Augmented AI (A2I)

Amazon Augmented AI (A2I) — Build human review workflows for machine learning predictions with confidence thresholds and quality control.

What is Amazon Augmented AI?

Amazon Augmented AI (A2I) is a managed service for adding human review to ML predictions. It makes it easy to build workflows where humans review low-confidence predictions, validate model outputs, or audit model performance — without building custom review systems or managing reviewers.

Key Insight: A2I is the “human-in-the-loop” service — when ML models aren’t confident, A2I routes predictions to humans for review.

Key Features

Feature	Description
Built-in Workflows	Pre-built for Amazon Textract and Amazon Rekognition
Custom Workflows	Integrate with any ML model (SageMaker, custom models)
Confidence Thresholds	Automatically trigger review when confidence is low
Multiple Reviewers	Route to multiple workers for consensus
Workforce Options	Private team, Mechanical Turk, or vendor workforce
Review UI Templates	60+ pre-built templates or create custom HTML/Liquid
Review Instructions	Provide guidance to reviewers within the UI
Audit Trail	Track all reviews and decisions

Use Cases

Document Processing

Route low-confidence form extractions from Textract to humans for verification before entering data into systems.

Content Moderation

Send borderline Rekognition results to human moderators to verify appropriateness before content goes live.

PII Redaction Validation

Have humans verify that sensitive information was correctly identified and masked in documents.

Model Performance Monitoring

Randomly sample predictions for human review to monitor model drift and accuracy over time.

Claims Processing

Route insurance claims with unusual patterns to human adjusters for review before auto-approval.

How It Works

1. Define Workflow: Choose built-in (Textract/Rekognition) or custom workflow

2. Set Review Conditions:

Confidence threshold (e.g., review if confidence < 80%)
Random sampling percentage (e.g., review 5% of all predictions)

3. Configure Workforce: Private team, Mechanical Turk, or AWS Marketplace vendor

4. Create Review Template: Use pre-built or custom UI for reviewers

5. Integrate with Application: Call A2I API when review is needed

6. Collect Results: Reviewers complete tasks, results returned to your application

# Example: Triggering A2I review for low-confidence Textract
if confidence_score < 0.8:
    a2i.start_human_loop(
        HumanLoopName='review-123',
        FlowDefinitionArn='arn:aws:...',
        HumanLoopInput={'InputContent': json_data}
    )

Pricing & Free Tier

Aspect	Details
Free Tier	✅ Part of $200 AWS Free Tier credits (starting July 15, 2025)
Rekognition Review	$0.02 -$ 0.03 per reviewed image
Textract Review	$0.02 -$ 0.03 per reviewed page
Custom Workflows	$0.02 -$ 0.08 per reviewed object
Workforce Costs	Additional — Mechanical Turk workers billed separately

Pricing Example: Using private workforce to review 10,000 Textract pages:

A2I fee: 10,000 × $0.03 =$ 300

Workforce cost: Internal employees (no additional cost)

Total: $300

Cost Tip: Use confidence thresholds strategically — reviewing 100% of predictions is expensive. Review only low-confidence or high-risk cases.

⚠️ Pricing Disclaimer: AWS pricing is subject to change. Free tier availability (500 reviews for first year) and pricing shown are based on information available as of January 2026. Always verify current pricing at the official Amazon A2I pricing page.

When to Use A2I

Use	Don’t Use
ML prediction validation	100% human review (just use MTurk)
Low-confidence routing	Real-time predictions requiring instant response
Model performance auditing	Simple pass/fail without ML context
Sensitive data review (private workforce)	Public data review at massive scale (use MTurk directly)

A2I vs Mechanical Turk

Aspect	Amazon A2I	Mechanical Turk
Purpose	ML prediction review	General crowdsourcing
Integration	Built-in for AWS AI services	Standalone marketplace
Workflow	Automated confidence-based routing	Manual HIT creation
Best For	ML validation pipelines	Data labeling, surveys

Workforce Options

Private Workforce: Your own employees (e.g., internal QA team)

Best for: Sensitive data, domain expertise required
Cost: No A2I workforce charges (just A2I service fee)

Amazon Mechanical Turk: 500K+ global workers

Best for: Non-sensitive data, scale, 24/7 availability
Cost: A2I fee + MTurk worker payments + MTurk fees

AWS Marketplace Vendors: Third-party data labeling services

Best for: Specialized tasks, managed service
Cost: A2I fee + vendor pricing

Built-in Workflows

Amazon Textract Integration:

Trigger review when form keys are missing
Review when confidence is below threshold
Validate extracted tables and key-value pairs

Amazon Rekognition Integration:

Moderate images with borderline content
Verify celebrity recognition
Review custom labels predictions

Custom Workflows (with any model):

Define custom review conditions in Lambda
Create custom review UI with HTML/Liquid
Integrate with SageMaker or external models

Important Notes

No Model Training: A2I doesn’t improve your models — it validates predictions
Human Review Only: A2I manages the review workflow, not the workforce itself
Audit Trail: All reviews are logged for compliance and quality tracking
Security: HIPAA-eligible, supports VPC, encryption at rest/transit
Integration Required: Not a standalone service — must be integrated with ML workflows

TL;DR

A2I = Managed human review for ML predictions
Features: Confidence-based routing, built-in workflows for Textract/Rekognition, custom workflows, multiple workforce options
Pricing: $0.02 -$ 0.08 per reviewed object + workforce costs
Best for: ML validation, low-confidence prediction review, model auditing
Works with: Textract, Rekognition, SageMaker, custom models
Key Differentiator: Automated ML-integrated workflows vs. manual crowdsourcing (MTurk)

Resources

Amazon Augmented AI Official product page and overview.

A2I Documentation Complete API reference and guides.

A2I Pricing Detailed pricing breakdown.

Lalit's Cloud & DevOps notes