The data-quality layer for AI training
Better data.
Better AI.
In production.
The data-centric AI platform for physical and industrial AI, from a data scientist’s first integration to every model running in the field.
Redundancy, label noise, and coverage gaps hold accuracy back. Curation fixes what volume can’t.
Works with the tools you already use
your infrastructure. Security & trust →
In production across energy, aerospace, agri and industrial AI
The constraint
Computer-vision AI isn’t bottlenecked by the model.
It’s bottlenecked by the data.
01
Mislabels and noise hide in plain sight
A few bad labels can steer your model wrong, and overall metrics never reveal them.
02
Domain shift breaks deployed models
A model that scores well in testing can quietly lose accuracy once it meets new sensors, lighting, and conditions in the field.
03
More data isn’t the fix
When most of your data adds little, collecting more of it doesn’t help. Better data beats more data.
What 3LC is
Per-sample, per-epoch visibility inside your existing training loop.
Three lines of Python. No rewrite of training loop, model, or dataloaders.
View every sample’s contribution to model error. Mislabels, edge cases, drift: surfaced and ranked.
Curate, edit, retrain. Browse and filter training data, fix labels, weight samples, and run again, immediately.
Your data never leaves. Air-gapped, on-premise, or cloud-native: runs wherever your data lives, with full audit lineage.
import tlc # 1 · wrap your dataset as a 3LC Table table = tlc.Table.from_image_folder("data/damage-detection") # 2 · describe the per-sample metrics to collect def metrics(batch, output): return {"loss": loss_fn(output, batch), "confidence": output.softmax(-1).max(-1).values} # 3 · one inference pass → per-sample metrics in a Run tlc.collect_metrics(table, metrics, model)
The pilot delivered immediate and measurable results — and is now integrated into multiple solutions across Equinor.
Kivanc Biber
Team Leader, Computer Vision, Industrial Automation & Autonomy, Equinor
Production outcomes
Numbers from real deployments.
Headline outcome
Less training data, same accuracy. Major aerospace & defense client.
Equinor · 33% time reduction
Saved per data scientist · per year
eSmart Systems · energy infrastructure
Faster training cycles
Wenn · damage detection
Fewer false positives in the field
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