The 3LC platform

Four products.
One data-centric stack.

One platform for every step of the data-centric loop, from first integration to fleet-scale Physical AI.

The data-centric core

01 · Dashboard

Explore per-sample metrics, visualize embeddings, edit labels, retrain immediately.

Integration

Three lines of Python. No rewrite of your training loop, model, or dataloaders.

Data exploration

Per-sample, per-epoch metrics. Embedding views surface clusters, outliers, mislabels.

Action

Edit labels, remove noise, weight samples; retrain immediately. In the 3LC Hub (beta) or the Python SDK.

How it works
See and fix your data through the model’s eyes.
Lasso the confusion in embedding space, filter anything live, derive virtual columns, fix labels in batch, weight what matters, and commit it all as a sparse revision.
How it works
The whole loop, in one browser tab.
Import, train, run Insights, fix in the Dashboard, and re-train. Plugins bring the latest models with one click.

Orchestration · Beta

02 · Hub

The no-code layer that opens the data-centric loop to your whole team.

No-code workflow

Projects, experiments, datasets, training runs, lineage.

Connects to anything

Local drives, S3, Azure Blob, Google Cloud. Git-style data versioning.

Full lifecycle

Import, train, analyze, fix, retrain.

Hot-loaded plugins

Every capability is a plugin: one-click install, environments created automatically. Try the latest model without touching Python. Results land in the Dashboard.

Diagnostics

03 · Insights

Automatically surfaces the dataset issues degrading your model.

Auto-diagnosis

Finds the root causes of model errors. No manual digging.

What it finds

Missing annotations · label errors · edge cases · class imbalance.

Beyond data scientists

Diagnostics anyone on the team can act on.

How it works
Find what’s degrading your model.
Run Insights replays your trained model’s predictions against your labels, surfaces every data issue ranked by severity, and hands you a one-click Dashboard workflow to fix each one. Fix, retrain, compare: the health score climbs every round.
Step 1 of 6
How it works
Find the data that actually matters.
From your unlabeled pool, DriftCatcher picks out the rare samples that would genuinely move the model, so you label only those, with humans, AI, or both. Fold them into training, rebuild the Reference DB, repeat. Most projects reach equal or better accuracy on just 5–50% of the data.
Step 1 of 6

The production layer · Patent pending · Coming soon

04 · DriftCatcher

Trust, drift, and feedback on every prediction in production. Auto-curates data from the edge.

Per-prediction trust

Confidence scored on every inference call. Not aggregated, not sampled.

Drift detection

Catches distribution shift in the field; auto-curates the samples that matter for retraining.

Closed loop

Production → curation → retraining → deployment, on one artifact.

Works with the tools you already use

PyTorch TensorFlow Ultralytics YOLO HuggingFace SAM 3

What it does: breadth of support

3LC works across all computer-vision tasks.

The same per-sample diagnostics across every task type.

Classification in the 3LC Dashboard

Classification

  • Find misclassified samples
  • Watch per-sample metrics evolve across training
  • Edit labels; retrain instantly
Object detection in the 3LC Dashboard

Object Detection & Oriented Bounding Boxes

  • Add, resize, rotate, or relabel any box, axis-aligned or oriented
  • Filter per box by confidence or IoU
  • Apply thousands of prediction-based edits in one click
Pose estimation in the 3LC Dashboard

Pose Estimation

  • Edit keypoints, skeletons, and boxes as one pose annotation
  • Per-keypoint OKS metrics pinpoint where the model struggles
  • COCO and YOLO formats; Ultralytics and SuperGradients training
Semantic segmentation in the 3LC Dashboard

Semantic Segmentation

  • Edit class masks per pixel
  • Find class and object-size imbalances
  • Per-class IoU shows exactly where the model struggles
Instance segmentation in the 3LC Dashboard

Instance Segmentation

  • Edit per-instance masks with pixel precision
  • One click turns predictions into ground truth; SAM auto-generates masks
  • COCO and YOLO formats; scale fast with active labeling
LiDAR point cloud scene with labeled 3D bounding boxes around vehicles and pedestrians

LiDAR & 3D Point Clouds

  • 3D detection runs in the same per-sample workflow
  • Score and curate scenes by loss, confidence, coverage
  • Surface sparse, drifted, or mislabeled scans
Scene: PandaSet by Hesai & Scale AI · CC BY 4.0

Where it applies: industries

Built for industrial AI.
Trusted across industries.

The data quality problem looks different in every vertical. Here’s how it plays out where we work.

Inspection at the scale of an asset base

Energy

Robotic and drone inspection at fleet scale. Data quality is the operational constraint.

EquinoreSmart SystemsEnergy transition
Edge-deployable models for classified workflows

Aerospace & Defense

Less data, smaller models, sovereign by design. No cloud round-trips.

Classified A&D primeDefense-grade
Damage detection & ADAS data curation

Automotive

Multi-camera CV in every lighting condition and edge case. 3LC pinpoints the samples driving false positives.

WennADAS
CV in the most variable conditions on earth

Agriculture

Field robots, sprayers, crop classification. Managing natural variation is the difference between a trial and a product.

Saga RoboticsPrecision agri
From notebook to fleet

Robotics & Physical AI

Robots stream more data daily than classic CV saw in a year. 3LC finds the few thousand samples that actually improve the model.

RoboxiBramblesPhysical AI
Real-time analysis on every camera

Infrastructure & Smart Cities

CV across thousands of edge points. Drift and domain shift are constants, not incidents.

HelinInfrastructure CV

Our position in the landscape

Adjacent tools solve a piece.
3LC closes the loop.

An honest map of the space. Good tools exist in every adjacent category; the difference is the closed loop on a single artifact.

What others do
What 3LC uniquely does
Scoring tools: detect noise, no closed loop
◆Finds the noise and fixes it: label edits live during training
Weak supervision: no per-sample tracking
◆Per-sample, per-epoch metrics across the full training run
Annotation platforms: disconnected from training
◆Labels and training share one artifact, with version-locked lineage
Dataset viewers: outside the training cycle
◆Exploration wired into the loop: browse, filter, edit, retrain
Production monitoring: no link back to training
◆Closed-loop drift, trust and feedback, feeding the next retrain
All-in-one suites: separate tools, no loop
◆One artifact from training to production monitoring

When customers consolidate, and they do, 3LC wins. All of it running entirely inside your infrastructure.

Get started

Better data.
Better AI.
In production.

Book a demo →
See the platform →
3LC
Powered by  GDPR Cookie Compliance
Privacy Overview

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.