SDK

Infrastructure maintenance still relies on manual reports or sporadic inspections. Detecting anomalies — potholes, cracks, breakages — requires specialized personnel, and interventions are reactive rather than planned. Whoever monitors needs to act the moment an anomaly emerges, not read a report a week later.

A distributed platform that analyzes video streams in real time with YOLO models on a microservices architecture. It's not just computer vision: the operator works inside a single web app — georeferenced map, filterable table, charts — and can qualify, dismiss or forward a report with one click. Training pipeline integrated for fine-tuning on the client's dataset, model hot-swap with no downtime.

Continuous detections on the operator dashboard, one-click action, georeferenced dataset ready for predictive maintenance planning. Modular architecture: same pattern applies to environmental monitoring, industrial quality control, perimeter security.

Recruitment firms handle thousands of CVs in heterogeneous formats. Manual extraction of skills and experiences is costly and inconsistent, and slows down time-to-hire. Legacy systems don't guarantee secure data isolation between different clients.

Multi-tenant SaaS platform with AI-powered CV parsing based on Claude Haiku. Java/Spring Boot backend, PostgreSQL with Row-Level Security for native per-tenant isolation, full-text and fuzzy search with pg_trgm, React + TypeScript frontend.

CV analysis time from minutes to seconds, automated candidate-to-position matching, architecture ready to serve multiple clients from a single installation. Pattern replicable across any domain with unstructured document intake: legal, insurance, healthcare, procurement.

In most companies, development teams use AI sporadically, without a shared method. Every developer has their own habits, there's no company-wide framework. The result: incremental productivity that doesn't translate into a real efficiency leap — and no way to prove the actual ROI of AI to leadership.

Intensive 4-week program (Track A, entire team) with an optional 6-week advanced track for senior developers. A structured program that takes the team from occasional use to a measurable AI-first workflow. Exercises run on the client's real codebase, not on didactic examples: at the end, the team has transformed how they work on actual projects, not on toy examples.

Debug and repetitive development time reduced by up to 30%, documentation writing time halved, faster code review cycles, accelerated onboarding of new developers. Modular program: adaptable to the tech stack, team seniority and time constraints; financeable via Fondimpresa.