time learning from corrections, domain authority tracking, persistent knowledge graph.
Features
Correction Pattern Detection
AI analyzes conversations to detect when users correct previous statements. Builds a knowledge graph of corrections and factual updates.
- •Contradiction detection
- •Correction phrase patterns
- •Temporal reasoning
- •Context-aware analysis
- •Multi-turn conversation tracking
- •Implicit vs explicit corrections
Domain Authority Weighting
Track which users are authoritative in specific domains. Daniel = 100% authority on Eagle Fishing, engineers on tech stack, etc.
- •Per-user authority scores
- •Domain-specific expertise
- •Trust level tracking
- •Expert vs novice detection
- •Authority decay over time
- •Conflicting expert handling
Knowledge Graph Building
Automatically construct a knowledge graph from conversations. Entities, relationships, facts, and temporal changes tracked systematically.
- •Entity extraction
- •Relationship mapping
- •Fact versioning
- •Temporal tracking
- •Confidence scoring
- •Conflict resolution
Feedback Loop Integration
When AI gets something wrong, corrections are immediately incorporated. Future responses use updated knowledge with confidence scores.
- •Real-time knowledge updates
- •Confidence adjustment
- •Error pattern analysis
- •Automatic fact refreshing
- •Version control for facts
- •Rollback capabilities
Multi-Modal Learning
Learn from text corrections, code examples, screenshots, and structured data. Handle complex technical corrections across formats.
- •Text-based corrections
- •Code snippet learning
- •Screenshot analysis
- •Structured data (JSON, CSV)
- •API response patterns
- •Configuration file understanding
Knowledge Export & APIs
Export learned knowledge as structured data. Provide APIs for other systems to query facts, confidence scores, and sources.
- •JSON/CSV export
- •REST API queries
- •GraphQL interface
- •Webhook notifications
- •Integration with Claude Code
- •Custom MCP servers
Learning Architecture
Conversation Analysis
Monitor ongoing conversations for correction patterns, contradictions, and factual updates.
Correction Detection
ML models identify explicit corrections ("Actually...", "No, it's...") and implicit contradictions.
Authority Verification
Check user authority for the domain. Daniel > random user for Eagle Fishing facts.
Knowledge Graph Update
Update entities, relationships, facts with new info. Version old facts, track confidence changes.
Future Query Enhancement
AI queries knowledge graph before answering. Uses learned facts with appropriate confidence.
Real-World Example
Initial AI Response (Incorrect)
User: "What's the LIFVO-korgen product called in our system?"
AI: "It's called 'LIFVO-standard' in the SKU database."
Confidence: 60% (guessed)User Correction (Domain Expert)
Daniel (CEO, Eagle Fishing - 100% authority): "No, it's actually called 'LIFVO-bas' in our SKU system, not LIFVO-standard."
Leeloo: Correction detected!Knowledge Graph Update
Attribute: SKU_name
Old_value: "LIFVO-standard" (confidence: 60%, source: guessed)
New_value: "LIFVO-bas" (confidence: 100%, source: Daniel, timestamp: 2024-01-15)
Future AI Response (Corrected)
Different User (1 week later): "What's the LIFVO-korgen SKU name?"
AI (using Leeloo): "It's called 'LIFVO-bas' in the SKU database (verified by Daniel on 2024-01-15)."
Confidence: 100% (domain expert verified)Use Cases
Enterprise Knowledge
Build company-specific knowledge graphs from Slack, email, docs. Learn product names, processes, internal jargon.
Technical Documentation
Learn from code corrections, infrastructure changes, deployment processes. Keep AI up-to-date with latest tech stack.
Domain Expertise
Track who's an expert in what domains. Trust engineer corrections on tech, CEO on business, designer on UX.
Tech Stack
AI & NLP
- •GPT-4 (pattern detection)
- •spaCy (NER, entity extraction)
- •Custom ML models
- •Semantic similarity
Knowledge Graph
- •Neo4j (graph database)
- •Vector embeddings (pgvector)
- •Temporal tracking
- •Confidence scoring
Backend & API
- •FastAPI (Python)
- •GraphQL API
- •MCP Server integration
- •Real-time updates (WebSocket)
Current Status
Leeloo is an active research project exploring how AI systems can learn from corrections and build domain-specific knowledge graphs. We're developing the core algorithms and testing with real-world Eagle Fishing/Labs data.
Correction detection patterns, authority framework design
Knowledge graph integration, real-time learning pipeline
MCP server, Claude Code integration, production deployment
Intresserad av AI som lär sig från feedback?
Leeloo är ett forskningsprojekt, men vi kan diskutera liknande lösningar för ditt företag. Domain-specific AI knowledge graphs, continuous learning systems.