Cohort: 2 

Location: Rockville, Maryland (US) and Bangui, Central African Republic

Application Title: Private LLM infrastructure and edge intelligence for urban maintenance and safety 

Team Members: Samuel Alemayehu (Executive Sponsor), Olivier Nizeyimana, Abbai Belai, Florent Bodo

The challenge 

Across rapidly growing cities, particularly in parts of Africa, infrastructure systems face persistent challenges. Roads deteriorate faster than they can be repaired. Construction sites operate under uneven safety oversight. Municipal teams responsible for maintenance and inspection are often small and have limited access to digital tools or specialized engineering expertise. 

Much of this work falls to mid-skill professionals; inspectors, technicians and site coordinators, who are expected to manage complex assets with limited support. At the same time, many smart-city solutions are designed for high-income markets. They rely on centralized cloud platforms, assume stable connectivity, and place control of data and workflows outside local institutions. In practice, this makes them difficult to localize and harder to govern.

The solution 

Locally deployed AI for infrastructure oversight 

Cambridge Industries focuses on deploying AI systems that operate within locally controlled environments and are accessible to people working directly in the field. 

The company has developed a private large language model (LLM) infrastructure stack that runs entirely within client-owned systems. This approach allows municipalities and contractors to retain control over data, workflows and configuration. Complex engineering standards and safety regulations are translated into mobile-friendly tools that inspectors and site managers can use on site, supporting more consistent decision-making without requiring specialized training. 

Rather than centralizing intelligence in remote platforms, the system is designed to function where infrastructure is built and maintained. 

How it works 

AI tools embedded in day-to-day workflows 

The platform is implemented through two primary applications. 

Orbit is a predictive road-maintenance tool that enables inspectors to capture road-surface images using smartphones. AI models analyze these images to identify cracks and early-stage deterioration, then generate colour-coded heatmaps that indicate where maintenance is most urgent. This allows municipalities to prioritize interventions before minor defects develop into more extensive repairs. 

In pilot deployments across Nigeria (Butterfly Island), Bangui (Central African Republic), and Lualaba (DRC); in partnership with large-scale developer partners, approximately 150 inspectors submit around 20 inspection captures per week per municipality. The system processes more than 12,000 AI queries per month, and early pilot results indicate up to a 40% reduction in emergency road-repair costs within six months.

The second application, Ndege, supports construction-site safety monitoring using autonomous drones. Drones collect imagery that is analysed to identify missing barriers, protective equipment or signage. Alerts are sent directly to site managers via WhatsApp or dashboard interfaces, allowing issues to be addressed quickly. 

At pilot sites, the system records around 400 drone flight hours per month across 20 operators, has generated more than 3,000 AI-generated safety alerts, and has contributed to a 50% reduction in recorded incidents based on site logs over the pilot period.

How it differs from conventional approaches 

Keeping intelligence local 

Both Orbit and Ndege are built on private multimodal AI models that combine computer vision with fine-tuned LLMs derived from LLaMA-family and other Open Source architectures. The models are trained on locally collected data, including smartphone footage, drone imagery, inspection logs and more than 100 standardized engineering and safety documents/manuals/checklists. 

All models run on client-owned servers with encrypted data flows and role-based access control. This avoids reliance on external cloud platforms and allows systems to be adapted to local regulatory, operational and connectivity conditions. 

By embedding decision support into smartphones, drones and edge devices, the platform supports decentralized use without removing oversight from local institutions. 

Impact 

• Pilot results indicate up to 40% reduction in emergency road-repair costs over a six-month period (pilot sites) 
• Annualized, pro-rated maintenance costs reduction from approximately $5 million to $3 million per municipality based on pilot spend patterns, with additional scale efficiencies anticipated as more of the road network is covered
• Over 12,000 model inferences processed per month across the piloted road network(s)
• Approximately 400 drone flight hours per month across pilot sites
• More than 3,000 AI-generated safety alerts issued during the pilot period
• Reported up to 50% reduction in recorded safety incidents during the pilot period (based on site logs)

Why it matters 

Supporting more reliable infrastructure maintenance 

Cambridge Industries’ approach illustrates how locally deployed AI can support infrastructure maintenance and construction safety in contexts where resources and expertise are limited. 

By keeping data and decision-making close to the field, the system helps municipalities identify issues earlier, prioritize interventions and reduce reliance on emergency repairs. This supports more consistent maintenance outcomes while maintaining local control over critical infrastructure systems.