AI Clearing vs Liner.ai

AI Clearing connects design and BIM data, schedules, budgets, drone imagery, mobile inputs, and ground surveys into one construction intelligence system for large-scale infrastructure and renewable energy projects. Contractors and asset owners use its CORE platform to see what is actually happening on site instead of relying on scattered spreadsheets and field notes.

A proprietary computer vision engine trained on millions of construction objects compares as-built conditions against design plans. The platform delivers progress and quality reports within 6 to 24 hours of data capture and includes Clara, a conversational assistant that answers project questions from live field data.

Where most construction software tracks tasks in spreadsheets, AI Clearing is built around visual site evidence. It reconciles drone imagery, schedules, and BIM models automatically, which makes it better suited to utility-scale earthworks and renewables than generic project management tools that lack geospatial progress verification.

General contractors, EPC firms, and asset owners working on renewables, transmission, wind, highways, pipelines, railways, and civil earthworks use AI Clearing for progress tracking, quality control, billing verification, and portfolio oversight. Customers include PCL Solar, baywa-re, and Rosendin Renewables.

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.