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Shape the future with Omara

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Generalist Video Labeller

2 month - Contract

$10- $20/hr

Remote

Proficient in English

Generalist Video Labeller

2 month - Contract

$10- $20/hr

Remote

Proficient in English

Generalist Video Labeller

2 month - Contract

$10- $20/hr

Remote

Proficient in English

Generalist Image Labeller

2 month - Contract

$10- $20/hr

Remote

Proficient in English

Case study

How We Organized and Categorized a 30-Year-Old Welfare Fund by the Government of West Bengal

The Advocates Welfare Fund (AWF) is a trust providing financial and social security to advocates in West Bengal, offering retirement benefits and medical relief to over 10,000 members. Omara digitizes records, streamlines claims, and enables real-time access for advocates.

Visit the official AWF website →

4M+ pages digitised

120+ annotations

20+ labellers

< 60 days

I appreciate that the platform values labelers, providing quality scores and feedback, making me feel my work significantly impacts real-world AI.

Liora Tan

Liora Tan, Mathematics Professor

Pay Structures at Omara

DATA LABELLING

Pay Structures at Omara

Insurance is no longer just about claims and premiums. It's about forecasting risk, understanding policyholder behavior, and personalizing products in real time. But to unlo...

Labeling Data With Keypoints & Skeletons

DATA LABELLING

Labeling Data With Keypoints & Skeletons

Learn how keypoint skeletons and keypoint annotation can enhance your training. Explore tools and best practices for skeleton-based labeling techniques...

Enabling Insurance Intelligence Through Labelled Data

AI & ML

Enabling Insurance Intelligence Through Labelled Data

Insurance is no longer just about claims and premiums. It's about forecasting risk, understan...

A Friendly Guide to Labellmg

AI & ML

A Friendly Guide to Labellmg

Insurance is no longer just about claims and premiums. It's about forecasting risk, understanding policyholder behavior, and personalizing products in real time. But to unlock that future, Insurers must first deal with their past: decades of paperwork.Claims forms, hospital invoices, agent notes—these sit in scattered silos, often scanned or handwritten. Extracting meaningful insights requires a foundational shift in how data is treated.