Qriton Enterprise Platform - Neural-Symbolic AI

Total Processing Tasks

12% from last week

📊

Average HallMeter Score

{{ stats.avgHallMeterScore }}%

High confidence

🎯

Active Stations

All systems operational

🏭

Active Agents

{{ newAgentsThisWeek }} new this week

🤖

Neural-Symbolic Processing Pipeline

📥

Data Input

🔌

Data Sources

⚡

Symbolic Rules

🧠

Neural Processing

🎯

HallMeter Validation

📊

Results Transparency

Causal Advice Assistant

AI-Powered Recommendations

Describe your desired outcome, and I'll analyze the causal graph to find proven paths to achieve it. Recommendations are based on successful patterns observed in your system.

What would you like to achieve?

Domain (optional)

Max Steps

Min Confidence %

Quick examples:

Causal Recommendations

{{ causalRecommendations.recommendations.length }} paths found

Suggestions:

{{ suggestion }}

AI Analysis

Key Success Factors:

{{ factor }}

Considerations:

{{ risk }}

Option {{ index + 1 }}: {{ rec.startingAction }}

Target: {{ rec.targetOutcome }}

{{ rec.confidence }}%

{{ rec.steps }} steps

Causal Path:

Observations

Success Rate

{{ rec.evidence.successRate }}%

Evolution

Implementation Steps

{{ step.action }}
Expected: {{ step.expectedResult }}

Requirements:

{{ req }}

System Processing State Monitor

Processing Efficiency: {{ quantumEfficiency }}%

What is this visualization?

This shows the real-time processing state of your AI agents and tasks. Each node represents a processing unit, and the connections show data flow between them. The neural wave displays overall system activity. Active nodes indicate ongoing computations, while the efficiency percentage shows how well the system is utilizing resources.

Recent High-Confidence Results (90%+ HallMeter)

No high confidence outcomes yet. Run some tasks to see results!

{{ outcome.output?.substring(0, 100) }}...

Domain: {{ outcome.domain }} | Agent: {{ outcome.agent_name }}

{{ outcome.probability }}%

HallMeter™ Advanced Validation System

Verify the truth probability of any statement with AI-powered multi-dimensional analysis
HallMeter combines pattern recognition, logical analysis, neural networks, and bias detection to evaluate truth probability

🧮 The HallMeter Formula

Final Probability = (Base × 0.4) + (Components × 0.6)

Where: Components includes bias check with 15% weight

Component Calculation:


          Components Score = Σ(component[i] × weight[i])

          = (Factual × 0.20) + (Logical × 0.18) + (Contextual × 0.13)

            + (Temporal × 0.09) + (Causal × 0.18) + (Environmental × 0.07)

            + (Bias × 0.15)

Seven Scientific Dimensions of Truth Validation:

1. Factual Accuracy 20%

Verifies if information can be substantiated with evidence and aligns with established facts.

Scoring Algorithm:

Verified facts detected: +25%
Speculative language: -20%
Precise data/numbers: +15%
Scientific formulas: +20%
Universal truths: +30%

Base: 50% | Max: 100%

2. Logical Consistency 18%

Evaluates reasoning structure and checks for contradictions or fallacies in the argument.

Logic Analysis:

Logical connectors: +15%
Contradictions found: -10%
Clear causal chains: +20%
If-then validity: +15%
Circular reasoning: -25%

Base: 70% | Range: 0-100%

3. Contextual Relevance 13%

Assesses alignment with specified domain, environment, and contextual parameters.

Context Scoring:

Domain alignment: +20%
Tag relevance: +20%
Environment match: +15%
Real-time data: +10%
Current context: +10%

Base: 60% | Boosted by data

4. Temporal Validity 9%

Checks if information is current, timeless, or appropriately dated for the context.

Time Analysis:

Timeless truths: 95%
Recent references: +15%
Current keywords: +10%
Outdated info: -30%
Future claims: -20%

Base: 80% | Decay over time

5. Causal Relationships 18%

Analyzes cause-effect validity and strength of correlations in the statement.

Causality Matrix:

Direct causation: 90%
Strong influence: 70%
Weak correlation: 50%
If-then patterns: 80%+
Complex chains: 85%+

Base: 50% | Evidence-based

6. Environmental Factors 7%

Considers physical conditions and external factors affecting statement validity.

Factor Impact:

Each factor: +25%
Multiple factors: +20%
Temperature critical: 90%
Location match: +15%
Conditions met: +30%

Base: 0% | Cumulative

7. Bias & Fallacy Check 15%

Detects common biases, logical fallacies, and cultural assumptions to ensure objective analysis.

Bias Detection:

Confirmation bias: -15%
Authority fallacy: -20%
Cultural bias: -10%
Absolutist language: -10%
Anecdotal evidence: -15%
Contradictions found: -5% to -30%
Diverse sources: +10%

Base: 100% | Deductive scoring

🔄 Processing Pipeline

Phase 1: Pattern Analysis (40%)

Domain-specific pattern matching
Keyword and phrase detection
Occurrence pattern classification
Initial probability estimation
Bias indicator detection

Phase 2: AI Enhancement (60%)

GPT-4/Claude deep analysis
Cross-validation with neural networks
Contradiction seeking (high temperature)
Real-time data integration
Contextual understanding refinement

Phase 3: Final Calibration

Component weight application
Bias factor adjustment
Domain-specific adjustments
Confidence interval calculation

📊 Confidence Scale

0-29%: Very Low

30-49%: Low

50-69%: Medium

70-79%: Good

80-89%: Very Good

90-94%: Excellent

95-99%: Near Certain

100%: Perfect!

🚀 Meta-Learning

85%+ Threshold:
High-confidence validations can be transformed into specialized AI agents.

Agent inherits:

Validated patterns
Causal logic
Domain expertise
Symbolic rules
Truth parameters
Bias awareness

Scientific Approach: HallMeter's multi-dimensional analysis is inspired by epistemological frameworks for truth validation, combining correspondence theory (factual accuracy), coherence theory (logical consistency), pragmatic theory (contextual relevance), and critical theory (bias detection) with modern AI capabilities for comprehensive, objective statement evaluation.

Validate Statement

Enter statement to validate

Examples: scientific facts, business claims, health statements... {{ hallmeterForm.text.length }}/2000

Domain Context

Location/Environment

Select data sources for real-time validation:

{{ source.name }} ({{ source.type }})

⚡ Complex thought detected - will use decomposition analysis

Thought Network Analysis

{{ fragment.validation.probability }}%

Thought Connections

{{ synapse.type }} {{ (synapse.strength * 100).toFixed(0) }}% {{ synapse.reasoning }}

Quick Examples

🧊 Physics

"Water freezes at minus degrees"

🌅 Astronomy

"Sun rises in the east"

💪 Health

"Exercise improves health"

📅 Business

"Weekly scheduled meetings"

What makes a good validation?
• Clear, specific statements
• Testable claims
• Domain-relevant context
• Cause-effect relationships

Validation Results

{{ getConfidenceLabel(hallmeterResult.probability) }} {{ hallmeterResult.occurrence }} occurrence

{{ hallmeterResult.probability }}%

Click the meter for detailed AI explanation

Bias & Fallacy Analysis

Impact: {{ (check.impact * 100).toFixed(0) }}% penalty

Recommendations to Improve Score:

{{ rec }}

Domain

Location

Method

Pattern

Component Analysis

Detailed Component Scores

{{ component === 'bias' ? 'Bias & Fallacy Check' : component }} {{ (score * 100).toFixed(1) }}%

Analysis Insights

Causal Relationships Detected

{{ rel.cause }} {{ rel.effect }} {{ (rel.strength * 100).toFixed(0) }}%

Environmental Factors

Real-time Data Sources Used

{{ hallmeterResult.insights.dataSourcesUsed.length }} sources Confidence boost: +{{ hallmeterResult.insights.dataSourceImpact?.confidenceBoost || 0 }}%

AI-Generated Explanation

Transform to Intelligent Agent

Meta-Learning Available

This validated knowledge can be transformed into a specialized AI agent that embodies the discovered patterns and relationships.

Confidence Score

{{ hallmeterResult.probability }}%

Domain Expertise

Agent Capabilities Preview

🧠 {{ getAgentCapability(hallmeterResult.domain, 0) }}

🔍 {{ getAgentCapability(hallmeterResult.domain, 1) }}

📊 {{ getAgentCapability(hallmeterResult.domain, 2) }}

Extracted Patterns

Strong causal relationships detected ({{ (hallmeterResult.components.causal * 100).toFixed(0) }}%)
High logical consistency ({{ (hallmeterResult.components.logical * 100).toFixed(0) }}%)
Occurrence pattern: {{ hallmeterResult.occurrence }}
{{ hallmeterResult.insights.causalRelationships.length }} causal relationship(s) identified

Creating intelligent agent from validated knowledge...

Extracting patterns • Generating rules • Building neural template

Agent Created Successfully!

ID: {{ transformedAgent.agentId }}

{{ transformedAgent.config.symbolic_rules.length }} Rules {{ transformedAgent.config.meta.probability }}% Confidence {{ transformedAgent.config.meta.domain }} Expert

Causal Intelligence Graph - Living Knowledge Network

Explore the self-organizing causal graph that captures relationships between validations, agents, and outcomes. Watch as patterns evolve from observations to theories to universal laws. Click nodes to explore causal paths and see predictions of future outcomes.

Filter by Domain

Evolution Stage

Min Strength {{ (causalFilters.minStrength * 100).toFixed(0) }}%

Type {{ selectedNode.NodeType }}

Evolution Stage {{ selectedNode.EvolutionStage }}

Observations {{ selectedNode.ObservationCount }}

Success Rate {{ (selectedNode.SuccessRate * 100).toFixed(1) }}%

Current State

{{ formatNodeState(selectedNode.CurrentState) }}

Predicted Outcomes

{{ pred.PredictedOutcome }} {{ (pred.Probability * 100).toFixed(1) }}%

{{ formatTimeToOutcome(pred.TimeToOutcome) }} Strength: {{ (pred.CausalStrength * 100).toFixed(0) }}%

Total Nodes

Causal Edges

Universal Laws

{{ graphStats.avgStrength }}%

Avg Strength

Evolution Progression

Processing Stations

Processing stations are isolated environments that run AI agents. Each station can be configured with specific resources, security policies, and agent assignments for different types of workloads.

🏭

No Stations Yet

Create your first processing station to start running AI agents

Agents

Tasks

{{ station.avg_confidence || 0 }}%

Avg Confidence

Task Schedulers

Create automated task schedules that run AI agents at specified intervals. Schedulers can pull data from connected sources and execute complex processing workflows automatically.

⏰

No Schedulers Yet

Create your first scheduler to automate task execution

Agent: {{ getAgentName(scheduler.agent_id) }}

Last run: {{ formatTime(scheduler.last_run) }}

Data Sources

Connect external data sources to enrich your AI processing. Sources can include APIs, databases, files, or webhooks that provide real-time data for your agents.

Connected Data Sources

No data sources connected yet

Type: {{ source.type }} | Refresh: {{ source.refresh_interval ? `Every ${source.refresh_interval}s` : 'Manual' }}

Transparency Dashboard

Complete visibility into AI decision-making processes. Track accuracy metrics, audit trails, and explainability coverage across all processing tasks.

Recent HallMeter Validations

Time	Statement	Domain	Confidence	Status
{{ formatTime(validation.created_at) }}	{{ validation.text.substring(0, 50) }}...	{{ validation.domain }}	{{ validation.probability }}%	{{ validation.probability >= 70 ? 'Valid' : 'Uncertain' }}

HallMeter Domain Distribution

Explainability Coverage

100%

✓

All decisions include detailed explanations

Audit Trail Completeness

100%

📋

Full tracking of all processing steps

Data Source Tracking

Active

🔍

All data sources logged and traceable

Decision Reversibility

Enabled

↩️

All decisions can be reviewed and modified

AI Agents - Neural-Symbolic Processing

Our AI Agents combine the power of symbolic reasoning with neural networks. Each agent can process deterministic rules before applying AI models, connect to multiple data sources for real-time information, and provide fully explainable outputs validated by HallMeter.

⚡ {{ agent.symbolic_rules.length }} Rules

🔌 {{ agent.data_sources.length }} Sources

🧠 Neural Processing

🎯 HallMeter Validated

Recent Performance

Tasks: {{ agent.task_count || 0 }}

Avg Confidence: {{ agent.avg_confidence || 0 }}%

Task Execution with HallMeter Validation

Every task execution now includes automatic HallMeter validation, providing confidence scores and detailed explanations for all AI-generated outputs. Tasks process through symbolic rules first, then neural networks, with full transparency at each step.

Execute New Task

Select Agent

Input Data (JSON Format)

Task Results

{{ currentTask.probability }}%

Input Processing

Symbolic Rules

Neural Processing

HallMeter Validation

Output

HallMeter Analysis

Domain

Confidence

{{ getConfidenceLabel(currentTask.hallmeter_analysis.probability) }}

Detailed Explanation

Summary

Overall Confidence: {{ (currentTask.explanation.confidence * 100).toFixed(1) }}%

→ {{ rule.evaluated }}

✓ {{ rule.result }}

Model: {{ exp.model || 'GPT-4' }}
Processing Time: {{ exp.processing_time || '1.2s' }}
Tokens Used: {{ exp.tokens || '847' }}

Factual Accuracy: {{ (exp.components?.factual * 100 || 0).toFixed(1) }}%

Logical Consistency: {{ (exp.components?.logical * 100 || 0).toFixed(1) }}%

Contextual Relevance: {{ (exp.components?.contextual * 100 || 0).toFixed(1) }}%

Temporal Validity: {{ (exp.components?.temporal * 100 || 0).toFixed(1) }}%

Recent Tasks

Task ID	Agent	Status	HallMeter Score	Time	Actions
{{ task.id }}	{{ getAgentName(task.agent_id) }}	{{ task.status }}	{{ task.probability }}% -	{{ formatTime(task.created_at) }}

Compliance & Transparency Reports

Comprehensive Reporting

Generate detailed reports covering EU AI Act compliance, HallMeter validation statistics, task performance metrics, and full transparency documentation. All reports include complete audit trails and explainability coverage.

Report Type	Status	Details
EU AI Act Compliance	Compliant	Full transparency and explainability for all decisions
HallMeter Validation	Active	{{ stats.totalTasks }} validations with {{ stats.avgHallMeterScore }}% average confidence
Data Source Tracking	Complete	All {{ stats.dataSources }} sources logged and traceable
Scheduler Audit	Up to Date	{{ stats.activeSchedulers }} active schedulers monitored

Report History

No reports generated yet

{{ report.type }} Report

Generated: {{ formatDateTime(report.created_at) }}

Qriton Platform Documentation

Welcome to the Qriton Enterprise Platform documentation. This guide covers the architecture, API references, and best practices for using our neural-symbolic AI system.

Core Components

1. Processing Stations

Stations are isolated processing environments that run AI agents. Each station provides:

Resource isolation and management
Security boundaries for sensitive processing
Scalable compute resources
Agent assignment and orchestration
Performance monitoring and metrics

2. AI Agents

Our hybrid AI agents combine symbolic reasoning with neural networks:

Symbolic Rules: Deterministic pre-processing logic
Neural Processing: Advanced AI models (GPT-4, Claude, etc.)
Data Integration: Real-time data from multiple sources
Explainability: Full transparency in decision-making
HallMeter Validation: Truth probability assessment

3. HallMeter Technology

HallMeter evaluates the truth probability of AI outputs using six key components:

Factual Accuracy: Verification against known facts
Logical Consistency: Internal logic validation
Contextual Relevance: Domain and context alignment
Temporal Validity: Time-relevance of information
Causal Relationships: Cause-effect analysis
Environmental Factors: External conditions assessment

Available AI Agents

🔍 Anomaly Detector

Identifies anomalies in system metrics using pattern recognition and statistical analysis. Combines rule-based thresholds with neural network pattern detection.

🎯 Root Cause Analyzer

Analyzes system failures and performance issues to identify root causes. Uses causal inference and dependency mapping.

⚡ Optimization Engine

Optimizes system performance and resource allocation. Applies constraint solving and multi-objective optimization.

📊 Market Analyzer

Analyzes market trends and provides investment insights. Integrates real-time market data with predictive modeling.

🛡️ Security Auditor

Performs security assessments and vulnerability analysis. Combines rule-based security policies with threat detection AI.

📋 Compliance Checker

Ensures regulatory compliance and policy adherence. Maps requirements to system states and generates audit reports.

API Reference

Authentication

// Login
POST /api/auth/login
{
  "username": "your-username",
  "password": "your-password"
}

// Response
{
  "token": "jwt-token",
  "user": {
    "id": "user-id",
    "username": "your-username"
  }
}

Stations API

// List all stations
GET /api/stations

// Create a new station
POST /api/stations
{
  "name": "Production Station",
  "description": "Main production processing",
  "config": {
    "max_agents": 10,
    "resources": {
      "cpu": "4 cores",
      "memory": "16GB"
    }
  }
}

// Update station
PUT /api/stations/{stationId}

// Delete station
DELETE /api/stations/{stationId}

Agents API

// List all agents
GET /api/agents

// Create a new agent
POST /api/agents
{
  "name": "Custom Analyzer",
  "type": "hybrid",
  "role": "Analyzes custom metrics",
  "prompt_template": "Analyze the following data: {{input}}",
  "symbolic_rules": [
    {
      "if": {
        "field": "metrics.cpu",
        "op": "gt",
        "value": 80
      },
      "then": {
        "flag": "high_cpu_usage"
      }
    }
  ],
  "data_sources": ["api-1", "db-1"]
}

// Update agent
PUT /api/agents/{agentId}

// Delete agent
DELETE /api/agents/{agentId}

HallMeter API

// Validate a statement
POST /api/hallmeter/validate
{
  "text": "Statement to validate",
  "context": {
    "domain": "technology",
    "tags": ["ai", "quantum"],
    "location": "global"
  }
}

// Response
{
  "id": "validation-id",
  "probability": 87,
  "components": {
    "factual": 0.92,
    "logical": 0.88,
    "contextual": 0.85,
    "temporal": 0.90,
    "causal": 0.83,
    "environmental": 0.84
  },
  "domain": "technology",
  "insights": {
    "domainSpecific": [...],
    "causalRelationships": [...],
    "environmentalFactors": [...]
  }
}

// Get explanation
POST /api/hallmeter/explain/{validationId}

Task Execution API

// Execute a task
POST /api/tasks/run
{
  "agentId": "anomaly-detector",
  "input": {
    "metrics": {
      "cpu": 85,
      "memory": 92,
      "disk": 78
    }
  }
}

// Response (via WebSocket)
{
  "type": "task:completed",
  "data": {
    "taskId": "task-123",
    "output": "Anomaly detected in memory usage",
    "hallmeter": {
      "probability": 94,
      "domain": "technology"
    },
    "explanation": {
      "summary": "High confidence anomaly detection",
      "explanations": [...]
    }
  }
}

Scheduler API

// Create scheduler
POST /api/stations/{stationId}/schedulers
{
  "name": "Hourly Analysis",
  "agent_id": "anomaly-detector",
  "cron_expression": "0 * * * *",
  "input_template": {
    "source": "scheduled",
    "fetch_latest": true
  },
  "active": true
}

// List schedulers
GET /api/schedulers

// Update scheduler
PUT /api/schedulers/{schedulerId}

// Delete scheduler
DELETE /api/schedulers/{schedulerId}

Data Sources API

// Add data source
POST /api/datasources
{
  "name": "Metrics API",
  "type": "api",
  "config": {
    "url": "https://api.example.com/metrics",
    "method": "GET",
    "headers": {
      "Authorization": "Bearer token"
    }
  },
  "refresh_interval": 300
}

// List data sources
GET /api/datasources

// Test data source
POST /api/datasources/{sourceId}/test

// Update data source
PUT /api/datasources/{sourceId}

// Delete data source
DELETE /api/datasources/{sourceId}

WebSocket Events

The platform uses WebSocket for real-time updates. Connect to receive:

quantum:update - Quantum state changes
task:completed - Task completion notifications
scheduler:update - Scheduler status changes
datasource:update - Data source updates
station:status - Station status changes

Best Practices

Agent Design

Start with symbolic rules for deterministic logic
Use neural processing for complex pattern recognition
Connect relevant data sources for real-time context
Design clear, specific prompt templates
Test with various inputs before production deployment

HallMeter Usage

Always validate critical outputs with HallMeter
Use domain-specific context for better accuracy
Monitor component scores to identify weak areas
Set confidence thresholds based on use case criticality
Review low-confidence results manually

Performance Optimization

Use station resources efficiently
Batch similar tasks when possible
Cache frequently accessed data
Monitor agent performance metrics
Optimize symbolic rules for speed

Compliance & Security

The Qriton platform is designed for enterprise compliance:

EU AI Act: Full transparency and explainability
GDPR: Data privacy and user rights
SOC 2: Security and availability controls
ISO 27001: Information security management
Audit Trails: Complete logging of all operations

Support & Resources

Need help? Access our support resources:

Technical Support: support@qriton.ai
Documentation: docs.qriton.ai
API Status: status.qriton.ai
Community Forum: community.qriton.ai
Training Resources: learn.qriton.ai