Our Unique Value Proposition


Improving ML models

Machine Learning models are constantly improved through training via high-quality datasets. The amount of data used to train a model, influences the subsequent improvement of the model’s prediction capabilities. We enable Machine Learning companies to improve their models faster, enabling them to be more competitive and lead their markets segments through: Diversity: access to geographical distributed multilingual Tasqers Reliability: ability to leverage multiple judgments for higher confidence Scale: simultaneous, online, unbiased workforce In short - by using a global, multilingual network of labelers, we solve the issue of fairness in AI.

Get to production faster

We accelerate the data annotation process and create higher quality datasets, faster. In turn, you can train your Machine Learning models, reach precision levels and get to production... faster than ever before.

Dynamic data enrichment

Adding additional layers of information to existing datasets is fast and seamless with our data enrichment solutions. Ongoing projects are incremental, supporting the growth of meta-data growth and details over time. Simply upload your data and enrich existing datasets with ease. No time wasted, no need to start from scratch.

Limitless reach:

A global, digital marketplace of Tasqers

What is a Tasqer?
A Tasqer is an online consumer with profiled, superior cognitive capabilities, incentivized to task or label at high quality.”
What’s so special about Tasqers and why are they different (and better) than BPOs?
Work When They Want to
More productive and accurate than overworked BPOs on minimum wage.
Work for content and rewards of personal value.
Instant & Scalable
No bottlenecks, no limit on access or reach.
Trained & Preselected
Cognitive ability-based training and task distribution for faster, higher quality results.
Automated consensus/quality controls for pinpoint precision.
Tasqers are global, multilingual, and multi- cultural.
Profiled for Excellence
Assigned work based on individual strengths, capabilities.

From Our Data Labeling Experts

Nov 13 | 2023

LLM Evaluation Methods: A Primer

Evaluating large language models (LLMs) is a critical step in understanding their capabilities and limitations, and finding a...
Crowd-Sourcing Human Feedback
Nov 07 | 2023

Crowd-Sourcing Human Feedback: The Open Movement and Unlocking the Potential of LLMs

When it comes to LLMs, a recent LinkedIn (and Twitter) post by Yann LeCun, VP & Chief AI Scientist at Meta, has sparked a...
Evaluating Generated Images
Sep 28 | 2023

Challenges and Solutions in Evaluating Generated Images

Introduction Behind the scenes of Generative AI, where algorithms conjure art, realism, and imagination from lines of code, a...