Services

Concrete services to bring AI into real systems.

From AI-assisted software development to private architectures, from RAG systems to verification of generated artifacts, all the way to production.

AI accelerates. Systems thinking governs.

Aree di intervento

Offerte comprabili, con output tecnici espliciti.

Design and development of custom software with controlled use of AI for analysis, prototyping, development, and testing.

Controlled conversational interfaces for documents, procedures, manuals, contracts, tickets, and corporate knowledge bases.

Architectures in which models, data, and applications sit in the right place: cloud, private, on-premise, or local.

Technical gates that turn AI-generated or AI-accelerated artifacts into verifiable, shippable software.

Docker, Kubernetes, CI/CD, hardening, logging, monitoring, and deployment to take prototypes to production.

Assessment of goals, data, risks, architecture, feasibility, costs, and minimum delivery path.

Each service is designed to produce verifiable artifacts, not just abstract consulting.

Design and development of bespoke software with controlled use of AI to accelerate analysis, prototyping, development, and testing.

Output: backend; API; dashboard; automations; microservices; internal tools; integrations.

Systems to query documents, procedures, manuals, contracts, tickets, or corporate knowledge bases through controlled conversational interfaces.

Output: chatbot interno; ricerca semantica; sistema documentale intelligente; knowledge assistant; RAG privato.

Design and implementation of controlled AI environments for sensitive or confidential data.

Output: LLM locale; deploy su GPU; ambiente containerizzato; RAG on-premise; policy accessi; isolamento dati.

Audit and verification pipelines for AI-generated code and artifacts.

Output: repository analysis; tests; runtime validation; dependency control; SBOM; CVE check; technical report; remediation plan.

I bring prototypes and software into runnable environments: Docker, Kubernetes, CI/CD, hardening, logging, monitoring, and deployment.

Output: Dockerfile; compose; manifest Kubernetes; pipeline CI/CD; hardening; documentazione deploy; osservabilità.

Initial assessment of an AI/software project: data, goals, risks, architecture, feasibility, costs, and minimum viable path.

Output: report di fattibilità; mappa rischi; opzioni architetturali; roadmap MVP; stima fasi; raccomandazioni.

Il primo passo è chiarire obiettivo, dati, vincoli, ambiente e rischio.