We create custom training datasets and handle data preparation for computer vision and AI models. We specialize in the data work most teams want to skip, helping clients avoid costly mistakes by ensuring their training data is properly prepared before they invest in model development.
Our Methodology
AI-Generated Images
We utilize generative AI models to create synthetic images that augment our dataset. This approach allows us to generate variations of specific scenarios, control for certain variables, and address data scarcity in underrepresented categories. Generated images help balance class distributions and introduce controlled diversity while maintaining consistency in style and composition.
Web Scraping
We systematically collect images from online sources using automated scraping tools. This method enables large-scale data acquisition from diverse internet sources, capturing real-world variation and authenticity. Our scraping process includes filtering mechanisms to ensure image quality, relevance, and compliance with usage rights and ethical guidelines.
Manual Image Curation
We supplement automated collection with hand-selected images to ensure quality and fill specific gaps in the dataset. This manual approach allows for careful selection of edge cases, rare scenarios, and high-quality examples that automated methods might miss. Human curation also helps verify that images meet specific criteria for clarity, relevance, and representativeness.
Image Annotation
All collected images undergo a systematic annotation process where we label, categorize, and add metadata to each image. This step is crucial for supervised learning tasks and includes defining bounding boxes, semantic labels, or other task-specific annotations. We implement quality control measures and potentially use multiple annotators to ensure consistency and accuracy.
Our Services
Discover our range of services designed to meet your data needs. From concept to implementation, we offer comprehensive solutions tailored to optimize your processes and elevate your business.
Consulting
Our data consulting helps you avoid the costly mistakes we've seen teams make with training datasets. We audit your current data pipeline and provide actionable recommendations to improve quality, diversity, and annotation processes.
Data Analysis
We analyze your existing datasets to catch the problems that only surface during production: label inconsistencies, coverage gaps, and edge cases your current data misses. You get actionable insights to strengthen your training data before investing in model development.
Data Gathering
We design and execute data collection strategies that capture the diversity your models actually need in production. From identifying edge cases to sourcing representative samples, we build datasets that reflect real-world conditions rather than lab assumptions.
Data Annotation
We annotate data the way engineers want it done, with rigorous quality controls, consistent labeling standards, and the technical context to catch issues that break models in production. Every dataset comes with transparency into our process and quality metrics.
Our Story
Most engineers hate dealing with datasets. We actually love it.
After a decade in computer vision engineering, we've observed a consistent pattern across the industry. Engineering teams invest heavily in architectures and algorithms, then quickly delegate data gathering and annotation elsewhere. They prioritize what they consider the "interesting" technical challenges.
That's a costly mistake.
Your data doesn't just feed your model—it fundamentally determines what your model can achieve. The most sophisticated architecture cannot overcome inadequate training data. It's like fueling a race car with low-grade gasoline and expecting peak performance.
We've witnessed production-level models fail not due to algorithmic limitations, but because data preparation was treated as a checkbox rather than the foundation it represents. The filtering lacked rigor. The annotations overlooked edge cases. The diversity fell short.
So we decided to do what most engineers avoid: make data preparation our specialty.
While others optimize neural networks, we optimize the data that powers them. Gathering, preparing, filtering, and annotating data isn't merely something we excel at—it's work we genuinely find fulfilling. When the data is right, everything else follows naturally.
We handle the component of AI that most teams prefer to skip, enabling your algorithms to reach their actual potential.
Get in touch
We’re happy to hear from you. Contact us today to learn more about our business and how you can benefit from working with us.