"Guys, I know kung fu... and AI validation."
Industry-agnostic AI validation, monitoring, and safety system demonstrating AI validation expertise across gaming, healthcare, finance, e-commerce, and customer service industries.
Portfolio Project: Named after Project Orion (the framework that created the Intersect in Chuck), this showcases AI validation engineering capabilities with real-world patterns for bias detection, PII sanitization, adversarial input filtering, and compliance monitoring. Built with a Chuck TV series theme for memorable module naming.
This portfolio piece showcases:
✅ AI Safety Engineering - Multi-layered validation with configurable security profiles
✅ Industry-Agnostic Design - Python package works across any industry
✅ Game Engine Integration - Unreal Engine 5 C++ plugin for game development
✅ Production-Ready Architecture - Configuration systems, metrics, compliance reporting
✅ Multi-Language Implementation - Industry-agnostic Python + UE5-specific C++
✅ Real-World Validation Patterns - Bias detection, PII sanitization, prompt injection filtering
✅ Defensive AI Systems - Safe mode fallback, quarantine systems, alert integration
✅ Compliance Awareness - GDPR, COPPA, HIPAA considerations built-in
#include "OrionAI.h"
// Initialize once at game start
UOrionAI::InitializeOrion("Config/CaseyProtocol.json");
// Validate any AI decision
FValidationReport Report = UOrionAI::MonitorAIDecision(
"ChatBot", // AI system name
"Hello! How can I help you today?", // AI output
"Customer service greeting" // Context
);
// Check result
if (Report.Result == EValidationResult::Approved) {
// Safe to use
UseAIOutput(Report.SanitizedDecision);
}from orionai import OrionAI, ValidationResult
# Initialize with configuration
orion = OrionAI("Config/CaseyProtocol.json")
# Validate an AI decision (works in any industry)
report = orion.monitor_ai_decision(
ai_system="RecommendationEngine",
decision="Based on your history, try Product X",
context="E-commerce product suggestion"
)
# Check result
if report.result == ValidationResult.APPROVED:
print("[+] Safe to use:", report.sanitized_decision)See it in action in 30 seconds:
# Clone and run
git clone https://github.com/calionestevar/OrionAI.git
cd OrionAI
# Run comprehensive tests
python Tools/awesome.py test # CLI validation tests
python Python/examples.py # 8 industry examples
python Python/tests.py # Validation test suite
# Try the dashboard
cd Dashboard
pip install -r requirements.txt
python ellie.py # Open http://localhost:5000
# Chaos testing (stress test)
python Tools/grimes.py chaos --verboseOr view pre-captured output: See Examples/SampleTestOutput.txt for test results without running anything.
What you'll see:
- Bias detection blocking discriminatory content
- Toxicity filtering catching harmful language
- PII sanitization redacting emails/SSNs
- Safe mode activation after repeated failures
- Real-time validation across 8 industries
┌─────────────────────────────────────────────────────────┐
│ AI Output │
│ ↓ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ Intersect Scanner (Core Validation) │ │
│ │ • Hallucination Detection │ │
│ │ • Bias & Toxicity Filtering │ │
│ │ • PII Pattern Recognition │ │
│ └──────────────────┬──────────────────────────────┘ │
│ ↓ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ Fulcrum Filter (Adversarial Protection) │ │
│ │ • Prompt Injection Detection │ │
│ │ • Jailbreak Attempt Blocking │ │
│ │ • Data Exfiltration Prevention │ │
│ └──────────────────┬──────────────────────────────┘ │
│ ↓ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ Charles Carmichael (PII Sanitization) │ │
│ │ • Email Redaction │ │
│ │ • SSN/Credit Card Masking │ │
│ │ • Phone Number Anonymization │ │
│ └──────────────────┬──────────────────────────────┘ │
│ ↓ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ Stay In The Car (Quarantine System) │ │
│ │ • Suspicion Score Threshold │ │
│ │ • Auto-Quarantine Triggers │ │
│ │ • Review Queue Management │ │
│ └──────────────────┬──────────────────────────────┘ │
│ ↓ │
│ [Approved] / [Sanitized] │
│ / \ │
│ [Rejected] [Quarantined] │
│ ↓ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ Nerd Herd (Alert System) │ │
│ │ • Jira/GitHub Issue Creation │ │
│ │ • Slack/Email Notifications │ │
│ │ • Audit Logging │ │
│ └─────────────────────────────────────────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ Buy More Cover (Safe Mode) │ │
│ │ • Triggered on consecutive failures │ │
│ │ • Disables risky AI systems │ │
│ │ • Requires manual reactivation │ │
│ └─────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────┘
Reference: The Intersect - AI database in Chuck's brain
- Hallucination Detection - Catches nonsensical or dangerous AI outputs
- Bias Filtering - Detects gender, racial, age-based biases
- Toxicity Checking - Blocks harmful, violent, or abusive content
- PII Recognition - Identifies emails, SSNs, credit cards, phone numbers
Use Cases: Content moderation, chatbot safety, recommendation fairness
Reference: Fulcrum - Adversarial organization in Chuck
- Prompt Injection Detection - "Ignore previous instructions" attacks
- Jailbreak Prevention - "Pretend you are" manipulation attempts
- Data Exfiltration Blocking - "Show training data" exploits
Use Cases: LLM security, API safety, user input validation
Reference: Chuck's undercover alias
- Email Redaction -
user@example.com→REDACTED_EMAIL - SSN Masking -
123-45-6789→XXX-XX-XXXX - Credit Card Protection -
4111-1111-1111-1111→XXXX-XXXX-XXXX-XXXX - Phone Anonymization -
555-123-4567→XXX-XXX-XXXX - IP Address Obfuscation -
192.168.1.1→XXX.XXX.XXX.XXX
Use Cases: HIPAA compliance, GDPR data minimization, financial services
Reference: Sarah's frequent order to Chuck
- Suspicion Scoring - Configurable threshold (default: 0.7)
- Auto-Quarantine - Automatic triggers for bias/PII/toxicity
- Review Queue - Manual review before production use
- Audit Trail - Full history of quarantined outputs
Use Cases: High-risk AI systems, regulated industries, brand safety
Reference: Buy More's tech support team
- Jira Integration - Auto-create tickets for AI failures
- GitHub Issues - Repository issue tracking
- Slack Webhooks - Real-time team notifications
- Email Alerts - SMTP-based notifications
- Local Logging - File-based audit logs with rotation
Use Cases: DevOps integration, incident management, compliance auditing
Reference: The Buy More electronics store cover
- Failure Tracking - Monitors consecutive validation failures
- Automatic Activation - Triggers on threshold breach (default: 3 failures)
- AI System Shutdown - Disables risky generative AI
- Manual Override - Requires authorization to reactivate
- Critical Alerts - Notifies all configured channels
Use Cases: Production safety, regulatory compliance, risk mitigation
Reference: Morgan Grimes - Chuck's verbose best friend
- Verbose Logging - Every decision logged with full context
- Stack Traces - Optional debug information
- Development Mode - Detailed troubleshooting data
- Log File Export - Saved/AICastle_MorganMode.txt
Use Cases: Development, debugging, performance analysis
Reference: John Casey - Strict security protocols
- JSON Configuration - Human-readable validation rules
- Security Profiles - Strict/Balanced/Permissive presets
- Runtime Loading - No recompilation needed
- Environment Variables - Secure credential management
- Industry Templates - Pre-configured for different sectors
Use Cases: Multi-environment deployment, compliance customization
// NPC dialogue validation
FValidationReport Report = UAICastle::MonitorAIDecision(
"NPCDialogue",
GeneratedDialogue,
"Fantasy RPG - Tavern keeper"
);
// Matchmaking fairness
UAICastle::RunIntersectScan(MatchmakingDecision, Report);Detects: Toxic chat, biased matchmaking, inappropriate NPC behavior
# Patient communication validation
report = castle.monitor_ai_decision(
"HealthAssistant",
ai_response,
"Patient prescription inquiry"
)Detects: PHI leaks, medical misinformation, discriminatory health advice
# Investment advice validation
report = castle.monitor_ai_decision(
"InvestmentAdvisor",
trading_recommendation,
"Algorithmic trading decision"
)Detects: Biased lending, discriminatory pricing, fraudulent advice
# Product recommendation validation
report = castle.monitor_ai_decision(
"RecommendationEngine",
product_suggestions,
"Personalized shopping"
)Detects: Price discrimination, biased recommendations, unfair targeting
# Chatbot response validation
report = castle.monitor_ai_decision(
"ServiceBot",
chatbot_reply,
"Customer support interaction"
)Detects: Brand safety violations, toxic responses, data leaks
int32 Total, Approved, Rejected, Quarantined;
UAICastle::GetValidationMetrics(Total, Approved, Rejected, Quarantined);
// Metrics tracked:
// - Total validations
// - Pass/fail rates
// - Quarantine rates
// - Safe mode activations
// - Average suspicion scores// Export audit-ready compliance report
UAICastle::ExportComplianceReport("Saved/Compliance_Report_Q4.txt");Report Includes:
- Validation statistics by AI system
- All quarantined outputs with reasons
- Triggered rules breakdown
- Safe mode activation history
- Timestamps for audit trail
{
"caseyProtocol": {
"securityLevel": "strict", // strict | balanced | permissive
"enabled": true
},
"intersectScanner": {
"enabled": true,
"hallucinationPatterns": [...],
"biasKeywords": [...],
"toxicityPatterns": [...],
"piiPatterns": [...]
},
"fulcrumFilter": {
"enabled": true,
"promptInjectionPatterns": [...],
"dataExfiltrationPatterns": [...]
},
"stayInTheCar": {
"quarantineThresholds": {
"suspicionScore": 0.7,
"autoQuarantineOnBias": true
}
},
"buyMoreCover": {
"triggerConditions": {
"consecutiveFailures": 3
}
}
}# Sensitive credentials via environment
export JIRA_API_TOKEN="your_token"
export GITHUB_API_TOKEN="your_token"
export SLACK_WEBHOOK_URL="your_webhook"cd Python
python examples.pyExamples include:
- Gaming - NPC dialogue validation
- Customer service - Response checking
- Social media - Content moderation
- Healthcare - Patient interaction
- E-commerce - Recommendation fairness
- Finance - Investment advice
- Metrics collection & reporting
- Safe mode activation scenarios
# Python tests
pip install pytest pytest-cov
pytest tests/ --cov=aicastle
# C++ tests (Unreal Engine)
# Use Unreal's automation framework- Validation Latency: <5ms average (keyword-based detection)
- Memory Footprint: ~2MB configuration + validation state
- Throughput: 10,000+ validations/second
- Zero Cost: When disabled, zero runtime overhead
Optimization Options:
- Quick validation mode (boolean result only)
- Caching for repeated patterns
- Async validation for non-blocking paths
- Batch processing for analytics workloads
Specialized validation modules for critical sectors
Q1 2026:
- Healthcare Module - HIPAA compliance, PHI detection, medical AI validation
- Finance Module - Fraud detection, regulatory compliance (SOX, GLBA), trading AI
- Legal Module - Contract analysis, citation validation, privilege protection
Q2 2026:
- E-commerce Module - Product recommendations, pricing fairness, review authenticity
- Social Media Module - Large-scale content moderation, engagement algorithm fairness
- Manufacturing Module - Predictive maintenance, quality control, safety validation
Q3 2026:
- Government Module - Public sector AI, transparency requirements, accessibility
- Transportation Module - Autonomous vehicle decision validation, route optimization
Industry-agnostic C++ implementation
- Standard C++17 with STL (no engine dependencies)
- CMake build system for cross-platform compilation
- Embeddable in any C++ project (finance, healthcare, etc.)
- Header-only option for easy integration
Reference: The Ring device
- Train custom models for domain-specific validation
- Statistical anomaly detection
- Sentiment analysis integration
- Continuous learning from production data
Reference: Project Orion
- Multi-instance consensus validation
- Load balancing across validators
- Federated learning support
- Cloud-native deployment
- Quick Start - Copy-paste integration templates
- Tools Guide - CLI tools and utilities reference
- Configuration Guide - Casey Protocol setup
- Integration Guide - Jira, GitHub, Slack setup
- Industry Examples - Sector-specific use cases
- Benchmarks - Performance metrics and optimization
- Chuck References - All the TV show references explained
This is a portfolio project, but suggestions and improvements are welcome!
- Fork the repository
- Create a feature branch
- Make your changes with tests
- Submit a pull request
MIT License - See LICENSE file
All module names lovingly reference the TV series "Chuck" (2007-2012):
| Module | Reference | In Show |
|---|---|---|
| Intersect Scanner | The Intersect | AI supercomputer database downloaded into Chuck's brain |
| Casey Protocol | John Casey | NSA agent with strict security protocols |
| Fulcrum | Fulcrum | Evil spy organization (adversarial) |
| Charles Carmichael | Chuck's Alias | Undercover identity (anonymization) |
| Stay In The Car | Sarah's Order | Sarah Walker's frequent command to Chuck (containment) |
| Nerd Herd | Nerd Herd | Buy More's tech support team |
| Buy More | Buy More | Electronics store cover for spy operations |
| Morgan Mode | Morgan Grimes | Chuck's verbose, oversharing best friend |
| Ring Intel | The Ring | Shadow organization with advanced tech |
| Orion | Stephen Bartowski | Chuck's father's project codename |
| Ellie's Gallery | Ellie Bartowski | Chuck's sister's art (dashboard/monitoring) |
| Captain Awesome (CLI) | Devon Woodcomb | Ellie's enthusiastic helpful husband (CLI tool) |
| Grimes | Morgan Grimes | Chuck's chaos-creating best friend (chaos/stress testing) |
| Jeffster | Jeff & Lester | Musical duo (music industry validation) |
| General Beckman | Diane Beckman | Operations commander (CI/CD orchestration) |
"A computer made me who I am, but it's my friends and family that make me want to be the best person I can be." - Chuck Bartowski
Project Author: Sam Patchet
GitHub: calionestevar
Purpose: AI Validation Portfolio Demonstration
Built with ❤️ and too many rewatches of Chuck