Why teams love Refuel's LLM-powered labeling

Simply instruct Refuel on the datasets you need, and let LLMs do the work of creating and labeling data. Absence of data shouldn't get in the way of building models or applications.

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Instant

Annotate your data on-demand. Easily scale to millions of data points per day by scaling compute.

Superhuman quality

Never sacrifice data quality for quantity. Get 100x the data at 99%+ agreement with human labelers.

Empower your experts

Copilot for domain experts - teach LLMs to think like you while they label your data.

Copilot for annotation

Instant feedback loops

Provide labeling instructions and get labels as fast as an API call – no more waiting weeks for the next batch of data. 

Every use case imaginable

Classification, information extraction from docs, entity matching or summarization, Refuel has templates for every need.

A "before" diagram showing that it could take 4 weeks to label 10,000 examples.An "after" diagram showing that LLMs can label more than 100,000 examples in 30 minutes.
A "before" diagram showing that it could take 4 weeks to label 10,000 examples.An "after" diagram showing that LLMs can label more than 100,000 examples in 30 minutes.
A diagram showing the ability to set accuracy thresholds and thus shift automation rates, changing the balance of labeling performed by LLMs versus humans.

Label with confidence

Powerful LLMs

Use state-of-the-art models like GPT-4 to label your data, and easily benchmark any set of LLMs for your task.

Balance automation with accuracy

Refuel minimizes hallucination and computes confidence for LLM outputs, so you can balance precision and recall for your use case. 

Built for enterprise

Security and privacy first

Try our open-source library in your own environment, or talk to us about deployment options that align with your needs

Scale to infinite volume

Label your initial training data, or your entire production traffic with LLMs that scale with your needs.

from autolabel import LabelingAgent

# create a labeling agent with your instructions and an LLM
agent = LabelingAgent('instructions.json', 'openai-gpt-4')

# start labeling millions of data points
agent.run(
    'dataset.csv',
    num_items=1000000
)
0%|
| 0/1000000   [0:00<12:42,  1.09s/it]

Ready to get started?

Start labeling data in minutes.