The dataset management platform helping you assemble efficient ground-truth data pipelines to create and optimize sensor-fusion datasets.
Our value and impact
If you are developing machines designed to navigate the physical world, we are focused on your needs.
A new home for the Pioneers of Embodied AI
The Kognic Platform provides enterprises a flexible toolset for sensor-fusion annotation; equips the ADAS/AD product owner with an efficient MLOps platform; and empowers enterprises to see minimized costs and optimized teams. Get a perfect balance between program cost and data quality while achieving safety-critical AI.
We help solve the hardest problems
Enterprises need to assemble efficient and flexible ground-truth data pipelines in order to create datasets, but challenges persist:
Data flow is too slow through pipelines.
As datasets grow, human feedback can be expensive.
Performance goals are difficult to define and reach.
Let Kognic show you how you can move forward - accurately and iteratively — towards higher model performance that aligns your model output with your intent.
End-to-end and top-to-bottom
The Kognic Platform is designed with your evolving dataset at its center and provides key capabilities – Explore, Shape and Explain – that quickly and accurately unlocks your data.
Supported by our industry-leading annotation engine, Kognic has critical tooling such as Multi-Sensor fusion, Data Refinement and Performance Analytics that have been proven in many ADAS / AD deployments.
How do we do it? Take a read here.
Latest from Kognic
The emergence of autonomous driving has marked a paradigm shift in the automotive industry. In this interview, we'll explore the current state of autonomous driving, its challenges, and the potential future in a podcast with our CEO, Daniel Langkilde.
Read more here.
The limitation of linear data pipelines is forcing many enterprises to rethink their processes, teams and methods of creating ground-truth. From two of our noted experts, read further to understand how iteration can be applied to your dataset.
Take a deeper dive here.