Eliminate Blind Spots in Your Deep-Learning Models
Deep-learning debugging & explainability platform
Built for enterprise teams developing their own neural networks.
Robotics
Autonomous vehicles
semiconductors
agritech
healthcare
defense
From “why it failed” to “fixed and tracked”, in one platform
Detection
Model behaviour analysis
Model behaviour analysis
Detect failure modes, edge cases, and domain gaps; reveal root cause.
Optimization
Dataset curation & optimisation
Dataset curation & optimisation
Labelling prioritisation, redundancy pruning, and synthetic data guidance to close gaps efficiently.
Optimization
Model optimisation
Model optimisation
Guidance for loss & hyper‑parameters, targeted retraining subsets, and architecture recommendations to avoid blind tuning.
monitoring
Production monitoring
Production monitoring
Detect drift and regressions in production and apply the fix right away.
Process_vision_01
Layer test
01
See why your model fails, no black boxes
02
Fix performance with the right data, not more data
03
Tune what matters, skip the trial-and-error
04
Monitor live with instant alerts and apply the fix right away
Outcomes you can measure
Debug in minutes,
not days
not days
Up to 60% reduction in labelling effort
Edge-case coverage to prevent costly
incidents
incidents
Real-time drift alerts to keep roadmaps
and SLAs on track
and SLAs on track
Results in the real world
We needed more insight into how the model sees our data and makes decisions. Tensorleap reveals insights we couldn’t get from other tools. It’s faster and more meaningful. Tensorleap helped us prioritize what to work on next and improve our process.
Tensorleap helped Hexagon achieve
40%
dataset reduction
without sacrificing accuracy, faster debugging, and clearer decisions on what to improve next
Works with your stack.
Enterprise-ready
Deploy on your terms
Your cloud or on-premise.
Secure access
SSO, RBAC, audit logs.
Fits your workflows
W&B/MLflow • S3/GCS/Azure Blob • PyTorch/TF
Deep-learning debugging & explainability - fixes, not noise
Root-cause to targeted fix - not just charts.
Identifies what’s breaking and recommends the most effective data or model change.
Everything happens in one closed loop.
Diagnose, apply the fix, re-test, then watch it live in one workspace.
Model-agnostic.
Works with any deep-learning model; standardises workflows across teams.
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Plug-in, not rip-and-replace.
Fits your existing workflows and tools.
