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.
Dashboard showing two different embedding layers across multiple epochs
Coming soon · 15s demo
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
Annotation errors aggregate metrics never surface — and they’re the samples driving your model wrong.
02
Domain shift breaks deployed models
New sensor, new lighting, new theatre. Models that pass evaluation drift silently the moment they leave the lab.
03
More data isn’t the fix
If 97% of your training set is low-value noise, adding more noise doesn’t help. The right data matters more than the most 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. Works with PyTorch, TensorFlow, Ultralytics YOLO, HuggingFace, and any custom model.
Every sample’s contribution to model error. Mislabels, edge cases, drift — surfaced and ranked, not buried in aggregate metrics.
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)
Works with the tools you already use
PyTorch
TensorFlow
Ultralytics YOLO
HuggingFace
Detectron2
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
The platform
Four products. One data-centric AI stack.
For data scientists, and the domain experts working alongside them.
Dashboard
01
The data-centric core
Debug, clean, and improve CV training data. Per-sample metrics, embedding visualisation, label editing — retrain immediately.
Hub
02
Orchestration
A no-code browser interface that opens the data-centric loop to your whole team. Import datasets, run training, track experiments.
Insights
03
Diagnostics
A decade of CV ML experience codified. Automatically surfaces mislabels, missing annotations, class imbalances and edge cases.
Driftcatcher
04
The production layer
Trust, drift, and feedback on every prediction at inference time. Patent in preparation.
Data sovereignty
Your data never leaves your infrastructure.
3LC reads data in place. No movement, no uploads, no third-party exposure. Designed for compliance from day one.
No upload
Reads from local drives, network storage, AWS S3, Azure Blob, Google Cloud. Data stays where it lives.
Air-gapped
Fully operational without internet connectivity. Deployed today in classified environments.
No lock-in
Your data is never locked in. Full API and SDK to export or import any dataset or metric.
Full lineage
Every dataset revision versioned. Audit trail across labelling, training, edits and retraining.
Production outcomes
Numbers from real deployments.
Headline outcome
Less training data — same accuracy. Major aerospace & defence client.
Equinor · over a year in production
Average accuracy improvement
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
Case study · Equinor
Pilot to production in under a year.
Robot dogs, satellites and microscopes. Four very different teams inside Equinor — and one data-quality layer underneath all of them.
4 teams
Adopted independently across the organisation
Cash-flow positive
Within the first year of deployment
Robots → microscopes
From CO₂ storage to subsea pipelines, satellites and microfossils
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