Abstract

Data annotation represents one of the most underestimated cost centers in enterprise AI development. While organizations meticulously budget for infrastructure, talent, and model training, annotation costs frequently emerge as budget-breaking surprises that derail otherwise promising AI initiatives. In my fourteen years of software development and seven years of AI research, I have observed annotation economics single-handedly determine project viability more often than any other factor.

This article presents a comprehensive economic framework for annotation decision-making, analyzing the crowdsourcing versus expert labeling dichotomy through the lens of total cost of ownership, quality-adjusted returns, and long-term strategic implications. Drawing on case studies from healthcare, autonomous vehicles, financial services, and manufacturing, I demonstrate that the optimal annotation strategy varies dramatically by domain, with the cost per correctly labeled sample ranging from $0.01 to $500 depending on complexity and required expertise.

The research reveals that organizations systematically underestimate annotation costs by 40-60% on average, primarily due to failure to account for quality control overhead, iteration cycles, and the hidden costs of label noise propagating through model training. I propose a decision framework incorporating domain complexity indices, error cost multipliers, and regulatory compliance factors that enables practitioners to make economically optimal annotation sourcing decisions.

Keywords: data annotation, crowdsourcing, expert labeling, annotation economics, data labeling costs, machine learning data, enterprise AI, annotation quality

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1. Introduction: The Annotation Cost Iceberg

In my experience at Capgemini Engineering, I have watched sophisticated AI initiatives collapse not from algorithmic failures or infrastructure limitations, but from a deceptively simple problem: the organization ran out of budget before accumulating sufficient labeled data. The annotation cost iceberg phenomenon — where visible upfront costs represent merely 20-30% of total annotation expenditure — consistently surprises even experienced AI practitioners.

The economics of data annotation exist at a fascinating intersection of labor economics, quality engineering, and machine learning theory. Every labeled data point carries embedded costs far beyond the direct payment to annotators: quality assurance overhead, tool infrastructure, iteration cycles for ambiguous cases, and the downstream costs of label errors propagating through model training.

Consider this fundamental tension: a single incorrectly labeled medical image in a cancer detection dataset might cost $2 to produce but could lead to model behaviors causing millions in liability. Conversely, over-investing in expert annotation for a sentiment analysis task might consume budget that would have been better allocated to simply collecting more data with acceptable noise levels.

This article provides a rigorous economic framework for navigating these tradeoffs, enabling practitioners to make annotation sourcing decisions that optimize for total cost of ownership while meeting quality requirements specific to their domain and use case.

2. The Annotation Cost Landscape

2.1 Direct Cost Components

Understanding annotation economics requires decomposing costs into their constituent elements. Direct costs encompass:

Per-Unit Labeling Costs:

Annotation Type	Crowdsource Rate	Expert Rate	Ratio
Binary Classification	$0.01-0.05	$0.10-0.50	10x
Multi-class (10 classes)	$0.03-0.10	$0.25-1.00	8x
Bounding Box	$0.05-0.20	$0.50-2.00	10x
Semantic Segmentation	$0.50-2.00	$5.00-20.00	10x
Medical Imaging	$1.00-5.00	$25.00-100.00	25x
Legal Document Review	$0.50-2.00	$10.00-50.00	20x
Audio Transcription (min)	$0.50-1.00	$2.00-5.00	4x

Quality Control Overhead: Quality assurance typically adds 30-100% to base annotation costs. The relationship follows a non-linear pattern:

graph LR
    subgraph \"Quality Control Cost Multipliers\"
    A[Base
Annotation] -->|+30%| B[Single
Review]
    B -->|+50%| C[Dual
Review]
    C -->|+40%| D[Expert
Adjudication]
    D -->|+20%| E[Statistical
Audit]
    end
    
    style A fill:#e8f5e9
    style B fill:#fff9c4
    style C fill:#ffe0b2
    style D fill:#ffccbc
    style E fill:#f8bbd9

2.2 Hidden Cost Categories

During my research at ONPU, I have identified seven categories of hidden annotation costs that organizations routinely underestimate:

1. Guideline Development (15-25% of project cost)
Creating unambiguous annotation guidelines requires iterative refinement through pilot batches. A financial services client spent $180,000 over four months developing annotation guidelines for transaction fraud classification before labeling their first production sample.

2. Edge Case Resolution (10-20% of project cost)
Ambiguous samples requiring escalation to domain experts or stakeholder committees represent disproportionate cost. In autonomous vehicle projects, edge cases constitute 5% of images but 40% of annotation budget.

3. Iteration and Rework (20-40% of project cost)
Model feedback revealing systematic annotation errors triggers costly rework cycles. An e-commerce recommendation system required three complete re-annotation passes after discovering annotator drift in product categorization.

4. Tool Infrastructure (5-15% of project cost)
Annotation platforms, custom interfaces, and integration with ML pipelines require ongoing investment. Specialized domains like medical imaging demand HIPAA-compliant infrastructure adding $50,000-200,000 annually.

5. Annotator Management (10-15% of project cost)
Recruitment, training, performance monitoring, and workforce management add overhead regardless of sourcing strategy.

6. Inter-Annotator Agreement Measurement (5-10% of project cost)
Statistical measurement of annotation consistency through overlap samples reduces productive output while being essential for quality assurance.

7. Label Error Downstream Costs (highly variable)
This represents the most significant and least quantified hidden cost. Label noise propagates through model training in non-linear ways — 5% label noise can reduce model accuracy by 2-15% depending on the error distribution relative to decision boundaries.

2.3 Total Cost of Ownership Model

Integrating direct and hidden costs yields the Annotation Total Cost of Ownership (ATCO) formula:

For a 100,000 sample image classification project at $0.10 per label:

Component	Crowdsource	Expert
Base Labeling	$10,000	$50,000
QC Multiplier (1.5x / 1.2x)	$15,000	$60,000
Guideline Development	$25,000	$15,000
Edge Cases	$8,000	$12,000
Rework (30% / 10%)	$4,500	$6,000
Tools	$5,000	$8,000
Management	$6,000	$10,000
IAA Measurement	$3,000	$4,000
Direct Total	$76,500	$165,000

The 2.2x cost ratio for expert annotation frequently delivers 3-5x quality-adjusted returns in high-stakes domains, fundamentally changing the economic calculus.

3. Crowdsourcing Economics

3.1 The Crowdsourcing Value Proposition

Crowdsourcing annotation through platforms like Amazon Mechanical Turk, Scale AI, Labelbox, or Appen offers compelling economics for appropriate use cases. The fundamental value proposition rests on four pillars:

• Scale Economics: Crowdsourcing can mobilize thousands of annotators simultaneously, enabling annotation throughput measured in millions of samples per week for well-structured tasks.
• Cost Efficiency: Labor cost arbitrage across global markets and the elimination of employment overhead reduce per-unit costs by 5-20x compared to in-house expert teams.
• Elasticity: Project-based scaling eliminates fixed costs associated with maintaining annotation capacity during low-demand periods.
• Diversity: Multiple independent annotators provide natural noise reduction through aggregation, particularly valuable when individual annotator reliability is uncertain.

3.2 When Crowdsourcing Succeeds

flowchart TD
    A[Task
Characteristics] --> B{Domain
Expertise
Required?}
    B -->|No| C{Clear
Guidelines
Possible?}
    B -->|Yes| D[Expert
Annotation]
    C -->|Yes| E{Error Cost
Tolerance?}
    C -->|No| D
    E -->|High| F[Crowdsource
with QC]
    E -->|Low| G{Budget
Constraints?}
    G -->|Tight| H[Crowdsource
+ Expert Review]
    G -->|Flexible| D
    
    style F fill:#c8e6c9
    style D fill:#bbdefb
    style H fill:#fff9c4

Crowdsourcing delivers optimal economics when:

1. Tasks require common knowledge: Sentiment analysis, object recognition of everyday items, content moderation for obvious violations, and transcription of clear speech represent ideal crowdsourcing candidates. A social media platform I consulted for achieved 94% accuracy on content toxicity classification using crowdsourcing at $0.02 per judgment with three-way majority voting.

2. Ground truth is unambiguous: Tasks with clear correct answers enable reliable quality control through gold standard questions. Image classification for animals, vehicles, or household objects achieves crowdsource accuracy exceeding 95% when guidelines specify clear category definitions.

3. Error costs are bounded: Applications where individual prediction errors have limited impact can tolerate the noise inherent in crowdsourced labels. Product recommendation systems, content personalization, and advertising targeting typically fall in this category.

4. Volume requirements are massive: When annotation needs exceed hundreds of thousands of samples, crowdsourcing scale economics become decisive. Autonomous vehicle companies label millions of images annually — expert annotation at such scale would require annotation teams larger than most AI organizations’ entire headcount.

3.3 Crowdsourcing Failure Modes

My experience across multiple failed crowdsourcing initiatives reveals systematic failure patterns:

3.4 Quality-Cost Optimization in Crowdsourcing

Effective crowdsourcing requires systematic quality engineering:

Redundancy Strategies:

Strategy	Cost Multiplier	Accuracy Gain	Best For
3-way Majority	3.0x	+5-10%	Binary/simple tasks
5-way Majority	5.0x	+8-15%	Multi-class tasks
Weighted Voting	3.5x	+10-18%	Known annotator reliability
Dawid-Skene Model	3.0x	+12-20%	Systematic bias correction
Expert Adjudication	3.5x	+15-25%	High-stakes decisions

Gold Standard Injection: Embedding 5-10% known-answer questions enables real-time annotator reliability estimation. This technique reduced effective error rates by 35% in a product categorization project while identifying and removing unreliable annotators.

Annotator Qualification: Pre-screening through qualification tests filters for task-appropriate workers. For a medical image annotation project adapted for crowdsourcing, requiring annotators to pass a 50-question anatomy identification test improved accuracy from 72% to 89%.

4. Expert Annotation Economics

4.1 The Expert Premium

Expert annotation commands premium pricing reflecting specialized knowledge, professional liability, and scarcity. The economics of expert annotation differ fundamentally from crowdsourcing:

• Fixed Cost Structures: Expert annotators typically require employment relationships or substantial minimum engagements, creating fixed cost components absent from crowdsourcing. A radiology annotation team requires baseline staffing regardless of annotation volume, fundamentally changing project economics.
• Quality Ceiling Effects: While crowdsourcing accuracy plateaus around 85-95% regardless of investment, expert annotation can achieve 97-99%+ accuracy for well-defined tasks. This quality ceiling difference justifies premium pricing in high-stakes applications.
• Knowledge Transfer Value: Expert annotators provide value beyond labels — their edge case observations, guideline refinements, and domain insights accelerate model development. A senior pathologist annotating histology images identified twelve distinct artifact types requiring guideline additions, knowledge that would have taken months to discover through model error analysis.

4.2 Expert Sourcing Strategies

flowchart LR
    subgraph \"Expert Sourcing Options\"
    A[In-House
Team] --> B[Full Control
High Fixed Cost]
    C[Contract
Experts] --> D[Flexible
Premium Rates]
    E[Academic
Partners] --> F[Research Access
Slower Timelines]
    G[Professional
Networks] --> H[Domain Depth
Availability Limits]
    end
    
    style A fill:#e3f2fd
    style C fill:#fff3e0
    style E fill:#f3e5f5
    style G fill:#e8f5e9

In-House Expert Teams: Building dedicated annotation capacity provides maximum control and knowledge retention but requires significant fixed investment. A medical AI company maintaining a team of six radiologists for annotation incurs $1.2M annually in fully-loaded costs but achieves annotation quality and throughput unmatched by external options.

Contract Domain Experts: Engaging professionals on a per-project basis offers flexibility but commands premium rates. Contract radiologists for medical imaging annotation charge $100-300 per hour, translating to $25-75 per complex image annotation including review time.

Academic Partnerships: University collaborations provide access to expert knowledge at reduced cost in exchange for research access and publication rights. A partnership with ONPU’s Department of Economic Cybernetics provided expert annotation for financial modeling datasets at 40% of market rates while generating three co-authored publications.

Professional Network Sourcing: Platforms like Doximity (healthcare), Upwork (general professional), and industry-specific networks enable project-based expert engagement. Quality variance is high; rigorous qualification testing is essential.

4.3 When Expert Annotation is Non-Negotiable

Certain domains categorically require expert annotation regardless of cost:

1. Regulated Industries: Healthcare, financial services, and legal applications face regulatory scrutiny of training data. FDA guidance on AI/ML medical devices emphasizes data quality assurance; expert annotation provides audit-defensible documentation.
2. Safety-Critical Systems: Autonomous vehicles, industrial robotics, and medical diagnosis systems where errors cause physical harm demand annotation quality exceeding crowdsourcing capability.
3. Professional Judgment Tasks: Annotations requiring professional licensing — radiological findings, legal interpretations, financial audit judgments — can only be performed by credentialed experts.
4. High-Stakes Low-Volume: When prediction errors carry extreme costs but volume is manageable, expert annotation cost premium is trivial relative to error costs. Detecting manufacturing defects in aerospace components represents a canonical example.

4.4 Expert Annotation Efficiency Optimization

Expert time is expensive; maximizing productive annotation requires careful workflow design:

• Pre-Processing Automation: Automated quality filters, image normalization, and document parsing reduce expert time spent on preparation. An OCR preprocessing pipeline reduced legal document annotation time by 35%.
• Tiered Difficulty Routing: Routing straightforward cases to junior reviewers or automated systems reserves expert attention for complex cases. A medical imaging pipeline using ML confidence scoring routes 60% of cases to automated adjudication, 30% to junior reviewers, and only 10% to senior radiologists.
• Annotation Interface Optimization: Purpose-built annotation interfaces with keyboard shortcuts, smart suggestions, and workflow automation can double expert annotation throughput. Investment of $50,000 in interface development for a pathology annotation project paid back within three months through productivity gains.

5. The Hybrid Approach: Optimizing the Crowdsource-Expert Mix

5.1 Stratified Annotation Strategies

The optimal annotation strategy rarely involves pure crowdsourcing or pure expert annotation. Hybrid approaches allocate resources based on sample characteristics:

flowchart TD
    A[Incoming
Sample] --> B{ML Confidence
Score}
    B -->|High >0.95| C[Auto-Label
with Audit]
    B -->|Medium 0.7-0.95| D[Crowdsource
3-way Vote]
    B -->|Low <0.7| E{Domain
Complexity}
    E -->|Standard| D
    E -->|Complex| F[Expert
Annotation]
    
    C --> G[Production
Dataset]
    D --> H{Agreement
Level}
    H -->|Unanimous| G
    H -->|Split| F
    F --> G
    
    style C fill:#c8e6c9
    style D fill:#fff9c4
    style F fill:#bbdefb

5.2 Active Learning Integration

Active learning algorithms that identify informative samples for annotation enable further optimization:

• Uncertainty Sampling: Prioritizing samples where model predictions are uncertain focuses expert annotation budget on decision boundary refinement. This approach reduced annotation requirements by 40% for a credit risk model while achieving equivalent accuracy.
• Diversity Sampling: Selecting samples that expand feature space coverage ensures annotation budget addresses data gaps rather than redundant examples.
• Expected Model Change: Annotating samples expected to most change model parameters maximizes learning per annotation dollar.
• Economic Model Change: Extending expected model change to incorporate error costs prioritizes annotations with highest expected value. For fraud detection, annotating transactions near the fraud decision boundary delivers 5x the value of annotating clearly legitimate transactions.

5.3 Quality-Cost Frontier Analysis

Optimal annotation strategy lies on the quality-cost Pareto frontier:

graph LR
    subgraph \"Quality-Cost Frontier\"
    A((Low Cost
Low Quality
Basic Crowd)) 
    B((Medium Cost
Good Quality
Crowd + QC))
    C((Higher Cost
High Quality
Hybrid))
    D((Premium Cost
Excellent Quality
Expert))
    E((Maximum Cost
Perfect Quality
Expert + Audit))
    end
    
    A --> B --> C --> D --> E
    
    style A fill:#ffcdd2
    style B fill:#fff9c4
    style C fill:#c8e6c9
    style D fill:#bbdefb
    style E fill:#e1bee7

The frontier position optimal for a given project depends on:

• Error cost structure: Higher error costs shift optimum toward quality
• Volume requirements: Higher volumes favor lower unit cost approaches
• Regulatory requirements: Compliance mandates may set quality floors
• Time constraints: Speed requirements may preclude expert-dependent approaches

6. Case Studies in Annotation Economics

6.1 Case Study: Healthcare AI Startup

Context: A medical imaging AI company developing breast cancer detection algorithms required annotation of 500,000 mammograms.

Initial Approach (Failed): Crowdsourcing through a medical annotation platform at $2 per image with quality control. Total budget: $1.5M.

Results: Inter-annotator agreement of 0.61 (Fleiss’ kappa) for malignancy classification — below the 0.70 threshold for clinical utility. Model trained on crowdsourced labels achieved 0.82 AUC versus published state-of-art 0.94 AUC.

Revised Approach:

• Tiered annotation: residents for initial read ($15/image), attending radiologists for positive cases and disagreements ($50/image)
• Active learning to prioritize informative samples
• Total budget: $4.2M for 200,000 high-quality annotations

Outcome: Model achieved 0.93 AUC, enabling FDA 510(k) clearance. The $4.2M investment enabled a $45M Series B funding round; the failed $1.5M crowdsourcing attempt would have precluded regulatory approval and company survival.

Cross-Reference: This case illustrates principles discussed in Data Quality Economics and Cost-Benefit Analysis for Healthcare AI.

6.2 Case Study: E-Commerce Product Classification

Context: An online marketplace required categorization of 50 million product listings into 10,000+ categories.

Strategy:

• Automated classification for 70% of products using existing category signals
• Crowdsourcing for clear cases (20%) at $0.03 per product
• Internal merchandising team for brand-sensitive categories (10%)

Tier	Volume	Method	Cost
Auto-classify	35M	ML + Rules	$50K
Clear Cases	10M	Crowdsource	$300K
Sensitive	5M	Internal Team	$1.2M
Total	50M		$1.55M

Outcome: Overall classification accuracy of 94%, with 99% accuracy in sensitive categories. Pure crowdsourcing would have achieved ~88% accuracy with significant brand misclassification complaints.

6.3 Case Study: Financial Services NLP

Context: A bank required annotation of 2 million customer communications for intent classification and entity extraction.

Compliance Constraint: Regulatory requirements mandated that annotators be trained on financial services regulations, eliminating standard crowdsourcing options.

Solution:

• Partnership with business process outsourcing firm with financial services training programs
• Custom annotation interface with compliance guideline integration
• Quality assurance by internal compliance team

Component	Cost
Annotation (2M @ $0.35)	$700K
Interface Development	$150K
Training Program	$80K
Compliance Review	$200K
Total	$1.13M

Outcome: Intent classification accuracy of 91% with full regulatory audit trail. Attempted crowdsourcing pilot achieved only 74% accuracy due to financial terminology unfamiliarity.

7. The Economics of Annotation Quality

7.1 Label Noise Propagation

The relationship between annotation quality and model performance follows non-linear dynamics that make quality economics counterintuitive:

• Symmetric Noise Impact: For binary classification with symmetric label noise rate ε, theoretical optimal accuracy is bounded by (1-2ε). With 10% label noise, maximum achievable accuracy is 80% regardless of model sophistication.
• Asymmetric Noise Impact: Real annotation errors are rarely symmetric. If false negatives are more common than false positives (typical in imbalanced datasets), models learn systematic biases that may not be apparent in aggregate metrics but cause severe performance degradation in deployment.
• Decision Boundary Corruption: Label errors near decision boundaries corrupt model learning disproportionately to their frequency. A 5% error rate concentrated near boundaries can reduce model performance equivalently to 20% random noise.

7.2 Quality-Adjusted Cost Analysis

The true cost of annotation must account for quality:

The squared denominator reflects non-linear error propagation.

Approach	Direct Cost	Error Rate	QAC
Basic Crowdsource	$50K	15%	$69K
Crowdsource + QC	$80K	8%	$94K
Hybrid	$150K	4%	$163K
Expert	$250K	2%	$260K

7.3 Error Cost Integration

Complete annotation economics requires integrating downstream error costs:

Healthcare Example:

• Annotation options: Crowdsource ($500K, 12% error) vs Expert ($2M, 3% error)
• Prediction volume: 1 million diagnoses annually
• Cost per diagnostic error: $10,000 (misdiagnosis liability, treatment costs)

Approach	Annotation	Annual Error Cost	5-Year Total
Crowdsource	$500K	$1.2B	$6.5B
Expert	$2M	$300M	$1.5B

The expert approach reduces total cost by 77% despite 4x higher annotation investment.

8. Emerging Trends in Annotation Economics

8.1 Synthetic Data Generation

Generative models increasingly supplement human annotation:

Economics of Synthetic Data:

• Generation cost: $0.001-0.01 per sample
• Quality limitation: Distribution match to real data
• Use case: Data augmentation, rare event synthesis, privacy-preserving training

A fraud detection project generated synthetic fraud transactions at $0.005 per sample, expanding fraud examples from 10,000 to 500,000 and improving model recall by 23%.

Limitations: Synthetic data cannot replace human annotation for novel domains without real data foundations. Models trained purely on synthetic data exhibit distribution shift vulnerabilities.

8.2 Self-Supervised Learning Impact

Self-supervised pretraining reduces annotation requirements:

Foundation Model Economics:

• Pretrained models (BERT, GPT, CLIP, etc.) encode general knowledge from massive unlabeled corpora
• Fine-tuning requires 10-100x fewer labeled examples than training from scratch
• Transfer learning from foundation models democratizes annotation economics

Cross-Reference: See Transfer Learning Economics for detailed analysis.

8.3 Human-AI Collaborative Annotation

AI-assisted annotation tools are transforming productivity:

Task Type	Traditional	AI-Assisted	Improvement
Image Segmentation	10 min/image	2 min/image	5x
Document Extraction	15 min/doc	4 min/doc	3.7x
Bounding Boxes	30 sec/object	5 sec/object	6x
Audio Transcription	4x real-time	1.5x real-time	2.7x

Cost Impact: AI assistance reduces per-unit expert costs by 60-80%, narrowing the crowdsource-expert cost gap and shifting the optimal strategy toward higher quality approaches.

8.4 Annotation-as-a-Service Evolution

The annotation market is professionalizing:

Market Structure:

• Enterprise providers (Scale AI, Appen, Labelbox) offer full-service annotation with quality guarantees
• Specialized vendors focus on domains (medical, legal, autonomous vehicles)
• Platform commoditization drives crowdsourcing costs toward $0.01 per simple task

Strategic Implications: Outsourcing annotation to professional providers reduces fixed costs and risk while sacrificing some domain knowledge accumulation. The build-vs-buy decision for annotation capacity parallels broader AI talent economics.

9. Decision Framework: Choosing Your Annotation Strategy

9.1 The Annotation Strategy Decision Matrix

9.2 Quantitative Decision Criteria

Crowdsourcing Appropriate When:

• Domain expertise requirement: Low (common knowledge tasks)
• Error cost per mistake: <$100
• Required accuracy: <95%
• Volume: >100,000 samples
• Timeline: <3 months for large volumes

Expert Annotation Required When:

• Regulatory compliance mandated
• Error cost per mistake: >$1,000
• Required accuracy: >97%
• Professional judgment required
• Safety-critical application

Hybrid Approach Optimal When:

• Mixed complexity distribution in data
• Budget constraints preclude full expert annotation
• Volume exceeds expert capacity but quality matters
• Active learning infrastructure available

9.3 Implementation Checklist

10. Conclusion: Annotation as Strategic Investment

The economics of data annotation fundamentally shape AI project outcomes. In my experience leading AI initiatives at Capgemini and conducting research at ONPU, I have observed that annotation strategy decisions made early in projects often determine ultimate success or failure.

The crowdsourcing versus expert dichotomy represents a false choice for most enterprise AI applications. Optimal strategies employ sophisticated routing that matches annotation resources to sample requirements, investing expert attention where it provides multiplicative returns while leveraging crowdsourcing scale for appropriate tasks.

Organizations that treat annotation as a commodity purchasing decision systematically underperform those that approach it as strategic investment. The latter develop annotation guidelines as intellectual property, build quality assurance capabilities as competitive advantages, and integrate annotation economics into AI project planning from inception.

The annotation economics framework presented in this article — incorporating total cost of ownership, quality-adjusted returns, and error cost integration — enables practitioners to make economically optimal decisions that balance cost efficiency with quality requirements specific to their domains and use cases.

As AI systems increasingly power critical business processes and safety-relevant applications, the importance of annotation economics will only grow. Organizations that master this discipline will build more effective AI systems at lower total cost, while those that optimize only for direct annotation costs will discover, too late, the true expense of label noise propagating through their production systems.

Cross-Reference: For related economic frameworks, see Data Quality Economics, Data Acquisition Costs, Hidden Costs of AI Implementation, and TCO Models for Enterprise AI.

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