Platform
One platform to debug, fix, and monitor your deep-learning models
From root-cause analysis to targeted fix to production monitoring — all in one closed loop, without switching tools.
Four capabilities. One closed loop.
01
Model Behaviour Analysis
Detect failure modes, edge cases, and domain gaps across your entire dataset. Reveal the root cause behind every underperforming sample — not just a list of metrics.
02
Dataset Curation & Optimisation
Labelling prioritisation, redundancy pruning, and synthetic data guidance to close performance gaps efficiently. Reduce labelling effort by up to 60%.
03
Model Optimisation
Guidance for loss & hyper‑parameters, targeted retraining subsets, and architecture recommendations to avoid blind tuning. Debug in minutes, not days.
04
Production Monitoring
Detect drift and regressions in production with real-time alerts. Apply the fix directly from the platform and monitor the impact immediately.
See inside your models
Root-cause analysis — Heatmaps and population clustering reveal exactly which data caused failures.
Production monitoring — Track loss, accuracy, and drift in real-time with instant alerts.
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Model-agnostic — Works across vision, NLP, point cloud, and more.
Plug-in, not rip-and-replace — Connects to your existing MLOps stack out of the box.
Works with your stack
PyTorch
TensorFlow
Amazon S3
Google Cloud Storage
Azure Blob
Weights & Biases
MLflow
Kubernetes
NVIDIA
Labelbox
Amazon SageMaker