Applied AI in complex data environments
A selection of technical case studies illustrating how we approach real-world problems across domains and data modalities.
These case studies are representative of our technical approach. Specific client details and proprietary outcomes are anonymized or simplified where required by confidentiality agreements.
Satellite analytics for large-scale ecosystem monitoring
Development of a remote sensing platform to monitor vegetation dynamics, land use transitions, and ecosystem health indicators across large geographic areas using multi-temporal satellite imagery.
Challenge
Environmental monitoring initiatives often rely on periodic field surveys that are expensive, slow, and difficult to scale across large territories. A regional monitoring program required continuous observation of vegetation dynamics, ecosystem degradation signals, and carbon sequestration indicators across a territory exceeding two million hectares. The available satellite data provided rich spectral information but required complex preprocessing and time-series analysis before reliable environmental indicators could be produced.
Approach
Deepleey designed a scalable satellite analytics pipeline integrating Sentinel-2 imagery with temporal compositing and cloud filtering. Vegetation indices such as NDVI and EVI were extracted across time, and a change detection framework was implemented to identify statistically significant shifts in ecosystem conditions. The resulting indicators were validated against ecological sampling plots and integrated into a geospatial database enabling environmental reporting and long-term ecosystem monitoring.
Outcomes
- Automated generation of vegetation health indicators at 10m spatial resolution
- Early detection of ecosystem degradation patterns
- Spatial indicators usable for sustainability reporting and environmental planning
- Scalable monitoring architecture covering multi-million hectare regions
Automated visual inspection for product integrity in manufacturing
Development of computer vision systems for automated inspection of product integrity and process monitoring across industrial production lines.
Challenge
Manufacturing environments frequently rely on visual inspection systems to ensure product quality and structural integrity during production. However, inspection processes are often fragmented across multiple machines and stages of the production workflow, generating heterogeneous image data that is difficult to consolidate and analyze. In addition, variability in lighting conditions, production speed, and product geometry can make consistent defect detection challenging. The objective was to design a unified analytical framework capable of supporting automated inspection and improving traceability of quality events across the production process.
Approach
Deepleey developed a modular computer vision pipeline capable of ingesting visual inspection data from multiple stages of the production line. Detection models were trained to identify anomalies and structural inconsistencies across different product types, while data normalization procedures ensured consistent image preprocessing despite variations in acquisition conditions. Inspection outputs were integrated into a centralized analytical system enabling correlation between visual anomalies and upstream process parameters. This architecture enabled continuous monitoring of product integrity and supported the identification of recurring defect patterns.
Outcomes
- Automated detection of visual anomalies affecting product integrity
- Improved traceability of quality events across manufacturing stages
- Integration of inspection data into process monitoring workflows
- Data-driven insights supporting continuous quality improvement
Relational modeling of product and distribution networks
Application of graph-based machine learning techniques to model complex relationships between products, suppliers, and distribution channels across multiple markets.
Challenge
Retail and distribution ecosystems generate highly relational data structures involving products, brands, suppliers, and points of sale. Traditional analytical approaches based on tabular data struggle to capture these relationships, especially when attempting to estimate product substitution effects or market propagation patterns across distribution networks.
Approach
Deepleey developed a graph-based representation of the product ecosystem where nodes represented products, brands, and distribution entities, and edges captured co-occurrence and supply relationships. Graph embedding techniques were used to learn structural similarities between products across markets. These representations enabled predictive models capable of estimating product propagation dynamics and market similarity patterns.
Outcomes
- Graph embeddings capturing cross-market product similarity
- Improved estimation of distribution propagation patterns
- Enhanced analytics for supplier-product relationships
- Scalable graph infrastructure supporting large product catalogs
Human flow and behavior analysis in physical environments
Developing a privacy-preserving computer vision system to analyze human flow patterns, dwell time, and spatial interactions in large indoor environments.
Challenge
Organizations managing large physical environments often lack quantitative data on how people move through and interact with spaces. Traditional observation-based studies are limited in scale and duration, while video surveillance systems are rarely structured for analytical purposes. At the same time, strict privacy regulations prevent the use of biometric identification or personal tracking methods.
Approach
Deepleey implemented an anonymized detection and tracking pipeline using pedestrian detection models producing bounding boxes without identity features. Movement trajectories were aggregated to generate flow density maps, dwell-time metrics, and transition matrices between spatial zones. All processing was performed locally without storing identifiable visual data, ensuring full compliance with privacy regulations.
Outcomes
- Real-time flow heatmaps with configurable temporal aggregation
- Zone dwell-time metrics supporting spatial layout optimization
- Identification of congestion patterns and spatial bottlenecks
- Full privacy compliance with no biometric data storage
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