The best Kognic alternative is Encord

Looking to transform 3D, video and sensor data into high-quality, labeled datasets? Encord is the top-rated Kognic competitor for 300+ AI teams.

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Why Physical AI teams choose Encord vs Kognic

Evaluating Kognic for 3D data annotation? Alternatives like Encord give ADAS teams an end-to-end data layer for curation, active learning, and full pipeline visibility – alongside the 3D annotation tools you’d get from Kognic.

Filtering LiDAR data

Encord gives you scalability

With Encord, you also get white glove configurable editors that scale with your needs, automated pipelines, custom tooling integration, and VLA and reasoning expertise along with our network of expert ADAS annotators.

Multimodal asset

Kognic has limited modalities

While Kognic matches Encord in 3D and sensor fusion data, Kognic has limited support for 2D modalities. As your roadmap expands to VLAs, embodied AI, time series, audio and more, Encord is the multimodal choice.

Which is better: Kognic or Encord?

Which is better: Kognic or Encord?
Encord logoKognic
3D + sensor fusion

Auto-labeling on cuboids
Sequences up to 1000+ frames

3D cuboid creation from 2D camera view

Auto-labeling on cuboids
Limited sequence frame cap

Data lifecycle

Data curation and similarity search
Active learning and model evaluation

Annotation-only

Analytics & feedback

Continuous improvement loops, per-annotator analytics and QA visibility

Custom additional tooling required

Modalities supported

Fully multimodal: 3D and sensor fusion, video, time series, image, audio, text

Limited 2D support

Configurable workflows

Custom HITL, confidence routing, QA review

Limited review workflows

Higher-quality data for ADAS models

Train models on better data, and reduce time spent on manual data annotation.

AI prelabels on self driving LiDAR scene

Product tour

Try our interactive platform experience to see what data workflows look like in Encord.

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MIGRATION GUIDE

Switching from Kognic?

Let your team focus on building better models, while we handle the migration.

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Step 1

Import existing datasets into Encord

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Step 2

Use AI-assisted pre-labeling to accelerate transition

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Step 3

Add structured human QA and review workflows

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Step 4

Activate evaluation and feedback loops for continuous improvement

Designed for reliable AI

Robotics & AV

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Frontier & Generative AI

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Get the data right

The leading AV & ADAS teams use Encord. Join them.