Model-Ready Data for Physical AI
Datafy Lab creates hard-to-get edge-case datasets that help robotics, computer vision, autonomous systems, and industrial AI models perform better in the real world.
Real-world AI breaks in the long tail.
Most AI systems perform well in controlled demos but fail when the physical world becomes messy. Lighting changes. Objects are damaged. Surfaces reflect. Cameras blur. Environments become crowded. Robots face new angles, failed grasps, rare object states, and human behavior they have never seen before.
Your dataset is too clean, narrow, or repetitive.
Your model fails on rare but expensive edge cases.
Your annotations are inconsistent or not training-ready.
Your team lacks real-world field data.
Your synthetic data does not transfer cleanly to production.
Your AI system needs better data, not just more data.
We build the missing data your model needs next.
Datafy Lab operates as a full-stack data foundry for physical AI. We diagnose model failures, design data strategies, collect real-world data, create edge-case datasets, run human-in-the-loop annotation, filter and certify datasets, and continuously improve your data pipeline as your model evolves.
One closed-loop data pipeline.
From expert data operations to real-world field capture, Datafy Lab gives physical AI teams the data execution layer they need to move from demo performance to deployment reliability.
Desk-based, expert-led, AI-first data operations for model improvement.
Data Failure Audit
Identify missing edge cases, label noise, imbalance, duplication, weak coverage, and dataset risk.
02Multimodal Annotation
Human-in-the-loop annotation for images, video, text, speech, sensor data, robotics trajectories, and industrial datasets.
03Filtering & Certification
Clean, validate, score, and certify datasets for quality, privacy, rights, balance, and training readiness.
04Expert Evaluation Data
Create expert-reviewed evaluation sets, model failure taxonomies, preference data, reasoning traces, and benchmarks.
Datafy Lab can combine all three into one closed-loop data pipeline.
A pipeline that improves as your model fails.
Not just labeled data. Model-ready data.
Data Failure Audit
We inspect your current dataset and model failure patterns to identify the missing data your model needs next.
View service →Egocentric Video Data
We capture first-person human POV datasets for robotics, humanoids, embodied AI, imitation learning, and task understanding.
View service →Multimodal Annotation
We annotate images, videos, text, speech, sensor data, robot trajectories, and temporal events with quality-controlled workflows.
View service →Edge-Case Dataset Creation
We create targeted datasets for rare, difficult, high-value scenarios your model cannot afford to miss.
View service →Dataset Certification
We deliver transparent dataset quality reports covering source rights, privacy, annotation quality, coverage, balance, and limitations.
View service →Continuous Data Foundry
We operate an ongoing data improvement loop based on your model’s real-world failures.
View service →Every dataset should come with proof.
Datafy Lab delivers a Model-Ready Data Certificate with every qualified dataset. This gives your ML, robotics, and compliance teams visibility into whether the dataset is ready for training, evaluation, or deployment improvement.
See how certification worksIllustrative certificate · values shown are examples
Stop guessing what data to collect next.
A Data Failure Audit turns your model’s failures into a prioritized data roadmap — before you spend another dollar on random collection.
Built for AI that meets the physical world.
Warehouse Robotics
Picking, sorting, barcode handling, damaged packages, cluttered bins, conveyor workflows, failed grasps, and unusual object positions.
Humanoid Robotics
Egocentric task video, human demonstrations, tool usage, manipulation sequences, household-like tasks, and workplace task understanding.
Manufacturing Inspection
Rare defects, surface anomalies, lighting variation, packaging issues, assembly errors, and quality-control edge cases.
Autonomous Inspection
Drones, mobile robots, infrastructure inspection, utilities, construction sites, industrial facilities, and hazardous environments.
Retail Vision AI
Shelf monitoring, checkout vision, inventory detection, crowding, occlusion, object variation, and in-store visual intelligence.
Agriculture Robotics
Crop detection, weeds, disease, harvesting conditions, terrain complexity, weather variation, and fruit quality datasets.
From model failures to certified training data.
Diagnose
We review your model, dataset, failure cases, and deployment environment.
Design
We define the exact data your model needs next.
Capture
We collect real-world data, egocentric video, field footage, task demonstrations, or synthetic scenarios.
Annotate
We apply human-in-the-loop labeling, expert review, temporal tagging, object tracking, segmentation, and QA.
Certify
We validate quality, rights, privacy, balance, coverage, and training readiness.
Improve
We continue the loop based on model performance and new failures.
What teams say.
Datafy Lab helped us stop collecting random data and focus on the edge cases that were actually hurting model performance.
Their dataset certificate gave our ML team a clear view of quality, coverage, and risk before we added new data into training.
We needed real-world task data that normal datasets did not include. Datafy Lab gave us a structured way to capture, annotate, and validate it.