ABOUT KALANOS
We’re building Kalanos to help you understand what’s in your datasets, how they measure up, and where to focus next—so you can make informed decisions for physical AI.
Built on experience with complex data.
Our collective background spans data science, AI, and enterprise software, including developing products for connected devices and launching data products for Fortune 500 enterprises.
We bring this experience to robotics data evaluation, with an emphasis on clear evidence, useful context, and workflows that fit the way teams work.
From quality checks to informed decisions.
We believe a dataset’s value depends on its intended use. An unusual signal may warrant investigation without making an entire recording unusable. Likewise, a high technical score does not establish suitability for every task.
We make evaluation findings and their scope visible, helping teams decide where to focus review and what to investigate next.
Tools, shared assessments, and expert support.
Through Kalanos Hub, we help users discover evaluated public datasets, audit their own data, organize public and private dataset inventories, and share quality assessments.
Our open-source tooling supports local evaluation. We also work with teams on custom audits, evaluation requirements, and in-house setup.
A practical approach to progress.
We’re developing Kalanos around three principles:
Evidence behind every assessment. We show what was checked, what was observed, and what remains unknown.
Context behind every decision. We believe technical quality should be considered alongside the dataset’s purpose.
Value in every workflow. We build evaluation tools to help teams focus their effort and create a process they can repeat.