AI Operations That Keep Your Models Running
We build dedicated offshore teams that handle the data work behind your AI products. From annotation to quality review, your pipeline stays moving without overloading your internal team.
The bottlenecks holding your AI pipeline back
Annotation backlogs slowing releases
Your model is ready but the data is not. Internal teams cannot keep up with labelling volume.
Inconsistent data quality across batches
Without dedicated reviewers, errors compound and model performance suffers downstream.
No bandwidth for operational AI tasks
Your engineers should be building, not managing repetitive data workflows.
The operational layer your AI team needs
Built around your pipeline, your tools, and your quality standards.
Data Annotation
Image, text, audio, and video labelling across all major formats
AI Model QA
Output review, error flagging, and feedback loops for continuous improvement
Dataset Management
Collection, cleaning, versioning, and storage of training datasets
Content Moderation
Real time and batch moderation across text, image, and video content
Performance Monitoring
Tracking model outputs against benchmarks and flagging anomalies
Prompt Operations
Prompt testing, refinement, and documentation for LLM workflows
Specialists in the operational side of AI
Every Datasphere AI Operations team is built from people who understand data quality, not just data volume.
Operations Lead
Manages pipeline delivery, team performance, and client reporting
Data Annotator
Handles high volume labelling across text, image, and multimodal datasets
QA Reviewer
Reviews annotated outputs, flags inconsistencies, and maintains quality standards
Prompt Engineer
Tests, refines, and documents prompts across LLM workflows and use cases
AI operations built for your sector
Our AI Operations teams understand the data standards and compliance requirements of your industry. Not just generic pipelines.
Ready to build your AI Operations team?
Tell us how your pipeline works and we will build a team around it.