High-Quality Arabic Dialect Annotation at Enterprise Scale
Karama Data delivers rigorously quality-controlled Arabic language annotation for NLP, ASR, and conversational AI systems — with a workforce invested in the outcomes they produce.
Arabic Dialect Annotation Services
We specialize exclusively in Arabic language data annotation — covering major dialect families — for organizations building the next generation of Arabic-language AI systems.
NLP Annotation
Named entity recognition, sentiment analysis, intent classification, and text categorization across Levantine, Gulf, Egyptian, and Maghrebi dialects.
ASR Data Annotation
Speech transcription, phonetic labeling, speaker diarization, and audio quality validation for Arabic automatic speech recognition training pipelines.
Conversational AI
Dialogue annotation, response ranking, RLHF data collection, and conversation flow labeling for Arabic-language chatbots and virtual assistants.
Quality Assurance
Multi-layer review with inter-annotator agreement measurement, senior reviewer sign-off, and structured QA reporting delivered with every project.
Dialect Coverage
Native-speaker annotators covering Levantine, Gulf (Khaleeji), Egyptian, Moroccan/Maghrebi dialects, and Modern Standard Arabic (MSA).
Enterprise Compliance
US-incorporated, domestically owned. No content moderation work. Structured data handling with privacy-first practices that meet enterprise procurement requirements.
Pricing is project-specific. We work with clients to scope engagements based on volume, dialect requirements, and QA depth. Contact us to discuss your project needs.
Built for Enterprise Trust
Karama Data is a US-incorporated LLC with domestic ownership and a leadership team with deep expertise in AI, enterprise technology, and regional operations.
Our Structure
We are a US LLC with US-based board leadership and domestic ownership — a structure that meets enterprise compliance requirements and instills client confidence. Our operational presence is in the region, giving us authentic access to the linguistic talent our clients need.
Worker Ownership Model
Our annotators participate in a profits interest units model — a structural investment in quality, not a charitable gesture. Worker-owners have a direct stake in project outcomes, which produces measurably lower error rates and lower churn than transactional annotation models. This is the operational differentiator that benefits our clients.
GCV Partnership
We operate in partnership with Gaza Children Village (GCV), providing operational infrastructure and community ties that allow us to build and retain a stable, highly-qualified annotator workforce.
Leadership
CEO and board member, providing executive leadership and enterprise AI industry expertise.
Board member with ties to GCV partnership, ensuring strategic alignment between the company mission and operational delivery.
Board member contributing strategic oversight and organizational governance.
In-region operations advisor ensuring on-the-ground operational credibility, annotator welfare, and delivery quality.
The Quality Starts With the Annotators
Our annotator workforce is our primary quality asset. We invest in their training, their ownership stake, and their stability — because high-quality annotations require a workforce that is both skilled and retained.
Why Workforce Investment Produces Better Data
Transactional gig-annotation models produce inconsistent quality as annotators churn and training investment is lost. Our workforce participates in profits interest units — a structural mechanism that creates long-term retention and shared accountability for the quality of every dataset we deliver.
This isn't a social mission claim. It's a measurable operational difference: higher inter-annotator agreement, better training data, and lower rework costs for our clients.
Worker privacy is a priority. We do not publish individual annotator names, photos, or location information.
Start a Conversation
Tell us about your project. We'll follow up to discuss scope, dialect requirements, QA standards, and how we can fit into your annotation pipeline.