Overview About Nilg Ai

For over 65 million wheelchair users and others with mobility challenges, city navigation means dignity, independence, and safety. Often, urban accessibility data is fragmented, forcing risky choices. Urban accessibility is a critical challenge. Outdated or missing data on sidewalks, ramps, and slopes leads to unsafe routes, impacting inclusive urban planning and limiting independence. In collaboration with the Câmara Municipal de Lisboa, NILG.AI developed SafeJourney, an AI-powered platform mapping Lisbon from a wheelchair user's perspective. Leveraging computer vision and geospatial analytics, SafeJourney analyzes Google Street View, OpenStreetMap, and OpenRouteService data. This innovative solution applies deep learning for accessibility mapping to evaluate:   Sidewalk conditions Crosswalk widths Ramp availability Street slopes The platform generates an Accessibility Index, empowering users to find wheelchair-friendly routes. Our commitment to open-source urban data (CC BY-ND 4.0) ensures transparency and adaptability for other cities seeking to enhance mobility inclusivity. SafeJourney delivers tangible benefits: Empowered Mobility: Users navigate with increased confidence and safety, promoting digital inclusion. Informed Urban Planning: City officials gain data-driven insights to address accessibility gaps, fostering smart city development. Scalable and Replicable: A blueprint for global urban accessibility solutions. Co-funded by Câmara Municipal de Lisboa and the European Regional Development Fund (UIA), this project showcases the transformative power of AI in urban planning for creating more accessible and equitable cities.

Vonovia, one of Europes largest real estate companies with over 500,000 properties, was facing long listing times and low performance from its email newsletter a valuable but dormant channel. The challenge: how to connect the right properties with the right people, without overwhelming users with irrelevant options. NILG.AI developed a personalized property recommendation engine using artificial intelligence, designed to reactivate Vonovias newsletter and deliver smarter, faster suggestions. The system leveraged:   Historical user clicks and visits Structured property data User behavior patterns Using a hybrid AI approach collaborative filtering, semantic analysis, and clustering the engine adapted in real time to recommend properties aligned with each users profile and browsing history. Results +18% increase in click-through rates (CTR) Faster rental turnaround with shorter listing durations Reactivation of a key marketing channel through intelligent automation. This project highlights how AI in real estate can enhance user engagement, reduce vacancy time, and increase marketing efficiency through data-driven property matching.

AI-Driven Combustion Optimization: The Steag Iqony Group Partnership Steag Iqony Group, a leading European energy and industrial service provider, teamed up with NILG.AI to tackle a key challenge: cutting energy consumption and CO emissions in their cement and waste-to-energy plants without compromising output or quality. Traditionally, assessing material quality in these complex industrial settings meant over two hours of manual lab analysis. This significant delay led to reactive process adjustments, preventing real-time combustion optimization and efficient industrial process control. NILG.AI developed an AI-powered system to revolutionize this. By using real-time sensor data and camera feeds, our solution instantly predicts material quality. This lets operators fine-tune combustion parameters on the fly, shifting operations from reactive to proactive industrial control. This application of AI in industrial optimization directly improves efficiency and sustainability. NILG.AI's AI solution for industrial efficiency delivered major environmental and operational benefits:   ~14,000 tons of CO saved annually: A significant drop in industrial emissions. 25% increase in production efficiency: Boosting operational efficiency and energy efficiency. This project proves that AI for sustainable industrial processes can transform even the most complex industrial environments, driving measurable progress in smart manufacturing and environmental sustainability. At NILG.AI, we're all about turning AI potential into real business value, right now.

Conventional cervical cancer screening faces hurdles: resource-intensive, requiring specialized training and equipment, and slow results. This leads to diagnostic delays and high dependency on manual interpretation, particularly impacting regions with limited healthcare infrastructure. Early detection with machine learning in a scalable, consistent manner is critical. NILG.AI developed an AI-powered cervical cancer screening system using medical image analysis, computer vision, and deep learning. This provides automated cervical cancer detection through real-time evaluation of cervical images, enabling faster, more precise diagnosis. Our system offers scalable diagnostic tools for all clinical environments. Key features:   Automated Visual Examination (AVE): Instantly evaluates cervical images for early signs. Image Quality Control: Filters low-quality images, reducing false negatives. Scalable Diagnostic Tools: Works efficiently in high-tech and low-resource settings. Our AI in healthcare solution delivers transformative results: Enables early detection with machine learning, improving patient outcomes. Improves diagnostic accuracy and consistency. Reduces dependency on manual interpretation. Our models are rigorously validated on multiple clinical trials and published:   Approaches to blur reduction in cervical images...: https://www.spiedigitallibrary.org/conference-proceedings-of-spie Comparison of performance of different automated visual evaluation quality assessment models: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/PC12369/PC1236906/Comparison-of-performance-of-different-automated-visual-evaluation-quality-assessment/10.1117/12. Ethical & Human-Centered AI Built with healthcare experts, NILG.AIs models comply with ethics and privacy. Our goal: enhance medical expertise through ethical AI in healthcare and artificial intelligence in diagnostics, empowering providers with advanced computer vision in healthcare tools.

Portugal's PERSU 2030 mandates over 6,230 new ecopoints by 2030 in the Algarve. Traditionally, manual planning made this recycling infrastructure optimization impossible within deadlines, creating a major challenge for smart city solutions. Meeting European policy targets for waste management demanded a radical shift from slow, inefficient manual planning. The goal was to enhance recycling infrastructure optimization and reduce manual effort significantly. In partnership with Algar and Sociedade Ponto Verde, NILG.AI developed an AI for public services approach. Using geospatial data analytics and machine learning for urban planning, our system integrated diverse data (waste records, population, satellite imagery, Google Street View) to provide accurate ecopoint placement optimization. This AI in waste management model considered urban constraints, environmental goals, and community needs, embodying data-driven sustainability. As Algar stated, this AI solution was "fundamental" for success, as the targets "would not be possible" otherwise Source: Indústria e Ambiente. The pilot in Lagoa showed rapid, measurable results, proving the power of artificial intelligence for sustainability: 83% reduction in proposal preparation time. Higher approval rates from municipal decision-makers. Planning time cut from weeks to hours. This initiative highlights how smart city solutions and data-driven sustainability revolutionize selective waste collection. The AI tool alone reduced proposal development time by 50%, making national expansion feasible Source: Indústria e Ambiente. NILG.AI and Algar earned the "Data Changemaker of the Year 2024" award for applying artificial intelligence for sustainability. This project serves as a prime model for using AI in waste management to achieve real-world benefits for people and the planet, ensuring compliance with critical environmental policies.