Use Cases

From training to managing model output quality in production, accelerate your data pipeline with Centaur.ai

Model Training

Give your model a competitive edge. Train your AI only with the highest quality data, labeled by multiple experts.

Expert  Feedback

Access real-time expert feedback to build the next generation of LLMs. Fuel generative AI with feedback from the experts who matter most.

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Evaluation

Test the performance of your model with datasets labeled by industry experts you can trust.

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Regulatory  Submission

Submit your model to regulatory authorities knowing your validation data was labeled to their requirements by experts with verifiable credentials.

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white paper

Are you prepared to use generative AI in your healthcare organization?

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Accelerate AI development

Improve data quality throughout the AI development lifecycle.

DESIGN

BUILT

TEST AND MONITOR

Accelerate healthcare AI development Centaur.ai

Design

Data Curation →

Understand what is in your dataset. Create classes to understand sampling and class representation. Easily organize your dataset to ensure efficient downstream labeling and label quality control.

Data Cleaning and Quality Control →

Find and remove low quality and irrelevant data before beginning the labeling process. Only spend time labeling the data that matters most for your model training.

Vector Database Augmentation and RAG →

Improve LLM outputs by enhancing prompts with context relevant to your use case and industry. Easily add organized and labeled datasets to customized vector databases.

Build

Model Training →

Train your AI only with the highest quality data, labeled by multiple experts.

Supervised fine-tuning →

Customize LLMs outputs by fine-tuning general purpose models with labeled datasets relevant to your use case, function or industry.

Expert feedback (RLEF) and Prompt generation →

Access a global network of 60,000+ experts to score and evaluate model outputs. Generate a wide range of real-world prompts so your model is prepared to answer appropriately in production.

Explainability →

Build explainability into your unsupervised model. Help users understand what influences model outputs, so they can trust and act on insights.

Test & Monitor

Evaluation →

Test model performance on site-specific or population-specific data. Ensure your model meets internal and customer performance standards.

Regulatory submission and validation →

Submit your model to regulatory authorities knowing your validation data was labeled by experts with verifiable credentials.

Model monitoring →

Ensure model safety and quality. Send outputs from in-production models to our expert crowd - in real-time or retrospectively - for review and evaluation.

Design

Data Curation →