Scientific Research AI Annotation

Centaur.ai can empower science and research teams to build, validate, and monitor AI models with trusted human evaluation, especially when stakes are high and context is complex.

Use cases

Reinventing science and research with AI-enabled solutions.

Scientific summarization & search QA

AI models are accelerating literature review and discovery workflows.

• Centaur reviewers evaluate the accuracy, relevance, and structure of scientific output.
• Abstract and citation scoring

Scientific method and claim alignment checks

• Calibrate annotations for seasonal variation and edge cases

Biosignal & behavior annotation

Research teams use Centaur to label time-series data, video, and imaging from biological experiments or behavioral studies.

• Motion tracking, EEG/EMG pattern tagging
• Lab observation and response labeling

Human judgment in hypothesis evaluation

In ML-guided research, reviewers help validate outputs that suggest hypotheses, patterns, or experiment plans, catching ethical or logical issues early.

• Evaluation of scientific plausibility
• Labeling unexpected model behavior

Trusted by AI leaders across all industries

From start-up to scale up, we support your data annotation needs throughout the AI lifecycle.

“We were able to use the disagreement between labelers to convert categorical labels into a continuous metric. That’s ultimately the training data we used and it was only possible because of the 20+ opinions we could gather from Centaur.ai on each piece of our data”.
Dr. Jason Shumake, Dir. of Data Science

“Working with Centaur.ai to annotate data is better in every way than our prior system. The annotations are more accurate, more affordable and the system is easier for our team to manage.