Human insight for
reliable AI
Build and evaluate AI with the right human expertise
DataSea helps AI teams create datasets, evaluate model behavior, and improve domain-specific performance through managed networks of trained contributors and qualified practitioners. We sit between mass annotation and expensive expert networks, matching each task to the level of human judgment it actually needs.
Mission
To make qualified human judgment accessible to AI teams by connecting them with trained contributors, working practitioners, and domain specialists suited to each task.
Vision
To be the flexible middle layer between mass annotation and scarce elite expertise, so AI builders can get reliable human input without building their own contributor network.
Our Values
Quality & Reliability
We use multi-stage review so datasets and evaluations hold up under real use, not just first inspection.
Collaboration
We work closely with partners to align on task design, review criteria, and scope before work begins.
Practical Innovation
We adapt workflows and tooling to each project instead of forcing every task through the same process.
Responsible Execution
We are clear about who performs each task, how it is reviewed, and what a deliverable does and does not cover.
Right-Sized Delivery
We scale contributor teams and review depth to match the project, from a focused pilot to an ongoing workflow.
Human judgment, matched to the task
From structured labeling to practitioner-led evaluation, we match each workflow with contributors suited to its complexity.
Data Creation & Annotation
Text, image, audio, video, and document annotation, with contributor profiles matched to task complexity.
Practitioner-Led Tasks
Realistic tasks, reference answers, and rubrics created by people who have done the underlying work.
Model Evaluation & Feedback
Rubric-based grading, preference ranking, and failure analysis to tell you whether an output is actually usable.
Managed Data Operations
We handle contributor sourcing, qualification, and workflow management so your team doesn't have to.
How we design every engagement
Instead of leading with scale, we lead with fit: the right contributors, the right review, and a project size that matches where you actually are.
Right-sized teams
Projects can start as a focused pilot and scale only once results justify it.
Contributor-role matching
Generalists, practitioners, and specialists are assigned based on task complexity.
Multi-stage quality review
Work moves through calibration, adjudication, and reviewer escalation before delivery.
Flexible project design
Engagements are scoped around the work itself, not a fixed minimum contract size.