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About Us
We're the data annotation company built by engineers, for engineers.
Founded by a computer vision engineer with a decade of AI experience, we understand the frustration of working with datasets that look good on paper but fail in production. We've been on both sides of the pipeline, building the models and managing the data that feeds them.
Why We're Different
While other services focus on volume and speed, we focus on getting it right. We're not the cheapest option, we're the option that saves you from rebuilding datasets, retraining models, and explaining to stakeholders why production performance doesn't match your benchmarks.
Ready to work with data specialists who actually understand your technical requirements? Contact us to discuss your project.
Our Approach
Most annotation services treat data labeling as a commodity task. We treat it as the foundation of AI success. Our process is designed around the technical requirements that actually matter: consistency, precision, and real-world applicability.
1. Quality First:
Every dataset undergoes rigorous quality controls with detailed reporting, so you know exactly what you're training on.
2. Engineering Insight:
We don't just label data. We understand how labeling decisions impact model performance and catch issues before they reach your training pipeline.
3. Production Focus:
Our annotations are designed for models that need to work in the real world, not just in research papers.
What We Do
1. Data Consulting:
Technical guidance to help you avoid the costly dataset mistakes we've seen teams make repeatedly.
2. Dataset Analysis:
We identify gaps, biases, and quality issues in your existing data before they derail model performance.
3. Data Gathering:
Strategic collection that captures the diversity and edge cases your models will encounter in production.
4. Data Annotation:
Object detection, semantic segmentation, classification, point clouds. We handle the full spectrum with technical precision and consistency.
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