Use Cases

Powering next-gen AI applications for startups and enterprises

12B+

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Months saved per project

99%+

Accuracy
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Data for ML Teams

Leading ML teams use Refuel to label data on-demand – from creating initial training datasets to ongoing performance monitoring – saving 3 months per project, reducing labeling costs by 90% and improving ML model performance by up to 50%.

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Entity Resolution

We partner with teams solving challenging entity matching and linking problems – whether it is business entities, consumer data or digital records – used in KYB/KYC, fraud detection and data deduplication.

A diagram of a user connected via lines to several banks, which are in turn connected to strings of transaction data

Transaction Categorization

Fintechs, data providers and banks use Refuel to categorize transactions at scale, perform attribute and merchant enrichment and drive better outcomes for customers with better data.

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Information Extraction in Insurance

Underwriting and operations teams use Refuel to classify documents and extract information from a wide variety of policy, loss run and benefits documents, thereby reducing manual effort and costs by up to 90% and improving data quality.

A screenshot of a social media post critiquing a restaurant, with buttons below it to label the post as 'Abuse' or 'Not Abuse' with a mouse cursor clicking 'Not Abuse'

Content Moderation

Social media companies, marketplaces and gaming platforms work with Refuel to detect hate speech, toxic content and misinformation and improve safety and user experience – Refuel is 80% cheaper than just manual review, and enables a 5x increase in throughput.

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