// field_data_capture
Train on the scenarios your model cannot afford to miss.
We create targeted real-world datasets for the rare, difficult, high-value scenarios that sit in the long tail — the ones that quietly break production performance.
// example_conditions
The conditions that break models.
01
Poor lighting
02
Motion blur
03
Occlusion
04
Damaged objects
05
Unusual object angles
06
Reflective surfaces
07
Crowded environments
08
Sensor noise
09
Rare defects
10
Failed grasps
11
Safety-critical near misses
12
Unusual floor / terrain
// method
Targeted, not random.
We start from your model’s failure clusters, define an edge-case taxonomy, then design capture or controlled-scenario shoots to produce exactly the distribution your model is missing.
edge_case_pipeline
01Failure-driven taxonomy
02Scenario design & capture plan
03Controlled + field capture
04Annotation & QA
05Coverage map & certificate
// faq
Common questions
A data foundry creates, collects, annotates, filters, certifies, and continuously improves model-ready datasets for physical-world AI.
We start from your model's real failure clusters and build an edge-case taxonomy, so capture is targeted at the distribution you're actually missing.
Yes — controlled-scenario shoots and on-site field capture across warehouses, factories, retail, farms, and facilities.
We validate synthetic data and measure synthetic-to-real transfer, and can blend synthetic with targeted real capture where it improves training usefulness.
Each qualified dataset ships with a Model-Ready Data Certificate covering rights, privacy, coverage, balance, annotation quality, and readiness.
Book a Data Failure Audit to surface your failure clusters, then we scope an edge-case dataset around them.