Renumics GmbH vs Liner.ai

Renumics GmbH is a Karlsruhe-based industrial AI company that builds systems for analyzing test, machine, and simulation data. They partner with automotive, aerospace, machinery, and pharma teams to move product development toward data-driven, end-to-end workflows.

Their product Onion is an agentic AI assistant for engineering data analysis. Engineers ask questions in natural language to query logging and fleet data, run plausibility checks, detect swapped channels, and generate interactive visualizations without waiting on custom reports from data specialists.

Renumics also maintains Spotlight, an open source toolkit for exploring unstructured datasets including audio, images, video, time-series, and 3D geometry. Beyond software, the company delivers workshops, data readiness checks, proof-of-concept builds, and full custom AI system deployments.

Liner.ai is a free desktop app for training and deploying machine learning models without writing code. You import your data, pick a project type, and Liner automatically selects an appropriate model and trains it on your machine. It is built for anyone who wants image, text, audio, or video classifiers, object detectors, or segmentation models without setting up a full ML stack.

The entire workflow runs locally. Training happens on your computer, so your data never leaves the device. Liner includes ready-made project templates for image classification, text classification, audio classification, video classification, object detection, image segmentation, and pose classification. Most models finish within minutes, and Liner optimizes training for standard CPUs so a GPU is not required.

When training is done, you export models to Keras, TensorFlow, TensorFlow Lite, TensorFlow.js, Core ML, or ONNX for use in web, mobile, or edge applications. Liner also hosts a free library of labeled public datasets covering tasks like dog vs cat classification, road sign detection, and yoga pose recognition. It fits educators, hobbyists, and product teams prototyping vision or classification features before bringing in a dedicated data science workflow.