CCProxy

Appearance

Local AI for Complete Privacy: A Guide for Privacy-Conscious Professionals ​

Published on July 10, 2025

Whether you're a healthcare worker handling patient data, a legal professional managing confidential cases, a researcher working with sensitive information, or simply someone who values privacy, local AI offers a compelling solution. This guide explores how to use Ollama and Claude Code through CCProxy to get AI assistance that never leaves your device—perfect for professionals who can't afford to risk data exposure.

Understanding Local AI and Why It Matters ​

What is Local AI? ​

Local AI means running artificial intelligence models directly on your own device instead of sending data to external servers. Think of it like having a personal AI assistant that lives entirely on your computer—nothing you type or process ever leaves your machine.

Why Privacy-Conscious Professionals Need Local AI ​

In many professions, data exposure isn't just a concern—it's a career-ending risk:

Healthcare Professionals:

  • Patient health information (PHI) protected by HIPAA
  • Medical research data and clinical trial information
  • Diagnostic algorithms and treatment protocols
  • Hospital system integrations and patient workflows

Legal Professionals:

  • Client confidentiality protected by attorney-client privilege
  • Case strategies and legal arguments
  • Financial documents and settlement negotiations
  • Regulatory compliance documentation

Researchers and Academics:

  • Unpublished research data and findings
  • Grant applications and funding strategies
  • Student information and academic records
  • Institutional knowledge and methodologies

Enterprise Workers:

  • Trade secrets and proprietary information
  • Customer data and business strategies
  • Financial models and competitive intelligence
  • Internal communications and strategic planning

Students and Learners:

  • Personal academic work and research
  • Thesis and dissertation content
  • Learning materials and study notes
  • Career development and job search activities

The Hidden Costs of Cloud AI ​

When you use cloud-based AI services, you're typically agreeing to terms that allow:

  • Data analysis for service improvement
  • Potential human review of conversations
  • Data storage on third-party servers
  • Compliance with various jurisdictions' data laws

For privacy-conscious professionals, these risks are simply unacceptable.

How Ollama Solves the Privacy Problem ​

What is Ollama? ​

Ollama is a free, open-source tool that lets you run large language models (LLMs) locally on your computer. Think of it as your personal AI server that:

  • Never sends data externally - Everything stays on your device
  • Works offline - No internet connection required after setup
  • Runs on regular hardware - Works on laptops, desktops, and servers
  • Supports multiple models - Choose from various AI models for different tasks
  • Costs nothing to use - No subscription fees or per-request charges

The Privacy Guarantee ​

With Ollama, you get true privacy because:

Your Input → Ollama (Local) → AI Model (Local) → Response → You

Compare this to cloud AI services:

Your Input → Internet → Third-Party Server → AI Model → Response → Internet → You

Your sensitive data never leaves your control.

Understanding Claude Code + CCProxy: The Bridge to Local AI ​

What is Claude Code? ​

Claude Code is Anthropic's official command-line interface that provides AI-powered assistance for coding and text work. Normally, it connects to Anthropic's servers, but with CCProxy, you can redirect it to use your local Ollama models instead.

What is CCProxy? ​

CCProxy acts as a bridge between Claude Code and your local Ollama installation. It:

  • Translates requests - Converts Claude Code requests to work with Ollama
  • Maintains compatibility - Lets you use Claude Code commands with local models
  • Handles routing - Directs your requests to local models instead of external servers
  • Preserves privacy - Ensures no data leaves your machine

How It All Works Together ​

Claude Code → CCProxy → Ollama → Local AI Model → Response → You

This setup gives you:

  • The familiar Claude Code interface you may already know
  • Complete privacy with local processing
  • No subscription costs or rate limits
  • Offline capability for secure environments

Choosing the Right Model for Your Needs ​

Ollama supports various models optimized for different tasks:

For General Text Work:

  • Llama 3 (8B): Great balance of capability and speed
  • Mistral (7B): Efficient and reliable for most tasks
  • Gemma (7B): Good for analysis and writing

For Code and Technical Work:

  • CodeLlama: Specialized for programming tasks
  • DeepSeek Coder: Advanced coding capabilities
  • Qwen2.5-Coder: Excellent for multiple programming languages

For Specialized Fields:

  • Llama 3 (70B): Maximum capability for complex analysis (requires more resources)
  • Mistral (22B): Strong reasoning for research and analysis

💡 Claude Code Pro Tip: Start with Llama 3 8B for general use—it provides excellent performance on most tasks while being efficient enough for laptops.

Step-by-Step Setup Guide ​

Prerequisites ​

Before we begin, ensure you have:

  • A computer running Windows, macOS, or Linux
  • At least 8GB of RAM (16GB recommended for larger models)
  • A few GB of free disk space for models
  • Basic comfort with the command line

Step 1: Install Ollama ​

On macOS:

bash
# Download and install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Or download from https://ollama.com/download

On Windows: Download the installer from https://ollama.com/download and run it.

On Linux:

bash
curl -fsSL https://ollama.com/install.sh | sh

Step 2: Download Your First AI Model ​

Start with a general-purpose model that works well for most tasks:

bash
# Download Llama 3 8B (recommended for beginners)
ollama pull llama3:8b

# This will download about 4.7GB of data
# The download happens once, then the model is stored locally

💡 Claude Code Pro Tip: The first model download takes some time, but subsequent uses are instant since the model is stored locally.

Step 3: Test Ollama ​

Verify Ollama is working:

bash
# Start a conversation with your local model
ollama run llama3:8b

# Try typing: "Hello! Can you help me write a professional email?"
# Type /bye to exit

Step 4: Install CCProxy ​

CCProxy bridges Claude Code to your local Ollama models:

bash
# Install CCProxy
curl -sSL https://raw.githubusercontent.com/orchestre-dev/ccproxy/main/install.sh | bash

# Or manually download from GitHub releases

Step 5: Configure CCProxy for Local AI ​

bash
# Set up environment variables
export PROVIDER=ollama
export OLLAMA_MODEL=llama3:8b
export OLLAMA_HOST=http://localhost:11434

# Start CCProxy in privacy mode
ccproxy --privacy-mode=maximum \
        --offline-mode=enabled \
        --local-only=true &

Step 6: Install and Configure Claude Code ​

bash
# Install Claude Code (if not already installed)
# Visit https://claude.ai/code for installation instructions

# Configure Claude Code to use your local setup
export ANTHROPIC_BASE_URL=http://localhost:3456
export ANTHROPIC_API_KEY=dummy  # CCProxy handles local routing

Step 7: Test Your Private AI Setup ​

bash
# Try your first private AI conversation
claude "Help me write a professional email to a client"

# Or for code assistance
claude "Explain this Python function: def fibonacci(n): return n if n <= 1 else fibonacci(n-1) + fibonacci(n-2)"

🔐 Privacy Verification: Your data never leaves your machine. You can verify this by disconnecting from the internet and seeing that everything still works perfectly.

Advanced Model Configuration ​

bash
# Download multiple models for different tasks
ollama pull codellama:7b-instruct    # Fast code completion
ollama pull codellama:13b-instruct   # Balanced performance
ollama pull codellama:34b-instruct   # Maximum capability

# Configure CCProxy for intelligent model selection
export OLLAMA_FAST_MODEL=codellama:7b-instruct
export OLLAMA_BALANCED_MODEL=codellama:13b-instruct
export OLLAMA_CAPABLE_MODEL=codellama:34b-instruct

# Enable automatic model selection
ccproxy --auto-model-selection=enabled \
        --task-optimization=enabled

Real-World Applications for Privacy-Conscious Professionals ​

Healthcare Workers: HIPAA-Compliant AI Assistance ​

Dr. Sarah, Emergency Medicine Physician:

bash
# Analyze patient symptoms without exposing PHI
claude "Help me create a differential diagnosis checklist for chest pain in elderly patients"

# Draft documentation templates
claude "Create a template for discharge instructions for patients with mild concussion"

# Research medical literature privately
claude "Summarize the latest treatment protocols for sepsis management"

Benefits for Healthcare:

  • Zero risk of PHI exposure to third parties
  • Compliance with HIPAA requirements
  • Offline capability for secure hospital networks
  • Custom medical knowledge integration possible

Attorney Mike, Corporate Law:

bash
# Analyze contracts without client data exposure
claude "Help me identify potential issues in this non-disclosure agreement template"

# Draft legal documents
claude "Create a checklist for due diligence in mergers and acquisitions"

# Research case law and precedents
claude "Explain the implications of recent changes in data privacy regulations"

Benefits for Legal:

  • Complete protection of client confidentiality
  • No risk of inadvertent disclosure
  • Secure document analysis and drafting
  • Offline research capabilities

Researchers: Protecting Intellectual Property ​

Dr. Jennifer, Biomedical Research:

bash
# Analyze research data privately
claude "Help me design a statistical analysis plan for this clinical trial"

# Draft grant proposals
claude "Create an outline for a research proposal on novel cancer therapies"

# Literature review assistance
claude "Summarize key findings from these research abstracts"

Benefits for Researchers:

  • Protection of unpublished findings
  • Secure analysis of sensitive data
  • No risk of research theft
  • Offline capability for secure facilities

Students: Academic Integrity and Privacy ​

Emma, Graduate Student:

bash
# Get writing assistance without plagiarism concerns
claude "Help me improve the structure of this thesis chapter"

# Research methodology support
claude "Explain the pros and cons of different survey design approaches"

# Data analysis assistance
claude "Help me interpret these statistical results"

Benefits for Students:

  • Original work remains private
  • No academic integrity violations
  • Personalized learning assistance
  • Cost-effective compared to tutoring

Educational Workflows: Learning with Local AI ​

Understanding Complex Topics:

bash
# Break down difficult concepts
claude "Explain machine learning in simple terms for a business audience"
claude "What are the key differences between qualitative and quantitative research?"

# Generate practice questions
claude "Create 5 practice questions about constitutional law"
claude "Help me create a study guide for organic chemistry"

Research and Analysis Training:

bash
# Learn research methodologies
claude "Walk me through the steps of conducting a literature review"
claude "How do I evaluate the credibility of research sources?"

# Practice critical thinking
claude "Help me identify potential biases in this study design"
claude "What are the strengths and weaknesses of this argument?"

Writing and Communication Skills:

bash
# Improve writing clarity
claude "Help me make this technical explanation more accessible"
claude "Review this email for professional tone and clarity"

# Practice different writing styles
claude "Help me write a formal business proposal"
claude "Convert this technical report into a executive summary"

💡 Claude Code Pro Tip: Use local AI as a learning partner, not a replacement for critical thinking. Ask it to explain its reasoning and always verify important information through authoritative sources.

Enterprise Workers: Trade Secret Protection ​

Tech Company Employee:

bash
# Analyze internal documents safely
claude "Help me create a technical specification for this new feature"

# Strategic planning assistance
claude "Outline potential risks and mitigation strategies for this product launch"

# Process improvement
claude "Suggest ways to optimize this workflow"

Benefits for Enterprise:

  • Complete protection of trade secrets
  • No competitive intelligence leakage
  • Secure internal document analysis
  • Compliance with corporate policies

Air-Gapped Environments and Maximum Privacy ​

What is an Air-Gapped Environment? ​

An air-gapped environment is completely isolated from external networks, including the internet. This is common in:

  • Government facilities handling classified information
  • Healthcare systems with strict PHI requirements
  • Financial institutions with sensitive trading data
  • Research facilities with proprietary information

Setting Up Ollama in Air-Gapped Environments ​

Step 1: Prepare on an Internet-Connected Machine

bash
# Download everything you need while online
ollama pull llama3:8b
ollama pull mistral:7b

# Export models for transfer
ollama list  # Note model names and sizes

Step 2: Transfer to Air-Gapped Machine

bash
# Models are stored in: ~/.ollama/models/
# Copy this entire directory to your air-gapped system
# Along with the Ollama and CCProxy binaries

Step 3: Verify Complete Isolation

bash
# On air-gapped machine, verify no network access
ping google.com  # Should fail

# Start Ollama and CCProxy
ollama serve
ccproxy --offline-mode=enabled --local-only=true

# Test local AI functionality
claude "Hello, are you working?"

💡 Claude Code Pro Tip: Test your air-gapped setup thoroughly before relying on it for sensitive work. Verify that all models work correctly without any network connectivity.

Maximum Privacy Configuration ​

bash
# Enable all privacy features
export OLLAMA_DISABLE_TELEMETRY=true
export OLLAMA_PRIVACY_MODE=maximum
export OLLAMA_LOG_LEVEL=warn  # Minimize logging

# Configure CCProxy for maximum privacy
ccproxy --privacy-mode=maximum \
        --offline-mode=enabled \
        --local-only=true \
        --disable-telemetry=true \
        --no-logging=true

Understanding Performance and Limitations ​

Honest Performance Expectations ​

Local AI models have different capabilities than cloud services:

What Local AI Does Well:

  • Text analysis and summarization
  • Writing assistance and editing
  • Code review and explanation
  • Research assistance
  • Template creation

Current Limitations:

  • Response speed depends on your hardware
  • Model capability varies by size
  • Some complex reasoning may be limited
  • No real-time internet knowledge

Hardware Requirements for Different Use Cases ​

Basic Text Work (Lawyers, Writers, Students):

  • 8GB RAM minimum
  • Modern CPU (last 5 years)
  • 10GB free storage
  • Models: Llama 3 8B, Mistral 7B

Advanced Analysis (Researchers, Developers):

  • 16GB RAM recommended
  • Modern CPU with 8+ cores
  • 20GB free storage
  • Models: Llama 3 70B, CodeLlama 34B

Enterprise/High-Performance:

  • 32GB+ RAM
  • High-end CPU or GPU
  • 50GB+ storage
  • Multiple models for different tasks

💡 Claude Code Pro Tip: Start with smaller models and upgrade as needed. A 7B model on fast hardware often outperforms a 70B model on slow hardware.

Model Selection Strategy ​

bash
# Configure intelligent model routing
export OLLAMA_TASK_ROUTING=enabled

# Task-specific model assignment
export OLLAMA_COMPLETION_MODEL=codellama:7b-instruct
export OLLAMA_ANALYSIS_MODEL=codellama:13b-instruct
export OLLAMA_GENERATION_MODEL=codellama:34b-instruct

# Performance monitoring
ccproxy --performance-monitoring=enabled \
        --model-performance-tracking=enabled

Memory and CPU Management ​

bash
# Optimize system resources
export OLLAMA_NUM_PARALLEL=2        # Parallel model instances
export OLLAMA_MAX_LOADED_MODELS=3   # Maximum loaded models
export OLLAMA_GPU_MEMORY_FRACTION=0.8  # GPU memory allocation

# Monitor resource usage
ollama ps  # Show running models and resource usage

Advanced Features ​

Custom Model Integration ​

bash
# Load your own fine-tuned models
ollama create my-custom-model -f Modelfile

# Modelfile example for custom coding model
echo 'FROM codellama:13b-instruct
PARAMETER temperature 0.1
PARAMETER stop "<|end|>"
SYSTEM "You are a senior software engineer specializing in secure coding practices."' > Modelfile

ollama create secure-coder -f Modelfile

Multi-Model Workflows ​

python
# Use different models for different tasks
def multi_model_workflow():
    """
    Leverage multiple models for comprehensive development assistance
    """
    
    workflow = {
        "quick_completion": "codellama:7b-instruct",
        "code_review": "codellama:13b-instruct", 
        "architecture_design": "llama3:8b",
        "documentation": "mistral:7b-instruct",
        "security_analysis": "custom-security-model"
    }
    
    return workflow

Offline Documentation Integration ​

bash
# Create offline documentation embeddings
claude "Index our internal documentation for offline search"

# Benefits:
# - Private documentation search
# - No external API dependencies
# - Contextual code suggestions
# - Company-specific knowledge integration

Security and Compliance ​

Air-Gapped Development ​

python
# Complete network isolation
network_isolation = {
    "no_internet_required": "Ollama works completely offline",
    "local_model_storage": "All models stored locally",
    "private_conversations": "Chat history never leaves machine",
    "encrypted_storage": "Local data encryption available",
    "audit_logging": "Complete local audit trails"
}

Compliance Features ​

bash
# Configure for compliance requirements
export OLLAMA_AUDIT_LOGGING=enabled
export OLLAMA_DATA_ENCRYPTION=aes256
export OLLAMA_ACCESS_CONTROL=strict

# Compliance monitoring
ccproxy --compliance-monitoring=enabled \
        --audit-trail=comprehensive \
        --data-governance=strict

Zero Trust Architecture ​

python
# Implement zero trust with local AI
class ZeroTrustLocalAI:
    def __init__(self):
        self.local_models = OllamaManager()
        self.access_control = AccessController()
        self.audit_logger = AuditLogger()
    
    def process_request(self, request):
        # 1. Verify user permissions
        if not self.access_control.verify(request.user):
            return self.access_denied()
        
        # 2. Log all interactions
        self.audit_logger.log(request)
        
        # 3. Process locally with no network access
        response = self.local_models.process(request)
        
        # 4. Log response (without sensitive content)
        self.audit_logger.log_response(response.metadata)
        
        return response

Enterprise Integration ​

Corporate Network Integration ​

bash
# Configure for corporate environments
export OLLAMA_CORPORATE_PROXY=http://corporate-proxy:8080
export OLLAMA_MODEL_REGISTRY=internal://models.corp.com
export OLLAMA_AUDIT_ENDPOINT=https://audit.corp.com/ai

# Enterprise security
ccproxy --corporate-integration=enabled \
        --sso-authentication=enabled \
        --policy-enforcement=strict

Team Collaboration ​

python
# Share models and configurations across teams
def team_model_sharing():
    """
    Share trained models and configurations
    while maintaining privacy
    """
    
    sharing_strategy = {
        "model_distribution": "Internal model registry",
        "configuration_sync": "Shared configuration templates",
        "knowledge_base": "Private team knowledge embeddings",
        "audit_coordination": "Centralized audit logging"
    }
    
    return sharing_strategy

CI/CD Integration ​

yaml
# .github/workflows/local-ai-review.yml
name: Local AI Code Review
on: [pull_request]

jobs:
  ai-review:
    runs-on: self-hosted  # Use self-hosted runner with Ollama
    steps:
      - uses: actions/checkout@v4
      - name: Start Ollama
        run: |
          ollama serve &
          sleep 10
      - name: Load Review Model
        run: ollama pull codellama:13b-instruct
      - name: AI Code Review
        run: |
          ccproxy --provider=ollama &
          claude "Review the changes in this PR for security and quality"

Model Management and Optimization ​

Model Lifecycle Management ​

bash
# Manage model versions and updates
ollama list                    # List installed models
ollama show codellama:13b     # Show model information
ollama rm old-model:version   # Remove outdated models

# Model update strategy
ollama pull codellama:latest  # Update to latest version
ollama create production-model -f ProductionModelfile  # Custom production model

Fine-Tuning for Your Codebase ​

python
# Prepare your codebase for model fine-tuning
def prepare_training_data():
    """
    Create training data from your codebase
    for model customization
    """
    
    training_preparation = {
        "code_extraction": "Extract relevant code patterns",
        "documentation_pairing": "Pair code with documentation",
        "best_practices": "Include coding standards and patterns",
        "security_examples": "Add security-focused examples"
    }
    
    return training_preparation

Performance Monitoring ​

bash
# Monitor model performance and resource usage
ollama ps                     # Show running models
htop                         # Monitor CPU and memory
nvidia-smi                   # Monitor GPU usage (if applicable)

# CCProxy performance monitoring
ccproxy --metrics-endpoint=http://localhost:9090 \
        --prometheus-export=enabled

Claude Code Best Practices and Pro Tips ​

Essential Claude Code Commands for Privacy-Conscious Professionals ​

Getting Started:

bash
# Check your setup
claude --version
claude "Test connection - are you working locally?"

# General writing assistance
claude "Help me write a professional email declining a meeting"
claude "Improve the clarity of this paragraph: [your text]"

# Research and analysis
claude "Summarize the key points from this document: [paste content]"
claude "Create a pros and cons list for remote work policies"

For Healthcare Professionals:

bash
# Medical documentation (no PHI)
claude "Create a template for patient education about diabetes management"
claude "Explain the latest guidelines for hypertension treatment"

# Research assistance
claude "Help me understand the methodology in this research paper"
claude "Create a literature review outline for wound healing studies"

For Legal Professionals:

bash
# Document drafting
claude "Create a template for a basic service agreement"
claude "Explain the key elements of a valid contract"

# Legal research
claude "Summarize recent changes in employment law"
claude "Create a checklist for corporate compliance audit"

Advanced Claude Code Techniques ​

Multi-step workflows:

bash
# Break complex tasks into steps
claude "Help me create a project plan for implementing new software"
claude "Now help me identify potential risks for this project"
claude "Create a communication plan for stakeholders"

Context building:

bash
# Provide context for better responses
claude "I'm a small business owner in healthcare. Help me understand HIPAA compliance requirements"
claude "As a graduate student in psychology, help me design a survey about workplace stress"

💡 Claude Code Pro Tip: Be specific about your role and context. The more relevant context you provide, the better the AI can tailor its responses to your needs.

Common Issues and Solutions ​

Performance Issues:

  • Slow responses: Try a smaller model like Llama 3 8B instead of 70B
  • High memory usage: Close other applications and restart Ollama
  • Connection errors: Verify CCProxy is running and configured correctly

Model Issues:

  • Poor quality responses: Try different models for your specific task
  • Inconsistent behavior: Clear conversation history and start fresh
  • Language/domain issues: Use models specifically trained for your field

Privacy Verification:

bash
# Verify your setup is truly local
# 1. Disconnect from internet
# 2. Test Claude Code functionality
# 3. Monitor network traffic (should be zero)
# 4. Check Ollama logs for any external requests

💡 Claude Code Pro Tip: Regularly verify your privacy setup by testing offline functionality and monitoring network traffic.

Performance Optimization Tips ​

bash
# Optimize for different scenarios
# For development laptops:
export OLLAMA_NUM_PARALLEL=1
export OLLAMA_MAX_LOADED_MODELS=2

# For development workstations:
export OLLAMA_NUM_PARALLEL=3
export OLLAMA_MAX_LOADED_MODELS=5

# For server deployments:
export OLLAMA_NUM_PARALLEL=8
export OLLAMA_MAX_LOADED_MODELS=10

Future Developments ​

Upcoming Features ​

  1. Distributed Local Models - Share models across team networks
  2. Advanced Fine-Tuning - Easier customization for specific domains
  3. Multi-Modal Support - Local image and document processing
  4. Enhanced Performance - Better hardware optimization
  5. Enterprise Management - Advanced admin and monitoring tools

Integration Roadmap ​

bash
# Preview upcoming features
ollama --experimental-features=enabled
ccproxy --beta-local-features=enabled

Community and Resources ​

Ollama + Claude Code Community ​

Contributing ​

bash
# Contribute to local AI development
git clone https://github.com/orchestre-dev/ccproxy
cd ccproxy/integrations/ollama
# Add your local AI workflow examples

The Future of Privacy-Conscious AI ​

Local AI with Ollama, Claude Code, and CCProxy represents more than just a technical solution—it's a paradigm shift toward privacy-first AI assistance. For professionals who handle sensitive information, this approach offers:

Key Benefits Across All Professions ​

Complete Privacy Protection:

  • Your data never leaves your device
  • No third-party servers or cloud processing
  • Full compliance with industry regulations
  • Protection against data breaches and leaks

Professional Flexibility:

  • Work in air-gapped environments
  • Maintain attorney-client privilege
  • Protect patient confidentiality
  • Secure intellectual property

Cost-Effective Solution:

  • No subscription fees or per-request charges
  • One-time setup with ongoing local use
  • Scales with your needs without additional costs
  • Reduces dependency on external services

Educational Value:

  • Learn about AI capabilities and limitations
  • Understand local vs. cloud AI trade-offs
  • Develop digital literacy in AI tools
  • Build confidence in privacy-focused technology

Getting Started: Your Next Steps ​

  1. Assess your needs: Determine what type of AI assistance would benefit your work
  2. Check hardware requirements: Ensure your computer meets minimum specifications
  3. Start small: Begin with basic models and simple tasks
  4. Gradually expand: Add more models and explore advanced features as you learn
  5. Verify privacy: Regularly test your setup to ensure complete local operation

A Note on Limitations ​

Local AI is powerful, but it's important to understand its current limitations:

  • Models may not have the absolute latest information
  • Complex reasoning tasks may require larger models and more powerful hardware
  • Setup requires some technical knowledge
  • Performance depends on your computer's capabilities

However, for many professional tasks—writing, analysis, research assistance, and document drafting—local AI provides excellent results while maintaining complete privacy.

The Broader Impact ​

By choosing local AI, you're not just protecting your own data—you're supporting a more privacy-conscious approach to AI development. This choice encourages:

  • Development of better local AI tools
  • Increased focus on privacy-preserving AI
  • Reduced dependency on large cloud providers
  • Greater individual control over AI technology

Ready to take control of your AI assistance?

Whether you're a healthcare worker protecting patient privacy, a legal professional maintaining client confidentiality, a researcher safeguarding intellectual property, or simply someone who values privacy, local AI offers a compelling path forward.

Learn more about setting up CCProxy with Ollama and join the growing community of privacy-conscious professionals using local AI.

Stay Updated ​

Join our newsletter to get the latest updates on new models, features, and best practices. We promise to only send you the good stuff – no spam, just pure AI development insights.

📬

Get Updates

•Stay informed about new features and providers

🤝 We promise to only send you the good stuff. No spam, just pure CCProxy goodness.

Questions about local AI for your profession? Join our community discussions and connect with other professionals who have made the switch to privacy-first AI assistance.