Selected work
A cross-section of the work — fuel, retail, telecom, mobility, logistics, industry, and enterprise AI. Open a client for the full picture: what we did, the scope, the result.
Customer analytics, segmentation, and personalisation built on device-and-consumable behaviour.
Behavioural segmentation, personalised communications, tariff and promo-response analysis, and first churn models for retention — for TravelSIM, CSC Telecom’s travel-eSIM brand.
Business-problem framing, an architecture blending operations research, ML and physical engineering rules, team-shaping support, and development oversight.
Business-problem framing, a target architecture, an ML prototype to prove feasibility, implementation-vendor support, and results control against a baseline.
Reporting infrastructure, anomaly detection, and clustering models that turn operational data into ops-level signals.
AI-readiness and roadmap, a DWH/BI foundation, customer segmentation, anti-fraud analytics, internal assistants, and automation across payments, logistics and procurement.
A pilot AI assistant for ISO audit preparation, built with a confidential enterprise client. The first version focused on ISO 9001, with future applicability to more specialized standards — automotive (ISO 16949) and medical (ISO 13485).
An AI strategy for a global Testing, Inspection & Certification firm — a benchmark of where AI actually creates value in TIC, turned into a CEO-level roadmap built on the firm’s own data and operations.
An AI-powered customer-operations platform for an audio company serving nightclubs, venues, events and entertainment clients — customer-request handling, document processing, an internal knowledge base, and task-oriented AI agents connected to the team’s workflows.
More work we've delivered, across industries.
Winery. Advanced analytics and predictive ML to forecast wine demand, optimise pricing, and segment consumers for targeted marketing.
Food-ingredients and confectionery manufacturer and retailer (Buona Sera shops). A heuristic and Operations-Research model that optimises product distribution between suppliers and stores — accounting for demand, availability, supplier constraints, and store-level needs. Better inventory allocation, less manual planning, and clearer visibility over logistics decisions.
Tell us the problem you are trying to move — margin, cost, availability, customer value, or productivity. We will tell you whether data and AI can realistically help, and what the first step looks like.