In this blog, we will explore how data annotation works across voice, text, image, and video, why quality still...
Read MoreHigh-Quality Data That Powers Generative AI at Scale
We deliver curated, compliant, and scalable training data across multimodal data built to meet the real-world demands of Gen AI.
Trusted Data for Intelligent Systems
Digital Divide Data delivers high-quality, ethically sourced, and expertly curated datasets that power next-generation Generative AI models. From language and speech to vision and multimodal systems, we help AI teams build reliable, scalable, and globally representative training data.
Data Collection & Curation for Generative AI
Language & Code Data
High-precision text and document datasets curated to meet the rigorous demands of modern Generative AI and LLM development.
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We collect, clean, structure, and enrich data across domains, languages, and formats, ensuring consistency, accuracy, and compliance. Our teams support everything from pretraining corpora to domain-specific fine-tuning datasets.
Sample Data Types that we collect:- Prompt & Instruction Datasets
- Financial & Business Documents
- Invoices, Receipts & Statements
- Forms, Contracts & Reports
- Technical & Source Code Data
- Multilingual & Low-Resource Language Text
Conversational AI Data
Diverse, real-world speech and audio datasets designed to improve the performance, robustness,
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and inclusivity of speech-enabled GenAI systems. We source and annotate audio data across languages, accents, demographics, and acoustic conditions, supporting conversational AI and multimodal applications.
Sample Data Types that we collect:
- Customer Service & Call Center Audio
- Telehealth & Medical Conversations
- Podcast & Media Transcripts
- Lecture & Educational Recordings
- Voice Messages & Commands
- Ambient & Environmental Audio
Multimodal Data
High-quality visual and sensor datasets curated to train, fine-tune, and evaluate vision and multimodal Generative AI models.
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From image and video collection to frame-level annotation and metadata enrichment, we support complex use cases with strict quality and privacy controls.
Sample Data Types that we collect:
- Self-Captured Camera Recordings
- Retail & Product Images
- Surveillance & Traffic Footage
- Autonomous Vehicle Sensor Data
- Facial & Biometric Data
- Sports & Action Videos
Data Solutions for Every Model at Every Scale
Foundation Models
We support foundation model development by delivering high-volume, high-variance data collected ethically, curated rigorously, and scaled securely.
Enterprise models
We partner with enterprises to build custom data collection and curation programs that align with internal policies, industry regulations, and performance goals.
Fully Managed, End-to-End Data Collection Pipeline
Digital Divide Data manages the complete data lifecycle, so your teams can focus on building and scaling Generative AI models.
Why Choose DDD?
Strategic
We go beyond execution. Our teams bring domain expertise, data strategy, and a deep understanding of model training, governance, and security requirements.
Reliable
With a global workforce operating year-round across time zones, we deliver consistent, high-quality data at scale, when and where you need it.
Consistent
We believe in long-term partnerships. Dedicated teams stay with your project, build expertise over time, and scale seamlessly as your needs grow.
Flexible
Platform-agnostic by design. We integrate with your tools, workflows, and infrastructure, never forcing proprietary systems.
What Our Clients Say
DDD’s Commitment to Security & Compliance
Your data is protected at every stage through rigorous global standards and secure operational practices.
SOC 2 Type II
Verified controls across security, confidentiality, and system reliability
ISO 27001
Comprehensive information security management framework
GDPR & HIPAA Compliance
Responsible handling of personal and sensitive data
TISAX Alignment
Automotive-grade security for mobility and vehicle-AI workflows
Blogs
Deep dive into the latest technologies and methodologies that are shaping the future of Gen AI.
Video Annotation for Generative AI: Challenges, Use Cases, and Recommendations
This blog examines video annotation for Generative AI and outlines core challenges, explores modern annotation, highlights practical use cases...
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Major Challenges in Large-Scale Data Annotation for AI Systems
This blog explores the major challenges that organizations face when annotating data at scale. From the difficulty of managing...
Read MoreBuild Better AI with Data You Can Trust
Partner with DDD to collect, curate, and scale the data that powers next-generation AI.
Frequently Asked Questions
What types of AI models does DDD support?
DDD supports both foundation models and enterprise AI models, including large language models (LLMs), multimodal models, generative image and video systems, speech and voice models, and domain-specific AI applications.
Can DDD handle large-scale data collection for foundation models?
Yes. DDD specializes in high-volume, globally diverse, multimodal data collection across text, image, audio, video, and sensor data, designed to meet the scale, variability, and quality requirements of foundation model training.
How does DDD support enterprise-specific AI use cases?
For enterprise models, DDD builds custom, proprietary, and highly controlled data pipelines aligned to your domain, workflows, and regulatory requirements, ensuring data quality, security, and governance at every stage.
Can DDD support multilingual and global data collection?
Absolutely. Our global workforce enables multilingual and culturally diverse data collection, supporting AI systems designed for international and localized deployment.
How does DDD ensure data quality and consistency?
DDD applies multi-layer quality assurance processes, including sampling, validation, enrichment, and feedback loops. Dedicated project teams remain consistent over time to maintain quality and institutional knowledge.
How does DDD handle sensitive or proprietary data?
All sensitive datasets are managed within controlled facilities, with strict access controls, encryption, workforce training, and compliance with global security standards.
How quickly can a data collection project be launched?
Project timelines vary by scope and complexity, but DDD is designed for rapid kickoff and scalable execution, with dedicated teams and proven operational frameworks.