Case Studies

The Challenge

Customs administrations process thousands of daily declarations with mainly manual targeting. Control rates are high but results are low: too many false positives, overloaded inspectors and complex fraud slipping through the cracks.

Our Solution

Our solution relies on a hybrid scoring engine combining multiple complementary Machine Learning algorithms, trained on declaration history and control results. The system analyzes 49 variables per declaration and assigns a real-time risk score. In parallel, revenue forecasting and anomaly detection modules complete the decision suite.

Expected Impact

The Challenge

Tax data is scattered across heterogeneous systems without a consolidated view. Fraud detection relies on random checks, and teams have no tools to anticipate revenue or identify at-risk taxpayers.

Our Solution

Our solution unifies tax, customs, banking and cadastral sources into a consolidated data platform. A scoring engine evaluates risk per taxpayer, while graph algorithms detect complex fraud patterns (VAT carousels, fake invoices). Forecasting models anticipate revenue by tax type.

Expected Impact

The Challenge

The lack of medical imaging specialists delays diagnoses. Epidemics are detected late, medication stockouts are frequent and patient records remain largely unexploited for public health management.

Our Solution

Our platform combines Deep Learning models for medical image analysis, an epidemiological forecasting module for early outbreak detection, patient risk scoring and a mobile application allowing health workers to work offline in the field.

Expected Impact

The Challenge

Social registries are incomplete and fragmented, beneficiary identification is slow, duplicates are hard to spot and payment tracking lacks traceability, making social program management ineffective.

Our Solution

Our solution consolidates existing social registries into a unified database, applies Machine Learning models for intelligent beneficiary targeting, detects duplicates through multi-source cross-referencing and automates payment tracking with complete traceability.

Expected Impact

The Challenge

Production records are still manual and unreliable, mineral traceability is fragmented across multiple actors, yield forecasts are non-existent and regulatory reporting is time-consuming.

Our Solution

Our platform centralizes production data from all sites, coupled with an offline-capable field data entry mobile app. Machine Learning models forecast yield by site and deposit. A traceability module ensures lot-by-lot tracking from extraction to export, with automatic regulatory report generation.

Expected Impact

The Challenge

Many logistics operations are still managed on paper and by phone: no real-time fleet visibility, no delivery traceability, unoptimized routes and inability to control per-trip costs.

Our Solution

Our centralized web platform, coupled with an offline-capable driver mobile app, integrates real-time GPS tracking, automatic route optimization, warehouse management with threshold alerts, and demand forecasting models to anticipate seasonal peaks.

Expected Impact

The Challenge

Architectural plan analysis is entirely manual: an architect must examine each plan to identify rooms, calculate surfaces and write descriptions. This process takes several hours per plan and remains subject to human error.

Our Solution

Our Deep Learning and Computer Vision model reads an architectural plan as input (image or PDF) and produces a complete analysis: room identification, area calculation, opening detection and automatic property data sheet generation. The model is trained on a large corpus of real architectural plans.

Expected Impact