Overview

Refuel is a platform to clean, structure and transform your data at scale and superhuman quality by leveraging state-of-the-art large language models (LLMs).

Transforming your data with Refuel is a 4 step process:

1. Connect your data: Connect your data to the platform, via integrations into your cloud storage, or by uploading it directly to Refuel.
2. Define your usecase: Describe how you want to transform, enrich or label your data, or pick from a library of pre-built transformations.
3. Iterate with feedback: Improve LLM quality and reliability with a simple thumbs-up or down feedback loop, and model fine-tuning.
4. Deploy and Monitor: Deploy your application to process data at scale, with realtime or batch processing workloads using the model that best fits your latency, cost and security needs.

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Core use cases

Some of the core use cases for Refuel include:

• Data cleaning or normalization

• Categorization

• Structured data creation or extraction

• Enrichment

• Summarization

and more.