verodat-adri – Enterprise Data Quality for Production AI

Enterprise-grade data quality protection for production AI workflows.

Built on the proven open source ADRI framework with enterprise extensions for team collaboration, compliance, and production reliability.

# Same code works in both editions - zero migration effort
from adri import adri_protected

@adri_protected(contract="customer_data", data_param="data")
def process_customers(data):
    return enhanced_data

Why Upgrade to Enterprise?

For Production Teams

• Centralized Monitoring: Optional upload of assessment logs to Verodat (requires API key)
• Audit Logs: Local JSONL audit logs (Verodat-compatible schema)
• Environment Separation: Dev/test/prod config via ADRI/config.yaml + ADRI_ENV
• Same API: Same @adri_protected usage as open source

For AI/ML Engineers

• Workflow Context: Accepts workflow_context metadata (logged when verbose)
• Reasoning Mode: reasoning_mode=True enables reasoning-oriented behavior + optional local JSONL prompt/response logs
• Data Provenance: Accepts data_provenance metadata (logged when verbose)
• Performance Options: Fast-path assessment manifest storage (memory/file/redis backends)

For Engineering Managers

• License Gate: API key validation + 24h cache on first use
• Operational Visibility: Centralized logs when Verodat upload is enabled
• Developer Productivity: No code changes needed beyond installing enterprise package and setting VERODAT_API_KEY
• Team Adoption: Same decorator + CLI patterns

Enterprise vs Open Source Comparison

Zero-Effort Migration

Step 1: Install Enterprise Edition

# If you already have the open source `adri` package installed,
# uninstall it first to avoid import conflicts (both ship an `adri` module).
pip uninstall -y adri

pip install verodat-adri

Step 2: Add API Key

export VERODAT_API_KEY="your-api-key-here"

Step 3: Your Code Already Works!

# This exact code works in both editions - no changes needed
from adri import adri_protected
import pandas as pd

@adri_protected(
    contract="customer_data",
    min_score=85,
    on_failure="raise"
)
def process_customer_data(data):
    # Your existing data processing logic
    enhanced_data = data.copy()
    enhanced_data['processed_at'] = pd.Timestamp.now()
    return enhanced_data

# Optional enterprise add-ons:
# ✅ License validation and access control
# ✅ Optional Verodat upload (when configured)
# ✅ Environment-aware contract resolution
# ✅ Optional reasoning logs (when enabled)

Enterprise Feature Deep Dive

1. Centralized Logging (Optional)

Problem Solved: Local-only logs are hard to aggregate across projects/environments.

Enterprise Solution: When Verodat upload is enabled, assessments can be sent to Verodat via API.

from adri import adri_protected

# Optional centralized logging (when Verodat upload is configured)
@adri_protected(contract="sales_data")
def analyze_sales_performance(data):
    return analysis_results

# The assessment can be uploaded to Verodat (depending on your logging configuration)

Customer Value:

• Visibility: Aggregate quality signals across environments
• Operational debugging: Access logs outside local machines/containers
• Auditability: Preserve verifiable assessment artifacts

2. Environment-Aware Configuration

Problem Solved: Same data quality rules in dev and production can cause issues.

Enterprise Solution: Environment-specific configurations with automatic environment detection.

# ADRI/config.yaml - Enterprise environment configuration
adri:
  project_name: "customer_analytics"
  default_environment: "development"
  environments:
    development:
      paths:
        contracts: "./dev/contracts"
        assessments: "./dev/assessments"
      protection:
        default_min_score: 70      # Relaxed for development
        default_failure_mode: "warn"  # Non-blocking for development
    production:
      paths:
        contracts: "./prod/contracts"
        assessments: "./prod/assessments"
      protection:
        default_min_score: 85      # Strict for production
        default_failure_mode: "raise"  # Blocking for production

# Runtime environment switching
export ADRI_ENV=production  # Automatically uses prod config

@adri_protected(
    contract="customer_data",
    environment="production"  # Explicit environment override
)
def process_production_data(data):
    return processed_data

Customer Value:

• Safety: Prevents accidentally deploying development-grade quality rules to production
• Consistency: Standardized quality requirements across environments
• DevOps Integration: Works with GitOps and infrastructure-as-code workflows

3. Reasoning Logs (Local JSONL)

Problem Solved: AI decision-making processes are often black boxes with no audit trail.

Enterprise Solution: Optional local JSONL logging of prompt/response metadata.

from adri import adri_protected

@adri_protected(
    contract="credit_decisions",
    reasoning_mode=True,           # Enable AI reasoning logging
    store_prompt=True,             # Log AI prompts
    store_response=True,           # Log AI responses
    llm_config={
        "model": "claude-3-5-sonnet",
        "temperature": 0.1,
        "seed": 42
    }
)
def assess_credit_risk(customer_data):
    # AI reasoning steps are automatically logged
    risk_assessment = ai_model.analyze_credit_risk(customer_data)
    return risk_assessment

# Optional JSONL audit logs:
# - adri_reasoning_prompts.jsonl
# - adri_reasoning_responses.jsonl

Customer Value:

• Debugging: Retain prompts/responses alongside quality results
• Traceability: Persist per-run artifacts in JSONL for later review

4. Workflow Context Metadata

Problem Solved: Data quality assessments are isolated from broader workflow context.

Enterprise Solution: Attach workflow context metadata to an assessment.

from adri import adri_protected

# Example workflow context
@adri_protected(
    contract="transaction_processing",
    workflow_context={
        "run_id": "run_20250116_143022_abc123",
        "workflow_id": "payment_processing_pipeline",
        "workflow_version": "2.1.0",
        "step_id": "fraud_detection",
        "step_sequence": 3,
        "run_at_utc": "2025-01-16T14:30:22Z"
    },
    data_provenance={
        "source_type": "database",
        "database": "transactions_prod",
        "table": "payment_events",
        "extracted_at": "2025-01-16T14:25:00Z",
        "record_count": 15000
    }
)
def detect_transaction_fraud(transaction_data):
    fraud_scores = ml_model.predict_fraud(transaction_data)
    return fraud_scores

# Metadata is available to logging / callbacks for downstream correlation.

Customer Value:

• Correlation: Link assessments to workflow runs and steps
• Debugging: Carry “what run/step produced this data?” through logs

5. Production-Grade Performance

Problem Solved: Open source logging can be too slow for high-throughput production systems.

Enterprise Solution: Optimized performance with batching, caching, and async processing.

from adri import adri_protected

# Performance optimizations are automatic:
@adri_protected(contract="high_volume_processing")
def process_high_volume_data(data):
    return processed_data

# Behind the scenes:
# ✅ License validation cached for 24 hours
# ✅ Fast-path manifest storage provides faster assessment-id availability

Performance Benchmarks: See test suite for performance checks and thresholds.

Enterprise Deployment Patterns

Pattern 1: Team Development Workflow

# Development team using shared Verodat workspace
from adri import adri_protected

@adri_protected(
    contract="user_behavior_analysis",
    environment="development",     # Uses dev quality thresholds
    reasoning_mode=True           # Enables reasoning-mode behavior
)
def analyze_user_behavior(user_data):
    insights = ai_model.analyze_behavior(user_data)
    return insights

# Benefits:
# - Consistent dev environment config
# - Optional reasoning logs

Pattern 2: Production AI Pipeline

# Production deployment with strict quality controls
from adri import adri_protected

@adri_protected(
    contract="risk_assessment",
    environment="production",      # Uses prod quality thresholds
    reasoning_mode=True,          # Required for compliance
    min_score=95,                 # Strict production requirements
    on_failure="raise",           # Block processing on quality failure
    workflow_context=workflow_metadata,
    data_provenance=data_source_info
)
def assess_financial_risk(customer_portfolio_data):
    risk_scores = risk_model.predict_risk(customer_portfolio_data)
    return risk_scores

# Benefits:
# - Strict quality enforcement protects production systems
# - Complete audit trail for regulatory compliance
# - Workflow integration provides operational visibility
# - Data lineage tracking for compliance and debugging

Pattern 3: Regulated Industry Compliance

# Healthcare/Finance with full audit requirements
from adri import adri_protected

@adri_protected(
    contract="patient_data_processing",
    reasoning_mode=True,          # Required: AI reasoning audit trail
    store_prompt=True,            # Required: Store all AI prompts
    store_response=True,          # Required: Store all AI responses
    data_provenance={
        "source_type": "ehr_system",
        "patient_consent_id": "consent_12345",
        "data_classification": "phi",
        "retention_policy": "7_years"
    },
    min_score=98,                # Very strict for patient data
    on_failure="raise"           # Block processing on any quality issue
)
def process_patient_diagnosis_data(patient_data):
    diagnosis_insights = medical_ai.analyze_symptoms(patient_data)
    return diagnosis_insights

# Notes:
# This shows parameterization for stricter controls; any compliance posture depends on your
# own policies and how you retain/secure these logs.

Installation & Setup

Requirements

• Python: 3.10+ (same as open source)
• Verodat API Key: Required for enterprise features
• Dependencies: Same as open source + requests for API integration

Get Your API Key

1. Create account at verodat.com
2. Navigate to Account Settings → API Keys
3. Generate new API key for your organization
4. Set environment variable: export VERODAT_API_KEY="your-key"

Install Enterprise Edition

# Remove open source version (if installed)
pip uninstall adri

# Install enterprise edition
pip install verodat-adri

# With optional backends
pip install verodat-adri[redis]      # Redis backend for fast-path logging

Verify Installation

from adri import adri_protected
import pandas as pd

# Test with sample data
test_data = pd.DataFrame({"id": [1, 2, 3], "value": [10, 20, 30]})

@adri_protected(contract="installation_test")
def test_installation(data):
    return len(data)

result = test_installation(test_data)
print(f"✅ Enterprise ADRI working! Processed {result} records")

Configuration

Environment Variables

# Required
export VERODAT_API_KEY="your-enterprise-api-key"

# Optional
export VERODAT_API_URL="https://api.verodat.com/api/v1"  # Custom API endpoint
export ADRI_ENV="production"                            # Environment selection

Enterprise Configuration File

ADRI/config.yaml (Environment-aware structure):

adri:
  project_name: "customer_analytics_platform"
  default_environment: "development"

  environments:
    development:
      paths:
        contracts: "./dev/contracts"
        assessments: "./dev/assessments"
        training_data: "./dev/training-data"
        audit_logs: "./dev/audit-logs"
      protection:
        default_min_score: 70
        default_failure_mode: "warn"

    production:
      paths:
        contracts: "./prod/contracts"
        assessments: "./prod/assessments"
        training_data: "./prod/training-data"
        audit_logs: "./prod/audit-logs"
      protection:
        default_min_score: 85
        default_failure_mode: "raise"

Usage with Configuration

from adri import adri_protected

# Automatically uses production config when ADRI_ENV=production
@adri_protected(
    contract="customer_data",
    environment="production"  # Explicit production environment
)
def process_production_customers(data):
    return enhanced_data

Advanced Enterprise Features

1. AI Reasoning Validation and Logging

Track every AI decision for compliance and debugging:

@adri_protected(
    contract="ai_credit_decisions",
    reasoning_mode=True,           # Enable AI reasoning tracking
    store_prompt=True,             # Log all AI prompts for audit
    store_response=True,           # Log all AI responses for audit
    llm_config={
        "model": "claude-3-5-sonnet",
        "temperature": 0.1,
        "seed": 42,                 # Reproducible AI decisions
        "max_tokens": 2000
    }
)
def ai_credit_assessment(applicant_data):
    # Optional logs (depending on configuration):
    # - Local JSONL files
    credit_decision = ai_model.assess_creditworthiness(applicant_data)
    return credit_decision

Compliance Notes: This package provides logging hooks and audit artifacts, but regulatory compliance is program/process dependent and must be validated by your organization.

2. Data Provenance and Lineage Tracking

Track data sources through complex processing pipelines:

@adri_protected(
    contract="cross_platform_analytics",
    data_provenance={
        "source_type": "verodat_query",        # Data source type
        "verodat_query_id": 12345,             # Source query ID
        "verodat_account_id": 91,              # Source account
        "verodat_workspace_id": 161,           # Source workspace
        "dataset_id": 4203,                    # Source dataset
        "record_count": 15000,                 # Expected record count
        "extracted_at": "2025-01-16T14:30:00Z", # Extraction timestamp
        "data_classification": "confidential"  # Security classification
    }
)
def cross_platform_analysis(multi_source_data):
    # Data lineage automatically tracked to Verodat
    combined_insights = complex_analytics.process(multi_source_data)
    return combined_insights

Operational Benefits:

• Root Cause Analysis: Trace quality issues back to specific data sources
• Impact Assessment: Understand downstream effects of data source changes
• Compliance Reporting: Automated data lineage reports for auditors
• Quality Attribution: Identify which data sources contribute to quality issues

3. Workflow Orchestration Context

Integrate seamlessly with enterprise workflow platforms:

# Workflow example (pseudo-code)
# from prefect import flow, task
from adri import adri_protected

@task
@adri_protected(
    contract="payment_processing_step",
    workflow_context={
        "run_id": "{{run_id}}",               # Prefect run ID
        "workflow_id": "payment_pipeline",    # Workflow identifier
        "workflow_version": "2.1.0",          # Version tracking
        "step_id": "fraud_detection",         # Current step
        "step_sequence": 3,                   # Step number in workflow
        "run_at_utc": "{{run_timestamp}}"     # Execution timestamp
    }
)
def detect_payment_fraud(payment_transactions):
    fraud_scores = fraud_model.predict(payment_transactions)
    return fraud_scores

@flow(name="payment_processing_pipeline")
def payment_pipeline():
    raw_data = extract_payment_data()
    validated_data = detect_payment_fraud(raw_data)  # Enterprise ADRI protection
    process_validated_payments(validated_data)

Workflow Benefits:

• End-to-End Visibility: See data quality in context of complete business process
• Failure Attribution: Identify whether failures are data quality or business logic issues
• Performance Analytics: Understand data quality impact on overall workflow performance
• Operational Debugging: Trace issues across complex multi-step workflows

Performance & Reliability

Performance (Tested)

Reliability Features

Fault Tolerance:

• Network failures don't block data processing
• API timeouts are handled gracefully with retries
• Local logging continues even if cloud integration fails
• License validation cached to handle temporary API outages

Scalability:

• Batch processing for high-volume operations
• Concurrent workflow support with shared license caching
• Memory-efficient logging with configurable retention
• Horizontal scaling with Redis backend support

Getting Started

1. Quick Win: Centralized Logging

# Install and configure
pip install verodat-adri
export VERODAT_API_KEY="your-key"

# Your existing code gets centralized logging **when Verodat upload is enabled**
# No code changes needed!

2. Enable Environment Separation

# Create ADRI/config.yaml with dev/prod environments
# Set ADRI_ENV=production in production deployment
# Automatic environment-aware contract resolution

3. Add AI Reasoning Audit Trail

# Add reasoning_mode=True to AI processing functions
@adri_protected(contract="ai_decisions", reasoning_mode=True)
def ai_processing_function(data):
    return ai_results

4. Integrate with Workflows

# Add workflow_context to track end-to-end data flow
@adri_protected(
    contract="workflow_step",
    workflow_context={"run_id": run_id, "step": "validation"}
)
def workflow_step(data):
    return results

Support & Resources

Documentation

• Complete Documentation - Comprehensive feature documentation
• API Reference - Complete API documentation
• Enterprise Setup Guide - Detailed deployment guide

Community & Support

• Issues: GitHub Issues
• Enterprise Support: support@verodat.com
• Community: ADRI Standard Community

Training & Onboarding

• Free Onboarding Session: Schedule with Verodat Customer Success
• Workshop Materials: Contact enterprise support for team training

License & Pricing

• Package License: Apache-2.0
• Enterprise Features: Verodat cloud upload and licensed enterprise capabilities require a valid Verodat API key
• Open Source: Always free at github.com/adri-standard/adri
• Enterprise Pricing: Contact Verodat Sales for enterprise pricing

When to Choose Enterprise vs Open Source

Choose Open Source ADRI when:

• Individual developer or small team (≤3 people)
• Development/testing environments only
• Basic data quality validation needs
• Cost is primary concern
• No compliance requirements

Choose Enterprise verodat-adri when:

• Production AI workflows requiring reliability
• Team collaboration and centralized monitoring needs
• Regulated industry with compliance requirements
• Complex workflows requiring integration with orchestration platforms
• Need for audit trails and data provenance tracking
• Enterprise security and access control requirements

Ready to upgrade your data quality infrastructure?

Get started: verodat.com/adri-enterprise

Built by Verodat - Enterprise data infrastructure for AI workflows