01 / DATASETS
Off-the-shelf, ready on Kaggle
Real-world, geographically diverse imagery with the edge cases studio data misses. Explore annotated samples below, then browse the full set on Kaggle.
SELECT A DATASET
Explore all datasets on Kaggle ↗, Hugging Face ↗ and Roboflow ↗.
02 / SERVICES
End-to-end training-data services
From raw collection in the field to model-ready labels — or a fully trained model delivered to your team. One pipeline, human-verified at every stage.
Data Collection
On-demand image, video and audio capture from 100,000+ contributors across geographies, devices and conditions.
Data Cleaning
De-duplication, blur and exposure filtering, PII scrubbing and format normalization before a single label is drawn.
Bounding-Box Annotation
Tight, consistent 2D boxes for detection tasks, with multi-pass review and class-level QA metrics.
Segmentation-Mask Annotation
Pixel-accurate instance and semantic masks for scenes where boxes are not enough.
Data Curation
Class balancing, edge-case mining and dataset composition tuned to your model’s failure modes.
Text / OCR Annotation
Transcription and region labeling for signage, plates, documents and in-the-wild text.
AI & Data Consulting
Dataset strategy, annotation specs, volumes and evaluation — senior guidance before you spend on collection or GPUs.
Model Training & Delivery
We train detection, classification or segmentation models on your dataset and hand over a ready-to-deploy model — data to model, one vendor.
03 / AGENTIC & MULTIMODAL
Data for agents and multimodal models
As models improve, the differentiator is no longer the model — it's the data. We build the visual data that VLMs, LMMs and computer-use agents need to reason about the real world, and to survive the edge cases that break them after deployment.
Visual QA
Image–question–answer triples for training and benchmarking visual reasoning in multimodal models.
Image captioning
Human-written, richly detailed scene descriptions for VLM grounding — see a real sample in the explorer below.
GUI & screenshot data
Real mobile UI screenshots and icon grounding — training data for computer-use and on-device agents.
Multimodal evaluation
Held-out, real-world benchmark sets that measure how your LMM actually performs outside the lab.
Reasoning datasets
Multi-step visual reasoning data — gauges, readings, signage and scenes an agent must interpret, not just detect.
LMM training data
Instruction-style multimodal data, collected and annotated to your ontology and deployment spec.
Hard-to-source classes are where models fail — luggage, construction vehicles, rare vehicles, mobile screenshots, cracked screens, meter readings. We field them on demand; web scraping never covers them.
04 / PIPELINE
How it works
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01
Collect
Contributors capture real-world data to your spec — locations, lighting, devices, edge cases.
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02
Clean
Automated + human filtering removes duplicates, junk and privacy-sensitive frames.
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03
Annotate
Trained annotators label with your ontology; multi-pass QA keeps accuracy high.
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04
Deliver
Model-ready exports in COCO, YOLO, Pascal VOC or your custom format.
05 / SOLUTIONS
Solutions by industry
Autonomous Mobility
Unstructured traffic, signage and road actors from streets your simulator can’t imagine.
Retail & E-commerce
Shelf, product and packaging imagery for detection, planogram and visual search models.
Safety & Surveillance
Fire, smoke, intrusion and PPE data for real-time monitoring systems.
Agritech
Crop, pest and field imagery across seasons, soils and lighting conditions.
Smart Cities
Waste, infrastructure and pedestrian data for urban-scale vision deployments.
Robotics
Cluttered indoor scenes, objects and surfaces for grasping and navigation.