Digital Divide Data (DDD) Tech Stack

AI data services and autonomy solutions with human-in-the-loop annotation

Professional Services New York 1,001–5,000 employees Founded 2001 Nonprofit

Digital Divide Data provides ML data operations—annotation, labeling, and model evaluation—for LLM and autonomous systems at Fortune 500 and defense-tech companies. The stack reveals a data-operations org: Labelbox, SuperAnnotate, and CVAT for annotation pipelines; SQL and Python for analytics; BI tools (Power BI, Tableau, Looker) for throughput visibility. Pain points (manual bottlenecks, workflow complexity, SLA misses) and active projects (automation tool adoption, accuracy dashboards) signal they're scaling throughput while fighting annotation inefficiency—a classic margin problem in human-feedback services.

Tech Stack 16 technologies

Core StackPython Power BI Tableau Looker Oracle NetSuite Jira Labelbox SuperAnnotate CVAT Excel SQL GPT Microsoft Office Google Workspace Google Sheets

What Digital Divide Data (DDD) Is Building

◆Challenges

Workflow complexity
Tooling inefficiencies
Manual annotation bottlenecks
Process inefficiencies in annotation
Meeting accuracy throughput timeline slas
Integrating ai tools into workflows
Optimizing learning technology issues
Ensuring compliance and data integrity
Meeting client kpis
Avoiding tax penalties

▲Active Projects

Process optimization using data trends
Automation tool adoption for annotation
Dashboard development for accuracy metrics
Ai learning curriculum deployment
Learning analytics dashboards
Localizing global solutions for africa
Capex reporting
Cash flow projections
Liquidity management
Lidar segmentation program

Hiring Activity

Decelerating8 roles · 2 in 30d

Department

Data

Finance

Ops

Support

Seniority

Manager

Mid

Senior

Junior

Lead

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About Digital Divide Data (DDD)

DDD is a nonprofit data services firm founded in 2001, headquartered in New York, with 1,001–5,000 employees distributed across Kenya, Cambodia, and Namibia. The core business: annotation, labeling, red teaming, and model evaluation for autonomous systems (fleet ops, navigation, V&V) and generative AI (NLP dataset creation, prompt engineering, output evaluation). DDD's Impact Sourcing model pairs commercial AI work with talent development—recruiting and upskilling youth from low-income backgrounds. Clients span Fortune 500, defense tech, government, autonomy companies, and AgTech. Current hiring is sparse and decelerating (2 roles in 30 days), concentrated in data operations and finance—reflecting either market contraction or a mature, stable staffing model.

HeadquartersNew York

Company Size1,001–5,000 employees

Founded2001

Hiring MarketsKenya, Cambodia, Namibia

Frequently Asked Questions

What annotation and labeling tools does Digital Divide Data use?

Labelbox, SuperAnnotate, and CVAT are primary platforms. Python and SQL handle data pipelines; Power BI, Tableau, and Looker track annotation accuracy and throughput metrics.

What is Digital Divide Data working on operationally?

Automation tool adoption for annotation workflows, accuracy metric dashboards, AI learning curriculum deployment, and process optimization to reduce manual bottlenecks and meet SLA timelines for clients.