AI Training Data
Creation
Purpose-built, high-quality datasets that give your AI models the foundation they need to perform in the real world.
What We Offer
We design and build training datasets from the ground up, handling data sourcing, collection, annotation, curation, and quality validation. Our datasets are built to the exact specifications your AI model requires.
Dataset Design and Scoping
Define dataset requirements, class taxonomy, data distribution, and collection strategy before work begins.
Data Collection
Collect image, video, text, and audio data through web scraping, photography, recording, and crowdsourcing.
Synthetic Data Generation
Generate synthetic training data using 3D rendering, GANs, and data augmentation to fill dataset gaps.
Annotation and Labeling
End-to-end annotation across all modalities with multi-stage quality review built in.
Dataset Curation
Deduplication, outlier removal, class balancing, and train/val/test splitting to optimize model training.
Benchmark Datasets
Evaluation and benchmark datasets for model testing, red-teaming, and performance comparison.
Datasets We Build
Computer Vision Datasets
Object detection, segmentation, pose estimation, OCR, and scene understanding datasets at any scale.
NLP Datasets
Text classification, NER, sentiment, intent, Q&A, summarization, and translation datasets.
LLM Training Data
Instruction datasets, preference pairs, RLHF feedback data, and domain-specific fine-tuning corpora.
Speech and Audio Datasets
ASR transcription, speaker diarization, emotion recognition, and sound event datasets.
Multimodal Datasets
Image-text pairs, video-caption datasets, audio-visual datasets for multimodal AI training.
Evaluation and Test Sets
Curated evaluation sets for model benchmarking, adversarial testing, and performance tracking.
Supported Formats
Ready to Build Your Training Dataset?
Tell us what your AI model needs and we'll design a dataset plan that fits your timeline and budget.
Datasets That
Drive Model Performance
The quality of your training data determines the ceiling of your model's performance. We design datasets from the model's perspective: what classes, what balance, what edge cases, and what evaluation criteria will actually improve real-world performance. Every dataset includes curation, deduplication, class balancing, and documented provenance so your models train on data you can trust.
Model-First Design
We design datasets around what your model needs to learn, not just what's easy to collect.
Curated Quality
Deduplication, outlier removal, and class balancing ensure your model trains on clean, representative data.
Documented Provenance
Every dataset includes collection methodology, license information, and quality metrics for full transparency.