Text Datasets
Classification, entity extraction, intent, sentiment, relevance and document annotation with clear taxonomies and labeling guidelines.
Training Data & Annotation Pipelines
Production-Grade Training Data Pipelines in Abu Dhabi
Build repeatable, measurable systems that turn raw text, image, video, audio and multimodal data into quality-controlled training datasets for production AI.
Trusted by ambitious AI teams
DittoCLIKA.ZYCUS
Annotation Is an Engineering System
Useful training data depends on more than task volume. Taxonomies, guidelines, calibration, reviewer workflows and adjudication determine whether annotations are consistent enough to support model development.
XIVTech builds the operating layer around annotation so teams can version the work, measure quality and improve the dataset as requirements change.
Annotation Capabilities
Image & Video Datasets
Classification, bounding boxes, object detection, segmentation, keypoints and tracking for visual AI workflows.
Audio Datasets
Transcription, speaker identification, classification and conversational annotation for speech and audio systems.
Multimodal Datasets
Cross-modal image-text, audio-text and video-text annotation for systems that reason across multiple data types.
What We Build
Taxonomy and Guidelines
Define label structures, edge-case rules and examples that make annotation tasks understandable and repeatable.
Reviewer Workflows
Design task assignment, review, escalation and adjudication paths around the complexity and risk of the dataset.
Quality Measurement
Use calibration, sampling, multi-pass review and agreement measures to make quality visible throughout delivery.
Versioned Dataset Preparation
Prepare structured datasets with traceable changes, documented decisions and outputs that can be evaluated and improved over time.
From Raw Data to Reliable Dataset
We establish the data and quality system before scaling production work.
- 01
Discover
Define data types, taxonomy, task complexity, security requirements and quality thresholds.
- 02
Calibrate
Run representative tasks, refine guidelines and align reviewers on ambiguous cases.
- 03
Annotate
Operate structured annotation workflows with appropriate human review and escalation.
- 04
Adjudicate
Resolve disagreements and update the guidance where recurring edge cases appear.
- 05
Measure
Report quality findings and feed improvements into the next dataset version.
Tooling That Fits the Workflow
Annotation Infrastructure
Label Studio, Argilla and custom annotation interfaces selected for the data type and review model.
Data Engineering
Python, Apache Airflow, dbt and Apache Spark for preparation, orchestration and repeatable processing.
Quality and Auditability
Versioned guidelines, reviewer calibration, sampling, audits and controlled access to sensitive data.
AI-Assisted Processing
Use automation where it scales while keeping domain specialists focused on ambiguous and high-value cases.