AI Clearing vs Catching Unicorns with GLTR

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.

GLTR is a forensic text analysis demo that highlights how predictable each word in a passage looks to a language model and colors the text so you can spot machine-written prose at a glance. Paste a sample into the live demo, and GLTR ranks every next-word prediction from GPT-2 117M, painting likely picks green or yellow and surprising human choices purple.

Unlike black-box AI detectors that return a single score, GLTR shows the forensic footprint word by word, plus histograms for top-k counts, probability ratios, and prediction entropy across the passage. That makes it useful for teaching how generative models stick to safe, high-probability wording while human writers reach for rarer terms that still fit the topic.

Researchers, journalists, and educators use GLTR to compare sample texts, explore model behavior, and sanity-check suspicious copy. The project is free, open source, and backed by an ACL 2019 demo paper from MIT-IBM Watson AI Lab and Harvard NLP, though the maintainers note it was built for GPT-2 era text and may struggle with newer models like ChatGPT.