Clixoo is an open‑access platform that hosts interactive AI‑driven demos and reproducible machine‑learning workflows for climate‑relevant energy and industrial applications, including battery‑management optimization, solar‑thermal control, wind‑turbine design, and grid‑balancing. It provides end‑to‑end pipelines, source code, and technical documentation so researchers and engineers can evaluate, adapt, and integrate AI solutions without building infrastructure from scratch.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations and researchers seeking to accelerate climate mitigation lack a centralized, easily accessible platform that demonstrates how modern AI techniques can be applied to diverse energy and industrial domains. Existing tools are often siloed, proprietary, or require extensive expertise, limiting rapid experimentation and broader adoption of AI-driven decarbonization strategies.
Solution
Clixoo is an open‑access experimental platform that aggregates and showcases AI models, data pipelines, and prototype applications targeting climate‑relevant challenges. It provides interactive demos of AI‑enhanced value‑chain analysis, battery‑management optimization, solar‑thermal control, wind‑turbine design, perovskite manufacturing, geothermal network modeling, and grid‑balancing. Each demo is built on reproducible machine‑learning workflows—ranging from supervised classifiers (e.g., SVM for contaminant detection) to generative deep‑learning for aerodynamic shape optimization—and is accompanied by technical documentation and source code. By curating real‑world case studies and a continuously updated news feed, Clixoo enables engineers, researchers, and policy analysts to evaluate, adapt, and extend AI solutions without building infrastructure from scratch.
Features
- Interactive web‑based demos that run end‑to‑end AI pipelines (data ingestion, model training, inference) for sectors such as solar, wind, geothermal, and battery systems.
- Value‑chain analysis tool for the cosmetics industry using graph‑based ML to map material flows and identify emission hotspots.
- Generative design workflow for wind‑turbine blades employing convolutional neural networks to explore high‑efficiency airfoil geometries.
- LIBS‑based contaminant detection on turbine blades with support vector machine classification achieving >95 % accuracy.
- Perovskite thin‑film crystallization optimizer that trains a neural network on video‑derived photoluminescence data to predict film quality metrics.
- Satellite‑imagery powered residential energy‑assessment engine (SolarScan) that combines convolutional feature extraction with regression models to estimate solar gain and cost savings.
- Geothermal district‑heating simulation integrated with reinforcement‑learning control policies for automated network regulation.
- Real‑time grid‑balancing pilot using recurrent neural networks to forecast load and dispatch 1 MW of flexible demand resources.
- Curated AI‑for‑climate news aggregator with metadata tagging for rapid literature scouting.
- Open‑source code repository and API endpoints for model inference, enabling downstream integration into corporate analytics stacks.