diff --git a/README.md b/README.md
index 5693a66f..ab59edcd 100644
--- a/README.md
+++ b/README.md
@@ -4,51 +4,22 @@
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![PyPI version](https://badge.fury.io/py/semanticore.svg)](https://badge.fury.io/py/semanticore)
 [![Downloads](https://pepy.tech/badge/semanticore)](https://pepy.tech/project/semanticore)
-[![Tests](https://github.com/yourusername/semanticore/workflows/Tests/badge.svg)](https://github.com/yourusername/semanticore/actions)
 
-**Transform Unstructured Data into Intelligent Semantic Layers for AI Systems**
+**Transform unstructured data into structured semantic layers for LLMs, Agents, RAG systems, and Knowledge Graphs.**
 
-SemantiCore is an open-source toolkit that transforms raw, unstructured data into semantic knowledge representations including ontologies, knowledge graphs, and context-aware embeddings. Built for developers creating AI agents, RAG systems, and intelligent applications that need to understand meaning, not just text.
+SemantiCore bridges the gap between raw unstructured data and intelligent AI systems by providing a comprehensive toolkit for semantic extraction, schema generation, and knowledge representation.
 
 ---
 
-## 🚀 Core Features Overview
+## 🌟 Why SemantiCore?
 
-### 🧠 **Semantic Processing**
-- **Multi-layer Understanding**: Lexical, syntactic, semantic, and pragmatic analysis
-- **Entity & Relationship Extraction**: Named entities, relationships, and complex event detection
-- **Context Preservation**: Maintain semantic context across document boundaries
-- **Domain Adaptation**: Specialized processing for cybersecurity, finance, healthcare, research
+Modern AI systems require structured, semantically rich data to perform effectively. SemantiCore solves the fundamental challenge of converting messy, unstructured information into clean, schema-compliant semantic layers that power:
 
-### 🎯 **LLM Optimization**
-- **Context Engineering**: Intelligent context compression and enhancement for LLMs
-- **Prompt Optimization**: Semantic-aware prompt engineering and optimization
-- **Memory Management**: Episodic, semantic, and procedural memory systems
-- **Multi-Model Support**: OpenAI, Anthropic, Google Gemini, Hugging Face, local models
-
-### 🕸️ **Knowledge Graphs**
-- **Automated Construction**: Build knowledge graphs from unstructured data
-- **Graph Databases**: Neo4j, KuzuDB, ArangoDB, Amazon Neptune integration
-- **Semantic Reasoning**: Inductive, deductive, and abductive reasoning capabilities
-- **Temporal Modeling**: Time-aware relationships and evolution tracking
-
-### 📊 **Vector & Embeddings**
-- **Contextual Embeddings**: Semantic embeddings with preserved context
-- **Vector Stores**: Pinecone, Milvus, Weaviate, Chroma, FAISS integration
-- **Hybrid Search**: Combine semantic and keyword search strategies
-- **Embedding Models**: OpenAI, Cohere, Sentence Transformers, custom models
-
-### 🔗 **Ontology Generation**
-- **Automated Ontology Creation**: Generate OWL/RDF ontologies from data
-- **Schema Evolution**: Dynamic schema adaptation and versioning
-- **Standard Compliance**: Schema.org, FIBO, domain-specific ontologies
-- **Multi-format Export**: OWL, RDF, JSON-LD, Turtle formats
-
-### 🤖 **Agent Integration**
-- **Semantic Routing**: Intelligent request routing based on semantic understanding
-- **Agent Orchestration**: Coordinate multiple AI agents with shared semantic context
-- **Framework Integration**: LangChain, LlamaIndex, CrewAI, AutoGen compatibility
-- **Real-time Processing**: Stream processing for live data semantic analysis
+- **🤖 Intelligent Agents** - With type-safe, validated input/output schemas
+- **🔍 RAG Systems** - Enhanced with semantic chunking and enriched metadata
+- **🕸️ Knowledge Graphs** - Automatically extracted entities, relations, and triples
+- **🛠️ LLM Tools** - Wrapped with semantic contracts for reliable operation
+- **📊 Data Pipelines** - Consistent, validated data flows across your stack
 
 ---
 
@@ -57,685 +28,545 @@ SemantiCore is an open-source toolkit that transforms raw, unstructured data int
 ### Installation
 
 ```bash
-# Basic installation
+# Install via pip
 pip install semanticore
 
-# Install with all integrations
+# Or install with all dependencies
 pip install "semanticore[all]"
 
-# Install specific providers
-pip install "semanticore[openai,anthropic,neo4j,pinecone]"
-
-# Available extras: openai, anthropic, google, huggingface, neo4j, kuzu, 
-# pinecone, milvus, weaviate, chroma, langchain, llamaindex, crewai
+# Development installation
+git clone https://github.com/yourusername/semanticore.git
+cd semanticore
+pip install -e ".[dev]"
 ```
 
-### 30-Second Demo
+### Basic Usage
 
 ```python
 from semanticore import SemantiCore
 
-# Initialize with your preferred providers
-core = SemantiCore(
-    llm_provider="openai",
-    embedding_model="text-embedding-3-large",
-    vector_store="pinecone",
-    graph_db="neo4j"
-)
+# Initialize the core engine
+core = SemantiCore()
 
-# Transform unstructured text into semantic knowledge
+# Extract semantic information from text
 text = """
-Tesla reported Q4 2024 earnings with $25.2B revenue, a 15% increase year-over-year.
-CEO Elon Musk highlighted the success of the Model Y and expansion in the Chinese market.
-The company plans to launch three new models in 2025, including the long-awaited Cybertruck.
+OpenAI released GPT-4 in March 2023, which significantly improved 
+reasoning capabilities over GPT-3.5. The model was trained using 
+reinforcement learning from human feedback (RLHF).
 """
 
-# Extract semantic information
-result = core.extract_semantics(text)
+# One-line semantic extraction
+result = core.extract(text)
 
-print("Entities:", result.entities)
-# [Entity(name="Tesla", type="ORGANIZATION"), Entity(name="Elon Musk", type="PERSON")]
-
-print("Relationships:", result.relationships) 
-# [Relation(subject="Tesla", predicate="reported", object="Q4 2024 earnings")]
-
-print("Events:", result.events)
-# [Event(type="EARNINGS_REPORT", date="Q4 2024", amount="$25.2B")]
-
-# Generate knowledge graph
-knowledge_graph = core.build_knowledge_graph(text)
-print("Graph nodes:", len(knowledge_graph.nodes))
-print("Graph edges:", len(knowledge_graph.edges))
+print(result.entities)     # [Entity(name="OpenAI", type="ORGANIZATION"), ...]
+print(result.relations)    # [Relation(subject="OpenAI", predicate="released", object="GPT-4"), ...]
+print(result.schema)       # Auto-generated Pydantic schema
+print(result.metadata)     # Enriched contextual information
 ```
 
 ---
 
-## 🔧 Integration Examples
+## 🧩 Core Features
 
-### 🤖 LLM Provider Integration
+### 🧠 Semantic Extraction Engine
+
+Advanced NLP pipeline that extracts meaningful structure from unstructured data:
 
 ```python
-from semanticore.llm import LLMProvider
+from semanticore.extract import EntityExtractor, RelationExtractor, TopicClassifier
 
-# OpenAI Integration
-openai_provider = LLMProvider(
-    provider="openai",
-    model="gpt-4-turbo",
-    api_key="your-openai-key"
+# Named Entity Recognition with custom models
+extractor = EntityExtractor(
+    model="en_core_web_trf",  # spaCy model
+    custom_labels=["MALWARE", "THREAT_ACTOR", "VULNERABILITY"]
 )
 
-# Anthropic Integration
-anthropic_provider = LLMProvider(
-    provider="anthropic", 
-    model="claude-3-opus-20240229",
-    api_key="your-anthropic-key"
-)
+entities = extractor.extract("APT29 used FrostBite malware against critical infrastructure")
 
-# Google Gemini Integration
-gemini_provider = LLMProvider(
-    provider="google",
-    model="gemini-pro",
-    api_key="your-google-key"
-)
+# Relation and Triple Extraction
+rel_extractor = RelationExtractor(llm_provider="openai")
+relations = rel_extractor.extract_relations(text, entities)
 
-# Hugging Face Integration
-hf_provider = LLMProvider(
-    provider="huggingface",
-    model="mistralai/Mistral-7B-Instruct-v0.1",
-    api_key="your-hf-key"
-)
-
-# Local Model Integration
-local_provider = LLMProvider(
-    provider="local",
-    model_path="/path/to/model",
-    device="cuda"
-)
-
-# Use with SemantiCore
-core = SemantiCore(llm_provider=openai_provider)
+# Topic Classification and Categorization
+classifier = TopicClassifier()
+topics = classifier.classify(text, categories=["cybersecurity", "technology", "politics"])
 ```
 
-### 🕸️ Knowledge Graph Database Integration
+### 🧱 Dynamic Schema Generation
+
+Automatically generate type-safe schemas from extracted data:
 
 ```python
-from semanticore.graph import GraphDatabase
+from semanticore.schema import SchemaGenerator, validate_data
 
-# Neo4j Integration
-neo4j_db = GraphDatabase(
-    provider="neo4j",
+# Generate Pydantic models from extracted entities
+generator = SchemaGenerator()
+schema = generator.from_entities(entities)
+
+# Export to various formats
+schema.to_pydantic()    # Python Pydantic model
+schema.to_json_schema() # JSON Schema
+schema.to_yaml()        # YAML Schema
+schema.to_typescript()  # TypeScript interfaces
+
+# Validate new data against generated schema
+is_valid = validate_data(new_data, schema)
+```
+
+### 🔌 Universal Connectors
+
+Seamlessly connect to any data source:
+
+```python
+from semanticore.connectors import FileConnector, WebConnector, APIConnector
+
+# File processing (PDF, DOCX, CSV, JSON, Markdown)
+file_conn = FileConnector()
+documents = file_conn.load("./documents/*.pdf")
+semantic_docs = core.process_documents(documents)
+
+# Web scraping and RSS feeds
+web_conn = WebConnector()
+pages = web_conn.scrape_urls(["https://example.com/news"])
+web_semantics = core.extract_from_web(pages)
+
+# REST API integration
+api_conn = APIConnector(base_url="https://api.example.com")
+api_data = api_conn.fetch("/endpoints")
+structured_data = core.structure_api_response(api_data)
+```
+
+### 🧪 Validation & Quality Assurance
+
+Ensure data quality and consistency across your pipeline:
+
+```python
+from semanticore.validation import SchemaValidator, ConsistencyChecker, QualityMetrics
+
+# Schema validation
+validator = SchemaValidator(schema)
+validation_result = validator.validate(data)
+
+if not validation_result.is_valid:
+    print(f"Validation errors: {validation_result.errors}")
+
+# Consistency checking across multiple extractions
+checker = ConsistencyChecker()
+consistency_score = checker.check_consistency([result1, result2, result3])
+
+# Quality metrics and confidence scoring
+metrics = QualityMetrics()
+quality_report = metrics.assess(extraction_result)
+print(f"Extraction confidence: {quality_report.confidence}")
+```
+
+### 📐 Intelligent Chunking & Embedding
+
+RAG-optimized document processing with semantic awareness:
+
+```python
+from semanticore.vectorizer import SemanticChunker, EmbeddingEngine
+
+# Semantic-aware chunking
+chunker = SemanticChunker(
+    chunk_size=512,
+    overlap=50,
+    respect_boundaries=True,  # Don't split entities/relations
+    add_metadata=True
+)
+
+chunks = chunker.chunk_document(document, semantic_info=result)
+
+# Multi-modal embedding support
+embedder = EmbeddingEngine(
+    provider="sentence-transformers",  # or "openai", "huggingface"
+    model="all-MiniLM-L6-v2"
+)
+
+embedded_chunks = embedder.embed_chunks(chunks)
+
+# Direct vector database integration
+from semanticore.vector_stores import FAISSStore, PineconeStore
+
+store = FAISSStore()
+store.add_embeddings(embedded_chunks)
+```
+
+### 📚 Knowledge Graph Export
+
+Transform extracted semantics into graph databases:
+
+```python
+from semanticore.kg import Neo4jExporter, RDFExporter, KuzuExporter
+
+# Neo4j export with Cypher generation
+neo4j_exporter = Neo4jExporter(
     uri="bolt://localhost:7687",
-    username="neo4j",
+    user="neo4j",
     password="password"
 )
 
-# KuzuDB Integration (Embedded Graph Database)
-kuzu_db = GraphDatabase(
-    provider="kuzu",
-    database_path="/path/to/kuzu/db"
-)
+# Create nodes and relationships
+neo4j_exporter.export_entities(entities)
+neo4j_exporter.export_relations(relations)
 
-# ArangoDB Integration
-arango_db = GraphDatabase(
-    provider="arangodb",
-    host="localhost",
-    port=8529,
-    username="root",
-    password="password"
-)
+# RDF triple export
+rdf_exporter = RDFExporter(format="turtle")
+triples = rdf_exporter.to_triples(entities, relations)
 
-# Amazon Neptune Integration
-neptune_db = GraphDatabase(
-    provider="neptune",
-    endpoint="your-neptune-endpoint.amazonaws.com",
-    port=8182,
-    region="us-east-1"
-)
+# Query the generated knowledge graph
+from semanticore.kg.query import GraphQuerier
 
-# Build knowledge graph
-from semanticore import SemantiCore
-
-core = SemantiCore(graph_db=neo4j_db)
-documents = ["doc1.txt", "doc2.txt", "doc3.txt"]
-
-# Automatically extract entities and relationships, build graph
-knowledge_graph = core.build_knowledge_graph_from_documents(documents)
-print(f"Created graph with {knowledge_graph.node_count} nodes and {knowledge_graph.edge_count} edges")
+querier = GraphQuerier(neo4j_exporter)
+results = querier.cypher("MATCH (n:ORGANIZATION)-[r:RELEASED]->(m:PRODUCT) RETURN n, r, m")
 ```
 
-### 📊 Vector Store Integration
+### 📡 Semantic Routing
+
+Intelligently route queries and tasks to appropriate handlers:
 
 ```python
-from semanticore.vector import VectorStore
+from semanticore.routing import SemanticRouter, IntentClassifier
 
-# Pinecone Integration
-pinecone_store = VectorStore(
-    provider="pinecone",
-    api_key="your-pinecone-key",
-    environment="us-west1-gcp",
-    index_name="semanticore-index"
+# Set up routing rules
+router = SemanticRouter()
+
+# Intent-based routing
+router.add_intent_route("question_answering", qa_agent)
+router.add_intent_route("data_extraction", extraction_pipeline)
+router.add_intent_route("summarization", summary_agent)
+
+# Keyword and pattern-based routing
+router.add_keyword_route(["threat", "malware", "vulnerability"], security_agent)
+router.add_pattern_route(r"CVE-\d{4}-\d+", vulnerability_lookup)
+
+# LLM-powered semantic routing
+router.add_semantic_route(
+    description="Handle complex analytical queries about financial data",
+    handler=financial_analysis_agent,
+    examples=["What's the trend in quarterly revenue?", "Analyze the risk factors"]
 )
 
-# Milvus Integration
-milvus_store = VectorStore(
-    provider="milvus",
-    host="localhost",
-    port=19530,
-    collection_name="semantic_embeddings"
-)
-
-# Weaviate Integration
-weaviate_store = VectorStore(
-    provider="weaviate",
-    url="http://localhost:8080",
-    class_name="SemanticChunk"
-)
-
-# Chroma Integration
-chroma_store = VectorStore(
-    provider="chroma",
-    persist_directory="/path/to/chroma/db",
-    collection_name="documents"
-)
-
-# FAISS Integration (Local)
-faiss_store = VectorStore(
-    provider="faiss",
-    index_path="/path/to/faiss/index",
-    dimension=1536
-)
-
-# Use with SemantiCore for RAG
-core = SemantiCore(
-    vector_store=pinecone_store,
-    embedding_model="text-embedding-3-large"
-)
-
-# Semantic chunking and embedding
-chunks = core.semantic_chunk_documents(documents)
-embeddings = core.embed_chunks(chunks)
-vector_store.store_embeddings(chunks, embeddings)
-
-# Semantic search
-query = "What are the latest AI developments?"
-results = core.semantic_search(query, top_k=5)
-```
-
-### 🔗 Framework Integration
-
-```python
-# LangChain Integration
-from semanticore.integrations.langchain import SemanticChain
-from langchain.chains import ConversationalRetrievalChain
-
-semantic_chain = SemanticChain(
-    semanticore_instance=core,
-    retriever_type="semantic",
-    context_engineering=True
-)
-
-langchain_chain = ConversationalRetrievalChain(
-    retriever=semantic_chain.as_retriever(),
-    memory=semantic_chain.get_memory(),
-    return_source_documents=True
-)
-
-# LlamaIndex Integration
-from semanticore.integrations.llamaindex import SemanticIndex
-from llama_index import VectorStoreIndex
-
-semantic_index = SemanticIndex(
-    semanticore_instance=core,
-    enable_semantic_routing=True
-)
-
-llama_index = VectorStoreIndex.from_vector_store(
-    semantic_index.get_vector_store()
-)
-
-# CrewAI Integration
-from semanticore.integrations.crewai import SemanticCrew
-from crewai import Agent, Task, Crew
-
-# Create semantic-aware agents
-researcher = Agent(
-    role='Research Analyst',
-    goal='Analyze semantic patterns in data',
-    backstory='Expert in semantic data analysis',
-    semantic_memory=core.get_semantic_memory()
-)
-
-writer = Agent(
-    role='Content Writer',
-    goal='Create semantic-rich content',
-    backstory='Specialist in semantic content creation',
-    semantic_memory=core.get_semantic_memory()
-)
-
-# Create semantic crew
-semantic_crew = SemanticCrew(
-    agents=[researcher, writer],
-    semantic_coordination=True,
-    knowledge_sharing=True
-)
+# Route incoming requests
+query = "What are the latest cybersecurity threats targeting healthcare?"
+handler = router.route(query)
+response = handler.process(query)
 ```
 
 ---
 
-## 🎯 Advanced Features
+## 🎯 Use Cases & Examples
 
-### 🧠 Multi-Domain Semantic Processing
+### 🔐 Cybersecurity Threat Intelligence
 
 ```python
-from semanticore.domains import CybersecurityProcessor, FinanceProcessor, HealthcareProcessor
+from semanticore.domains.cyber import ThreatIntelExtractor
 
-# Cybersecurity semantic processing
-cyber_processor = CybersecurityProcessor(
-    threat_intelligence_feeds=["misp", "stix"],
-    ontology="cybersecurity.owl",
-    enable_threat_hunting=True
-)
-
-# Process security incidents
-incident_report = """
-APT29 exploited CVE-2024-1234 in Microsoft Exchange to deploy Cobalt Strike.
-The attack used spear-phishing emails with malicious attachments.
+# Specialized cybersecurity extraction
+threat_extractor = ThreatIntelExtractor()
+threat_report = """
+APT29 (Cozy Bear) launched a sophisticated spear-phishing campaign 
+targeting US government agencies using a previously unknown malware 
+variant called FrostBite. The attack exploited CVE-2024-1234 in 
+Microsoft Exchange servers.
 """
 
-cyber_analysis = cyber_processor.analyze(incident_report)
-print("Threat Actors:", cyber_analysis.threat_actors)
-print("Vulnerabilities:", cyber_analysis.vulnerabilities)
-print("Attack Techniques:", cyber_analysis.mitre_techniques)
+intel = threat_extractor.extract(threat_report)
+print(intel.threat_actors)    # ["APT29", "Cozy Bear"]
+print(intel.malware)          # ["FrostBite"]
+print(intel.vulnerabilities)  # ["CVE-2024-1234"]
+print(intel.attack_patterns)  # ["spear-phishing", "server exploitation"]
 
-# Financial semantic processing
-finance_processor = FinanceProcessor(
-    market_data_sources=["yahoo", "alpha_vantage"],
-    ontology="finance.owl",
-    enable_sentiment_analysis=True
-)
-
-# Healthcare semantic processing
-health_processor = HealthcareProcessor(
-    medical_ontologies=["snomed", "icd10"],
-    enable_drug_interaction_detection=True
-)
+# Export to STIX format for threat intelligence platforms
+stix_bundle = intel.to_stix()
 ```
 
-### 🎯 Context Engineering for RAG
+### 🧬 Biomedical Research Assistant
 
 ```python
-from semanticore.context import ContextEngineer
+from semanticore.domains.biomedical import BiomedicalExtractor
 
-# Advanced context engineering
-context_engineer = ContextEngineer(
-    max_context_length=128000,
-    compression_strategy="semantic_preservation",
-    relevance_scoring=True
-)
+bio_extractor = BiomedicalExtractor()
+research_text = """
+The study investigated the efficacy of remdesivir in treating COVID-19 
+patients. Results showed a 31% reduction in recovery time compared to 
+placebo (p<0.001). Side effects included nausea in 12% of patients.
+"""
 
-# Optimize context for specific queries
-query = "How can we improve cloud security against APT attacks?"
-documents = load_security_documents()
+bio_data = bio_extractor.extract(research_text)
+print(bio_data.drugs)         # ["remdesivir"]
+print(bio_data.conditions)    # ["COVID-19"]
+print(bio_data.outcomes)      # ["31% reduction in recovery time"]
+print(bio_data.side_effects)  # ["nausea"]
 
-# Intelligent context compression
-optimized_context = context_engineer.optimize_context(
-    query=query,
-    documents=documents,
-    preserve_entities=True,
-    maintain_relationships=True,
-    compression_ratio=0.3  # 70% reduction while preserving meaning
-)
-
-print(f"Context compressed from {len(documents)} to {len(optimized_context)} tokens")
-print(f"Semantic preservation: {context_engineer.preservation_score:.2%}")
+# Generate structured clinical data
+clinical_schema = bio_data.to_clinical_schema()
 ```
 
-### 🔄 Real-time Semantic Processing
+### 📊 Financial Document Analysis
 
 ```python
-from semanticore.streaming import SemanticStreamProcessor
+from semanticore.domains.finance import FinancialExtractor
 
-# Real-time semantic processing
-stream_processor = SemanticStreamProcessor(
-    input_streams=["kafka://events", "websocket://feeds"],
-    processing_pipeline=[
-        "entity_extraction",
-        "relationship_detection", 
-        "ontology_mapping",
-        "knowledge_graph_update"
-    ],
-    batch_size=100,
-    processing_interval="5s"
-)
+fin_extractor = FinancialExtractor()
+earnings_report = """
+Q4 2024 revenue increased 15% YoY to $2.3B, driven by strong performance 
+in the cloud computing segment. Operating margin improved to 23.5% from 
+21.2% in the prior year. The company announced a $1B share buyback program.
+"""
 
-# Process streaming data
-async for semantic_event in stream_processor.process():
-    if semantic_event.confidence > 0.8:
-        # Update knowledge graph
-        core.update_knowledge_graph(semantic_event)
-        
-        # Trigger alerts if needed
-        if semantic_event.importance == "critical":
-            await alert_system.send_alert(semantic_event)
-```
+financial_data = fin_extractor.extract(earnings_report)
+print(financial_data.metrics)     # {"revenue": "$2.3B", "margin": "23.5%"}
+print(financial_data.periods)     # ["Q4 2024"]
+print(financial_data.events)      # ["$1B share buyback program"]
 
-### 🔀 Semantic Routing & Orchestration
-
-```python
-from semanticore.routing import SemanticRouter
-
-# Multi-dimensional semantic routing
-router = SemanticRouter(
-    routing_dimensions=["intent", "domain", "complexity", "urgency"],
-    agents={
-        "security_analyst": SecurityAgent(),
-        "data_scientist": DataScienceAgent(),
-        "business_analyst": BusinessAgent()
-    }
-)
-
-# Route queries to appropriate agents
-query = "Analyze the security implications of our latest data breach"
-routed_agent = router.route_query(query)
-response = routed_agent.process(query)
+# Export to financial analysis tools
+financial_json = financial_data.to_standardized_json()
 ```
 
 ---
 
-## 🏗️ Architecture & Deployment
+## 🔧 Advanced Configuration
 
-### 🏢 Enterprise Architecture
+### Custom Model Integration
 
 ```python
-from semanticore.enterprise import SemanticEnterprise
+from semanticore.models import CustomLLMProvider
 
-# Enterprise-grade deployment
-enterprise = SemanticEnterprise(
-    deployment_mode="distributed",
-    scaling_strategy="auto",
-    monitoring_enabled=True,
-    security_features=[
-        "encryption_at_rest",
-        "encryption_in_transit", 
-        "access_control",
-        "audit_logging"
-    ]
-)
+# Integrate your own models
+class MyCustomLLM(CustomLLMProvider):
+    def __init__(self, model_path):
+        self.model = load_model(model_path)
+    
+    def extract_entities(self, text):
+        return self.model.predict(text)
 
-# Multi-tenant configuration
-enterprise.configure_tenants({
-    "healthcare_org": {
-        "compliance": ["hipaa", "gdpr"],
-        "ontology": "healthcare.owl",
-        "data_classification": "sensitive"
-    },
-    "finance_org": {
-        "compliance": ["sox", "pci_dss"],
-        "ontology": "finance.owl", 
-        "data_classification": "confidential"
-    }
-})
+# Use custom model in SemantiCore
+core = SemantiCore(llm_provider=MyCustomLLM("./my_model"))
 ```
 
-### ☁️ Cloud Deployment
-
-```yaml
-# docker-compose.yml
-version: '3.8'
-services:
-  semanticore:
-    image: semanticore:latest
-    environment:
-      - SEMANTICORE_MODE=production
-      - OPENAI_API_KEY=${OPENAI_API_KEY}
-      - NEO4J_URI=${NEO4J_URI}
-      - PINECONE_API_KEY=${PINECONE_API_KEY}
-    ports:
-      - "8000:8000"
-    volumes:
-      - ./ontologies:/app/ontologies
-      - ./models:/app/models
-    depends_on:
-      - neo4j
-      - redis
-      
-  neo4j:
-    image: neo4j:latest
-    environment:
-      - NEO4J_AUTH=neo4j/password
-    ports:
-      - "7474:7474"
-      - "7687:7687"
-      
-  redis:
-    image: redis:alpine
-    ports:
-      - "6379:6379"
-```
-
-### 🚀 Kubernetes Deployment
-
-```yaml
-# k8s/deployment.yaml
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: semanticore
-spec:
-  replicas: 3
-  selector:
-    matchLabels:
-      app: semanticore
-  template:
-    metadata:
-      labels:
-        app: semanticore
-    spec:
-      containers:
-      - name: semanticore
-        image: semanticore:latest
-        ports:
-        - containerPort: 8000
-        env:
-        - name: SEMANTICORE_MODE
-          value: "production"
-        - name: DISTRIBUTED_PROCESSING
-          value: "true"
-        resources:
-          requests:
-            memory: "2Gi"
-            cpu: "1"
-          limits:
-            memory: "4Gi"
-            cpu: "2"
-```
-
----
-
-## 🎓 Examples & Use Cases
-
-### 🔐 Cybersecurity Intelligence
+### Pipeline Customization
 
 ```python
-# Threat intelligence analysis
-threat_data = """
-New malware family 'StealthBot' discovered targeting financial institutions.
-Uses advanced evasion techniques and communicates with C2 servers via encrypted channels.
-Initial infection vector appears to be phishing emails with malicious PDF attachments.
-"""
+from semanticore.pipeline import Pipeline, Step
 
-# Extract threat intelligence
-threat_analysis = core.extract_threat_intelligence(threat_data)
-print("Malware Family:", threat_analysis.malware_families)
-print("Attack Vectors:", threat_analysis.attack_vectors)
-print("Indicators:", threat_analysis.iocs)
+# Build custom processing pipeline
+pipeline = Pipeline([
+    Step("preprocess", text_cleaner),
+    Step("extract_entities", entity_extractor),
+    Step("extract_relations", relation_extractor),
+    Step("enrich_metadata", metadata_enricher),
+    Step("validate", schema_validator),
+    Step("export", knowledge_graph_exporter)
+])
 
-# Update threat knowledge graph
-core.update_threat_landscape(threat_analysis)
+# Process data through pipeline
+results = pipeline.run(input_data)
 ```
 
-### 📊 Financial Analysis
+### Configuration Management
 
 ```python
-# Market sentiment analysis
-financial_news = """
-Tesla's Q4 earnings beat expectations with record deliveries. 
-Stock surged 12% in after-hours trading as investors responded positively 
-to the company's guidance for 2025 production targets.
-"""
-
-# Extract financial insights
-financial_analysis = core.extract_financial_semantics(financial_news)
-print("Companies:", financial_analysis.companies)
-print("Financial Metrics:", financial_analysis.metrics)
-print("Sentiment:", financial_analysis.sentiment)
-print("Market Impact:", financial_analysis.market_impact)
-```
-
-### 🧬 Research Intelligence
-
-```python
-# Scientific literature analysis
-research_paper = """
-Our study demonstrates that CRISPR-Cas9 gene editing can effectively 
-target oncogenes in pancreatic cancer cells, showing 85% reduction 
-in tumor growth in mouse models.
-"""
-
-# Extract research insights
-research_analysis = core.extract_research_semantics(research_paper)
-print("Techniques:", research_analysis.techniques)
-print("Findings:", research_analysis.findings)
-print("Entities:", research_analysis.biological_entities)
-print("Relationships:", research_analysis.causal_relationships)
-```
-
----
-
-## 🛠️ Configuration
-
-### ⚙️ Configuration File
-
-```yaml
 # semanticore.yaml
-llm:
-  provider: "openai"
-  model: "gpt-4-turbo"
-  api_key: "$OPENAI_API_KEY"
-  temperature: 0.1
-  max_tokens: 4000
-
-embeddings:
-  provider: "openai"
-  model: "text-embedding-3-large"
-  dimensions: 1536
-
-vector_store:
-  provider: "pinecone"
-  api_key: "$PINECONE_API_KEY"
-  environment: "us-west1-gcp"
-  index_name: "semanticore"
-
-graph_database:
-  provider: "neo4j"
-  uri: "bolt://localhost:7687"
-  username: "neo4j"
-  password: "$NEO4J_PASSWORD"
-
-processing:
-  semantic_layers: ["lexical", "syntactic", "semantic", "pragmatic"]
-  enable_coreference_resolution: true
-  enable_temporal_reasoning: true
-  enable_causal_reasoning: true
-
-ontology:
+extractors:
+  entity:
+    model: "en_core_web_trf"
+    confidence_threshold: 0.8
+  relation:
+    llm_provider: "openai"
+    model: "gpt-4"
+    
+schema:
   auto_generate: true
-  formats: ["owl", "rdf", "json-ld"]
-  validation: true
-  versioning: true
-```
+  validation_level: "strict"
+  
+export:
+  formats: ["json", "rdf", "cypher"]
+  knowledge_graph:
+    provider: "neo4j"
+    batch_size: 1000
 
-### 🔧 Environment Variables
-
-```bash
-# Core configuration
-export SEMANTICORE_MODE=production
-export SEMANTICORE_LOG_LEVEL=info
-
-# LLM providers
-export OPENAI_API_KEY=your_openai_key
-export ANTHROPIC_API_KEY=your_anthropic_key
-export GOOGLE_API_KEY=your_google_key
-
-# Vector stores
-export PINECONE_API_KEY=your_pinecone_key
-export MILVUS_HOST=localhost
-export MILVUS_PORT=19530
-
-# Graph databases
-export NEO4J_URI=bolt://localhost:7687
-export NEO4J_USERNAME=neo4j
-export NEO4J_PASSWORD=your_password
-
-# Optional: Enable specific features
-export ENABLE_REAL_TIME_PROCESSING=true
-export ENABLE_SEMANTIC_CACHING=true
-export ENABLE_DISTRIBUTED_PROCESSING=true
+# Load configuration
+from semanticore.config import load_config
+config = load_config("semanticore.yaml")
+core = SemantiCore(config=config)
 ```
 
 ---
 
-## 📚 Documentation & Resources
+## 🏗️ Architecture
 
-- **📖 [Full Documentation](https://docs.semanticore.ai)**
-- **🚀 [Quick Start Guide](https://docs.semanticore.ai/quickstart)**
-- **🏗️ [Architecture Overview](https://docs.semanticore.ai/architecture)**
-- **🔧 [API Reference](https://api.semanticore.ai)**
-- **💡 [Examples Repository](https://github.com/semanticore/examples)**
-- **🌐 [Community Forum](https://community.semanticore.ai)**
-- **📺 [Video Tutorials](https://youtube.com/semanticore)**
+SemantiCore follows a modular, extensible architecture:
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│                    SemantiCore Engine                        │
+├─────────────────────────────────────────────────────────────┤
+│  Connectors    │  Extractors   │  Schema      │  Exporters   │
+│  ┌──────────┐  │  ┌─────────┐  │  ┌────────┐  │  ┌────────┐  │
+│  │   File   │  │  │   NER   │  │  │ Pydantic│  │  │ Neo4j  │  │
+│  │   Web    │  │  │ Relations│  │  │  JSON   │  │  │  RDF   │  │
+│  │   API    │  │  │  Topics  │  │  │  YAML   │  │  │ Vector │  │
+│  │   DB     │  │  │   LLM   │  │  │   TS    │  │  │  DB    │  │
+│  └──────────┘  │  └─────────┘  │  └────────┘  │  └────────┘  │
+├─────────────────────────────────────────────────────────────┤
+│              Validation & Quality Assurance                 │
+├─────────────────────────────────────────────────────────────┤
+│                  Semantic Routing                           │
+└─────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## 📋 Requirements
+
+- **Python**: 3.8+
+- **Core Dependencies**: spaCy, transformers, pydantic, networkx
+- **Optional Dependencies**:
+  - **LLM Providers**: openai, anthropic, huggingface-hub
+  - **Vector Databases**: faiss-cpu, pinecone-client, weaviate-client
+  - **Graph Databases**: neo4j, rdflib, kuzudb
+  - **Document Processing**: PyMuPDF, python-docx, openpyxl
+
+---
+
+## 🛣️ Roadmap
+
+### 🚀 Version 1.0 (Current)
+- ✅ Core semantic extraction engine
+- ✅ Schema generation and validation
+- ✅ Basic connectors (file, web, API)
+- ✅ Neo4j and RDF export
+- ✅ Vector database integration
+
+### 🔮 Version 1.1 (Q3 2025)
+- 🔄 **Web-based visual schema editor**
+- 🔄 **Real-time streaming support** (Kafka, MQTT, WebSockets)
+- 🔄 **Advanced semantic routing** with learning capabilities
+- 🔄 **Multi-modal support** (images, audio, video)
+
+### 🔮 Version 1.2 (Q4 2025)
+- ⏳ **Domain-specific modules** (Legal, Healthcare, Finance)
+- ⏳ **Graph reasoning engine** with inference capabilities
+- ⏳ **Distributed processing** support
+- ⏳ **Model fine-tuning** utilities
+
+### 🔮 Version 2.0 (2026)
+- ⏳ **Custom DSL** for semantic pipeline definition
+- ⏳ **AutoML** for extraction model optimization
+- ⏳ **Federated learning** across distributed deployments
+- ⏳ **Enterprise management** console
+
+---
 
 ## 🤝 Contributing
 
-We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
+We welcome contributions from the community! Here's how you can help:
 
-### 🛣️ Development Roadmap
+### 🐛 Report Issues
+Found a bug or have a feature request? [Open an issue](https://github.com/yourusername/semanticore/issues) on GitHub.
 
-**v1.0 (Current)**
-- ✅ Core semantic processing engine
-- ✅ Multi-LLM integration (OpenAI, Anthropic, Google)
-- ✅ Knowledge graph construction (Neo4j, KuzuDB)
-- ✅ Vector store integration (Pinecone, Milvus, Weaviate)
-- ✅ Ontology generation and management
+### 💻 Contribute Code
+1. Fork the repository
+2. Create a feature branch: `git checkout -b feature/amazing-feature`
+3. Make your changes and add tests
+4. Run the test suite: `pytest`
+5. Commit your changes: `git commit -m 'Add amazing feature'`
+6. Push to the branch: `git push origin feature/amazing-feature`
+7. Open a Pull Request
 
-**v1.1 (Next)**
-- 🔄 Multimodal processing (images, audio, video)
-- 🔄 Advanced reasoning capabilities
-- 🔄 Real-time streaming processing
-- 🔄 Enhanced enterprise features
+### 📖 Improve Documentation
+Help us improve our documentation by:
+- Fixing typos and clarifying explanations
+- Adding new examples and use cases
+- Creating tutorials and guides
+- Translating documentation
 
-**v1.2 (Future)**
-- 🔄 Federated learning capabilities
-- 🔄 Quantum-inspired semantic processing
-- 🔄 Advanced causal reasoning
-- 🔄 Autonomous semantic agents
-
-## 📄 License
-
-SemantiCore is released under the MIT License. See [LICENSE](LICENSE) for details.
-
-## 🙏 Acknowledgments
-
-- Built with ❤️ by the open-source community
-- Inspired by the latest advances in semantic AI and knowledge representation
-- Powered by cutting-edge LLM and embedding technologies
+### 🧪 Testing
+Help us maintain quality by:
+- Writing unit tests for new features
+- Testing on different platforms and Python versions
+- Performance testing and optimization
+- Integration testing with external services
 
 ---
 
-**Ready to transform your unstructured data into intelligent semantic knowledge?** 
+## 📚 Documentation
+
+- **📖 [Full Documentation](https://semanticore.readthedocs.io/)**
+- **🚀 [Quick Start Guide](https://semanticore.readthedocs.io/quickstart/)**
+- **📋 [API Reference](https://semanticore.readthedocs.io/api/)**
+- **💡 [Examples & Tutorials](https://semanticore.readthedocs.io/examples/)**
+- **🔧 [Configuration Guide](https://semanticore.readthedocs.io/configuration/)**
+
+---
+
+## 🏆 Community & Support
+
+- **💬 [Discord Community](https://discord.gg/semanticore)** - Chat with users and developers
+- **📧 [Mailing List](https://groups.google.com/g/semanticore)** - Stay updated with announcements
+- **🐦 [Twitter](https://twitter.com/semanticore)** - Follow us for updates
+- **📺 [YouTube Channel](https://youtube.com/c/semanticore)** - Tutorials and demos
+- **❓ [Stack Overflow](https://stackoverflow.com/questions/tagged/semanticore)** - Get help with specific issues
+
+---
+
+## 📄 License
+
+SemantiCore is released under the **MIT License**. See the [LICENSE](LICENSE) file for details.
+
+```
+MIT License
+
+Copyright (c) 2025 SemantiCore Contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
+
+---
+
+## 🙏 Acknowledgments
+
+SemantiCore is built on the shoulders of giants. We thank the communities behind:
+
+- **🤗 Hugging Face** - For democratizing NLP and ML
+- **🌶️ spaCy** - For industrial-strength NLP
+- **🔗 Neo4j** - For graph database excellence
+- **🐍 Python** - For being an amazing ecosystem
+- **🧠 OpenAI & Anthropic** - For advancing AI capabilities
+
+---
+
+## 📊 Project Stats
+
+![GitHub stars](https://img.shields.io/github/stars/yourusername/semanticore?style=social)
+![GitHub forks](https://img.shields.io/github/forks/yourusername/semanticore?style=social)
+![GitHub issues](https://img.shields.io/github/issues/yourusername/semanticore)
+![GitHub pull requests](https://img.shields.io/github/issues-pr/yourusername/semanticore)
+![PyPI downloads](https://img.shields.io/pypi/dm/semanticore)
+
+---
+
+**Ready to transform your unstructured data into intelligent, semantic knowledge?**
 
 ```bash
 pip install semanticore
 ```
 
-**Get started in 30 seconds →** [Quick Start Guide](https://docs.semanticore.ai/quickstart)
+**[Get Started Now →](https://semanticore.readthedocs.io/quickstart/)**