Applied AI & Agentic Workflows

The most common starting point in Italian mid-market: data lives in shared folders, email threads, scanned PDFs, legacy ERP exports, and someone's Excel file. No data governance. No API. No structure.

The wrong answer is buying an AI tool. The right answer is understanding what questions the business needs to answer from that data — then building the retrieval layer around those questions.

Our approach: a 2-week discovery sprint to map what exists, where it lives, and what quality it is. Then a 4-week RAG MVP on a defined, bounded corpus. Validated with real users before any further investment. This is the entry point for organisations that have never shipped an AI system.

Civil, structural, aerospace, industrial engineering firms sit on enormous corpora: ISO and UNI norms, EN standards, project specifications, capitolati, contractor documentation, revision histories, technical manuals. A senior engineer knows this corpus by instinct. But the corpus has thousands of pages, changes constantly, and varies by client and jurisdiction.

We build RAG systems that reason over technical documentation. Engineers query in natural language and receive cited answers — norm, article number, revision. The system is version-aware: it knows which revision of a standard applies to a given project date, and will not answer with an obsolete clause.

Every response is logged: who asked, when, which document answered. This is not optional in contexts with professional liability.

An AI agent acting on financial data is not a chatbot. It is a system making operational decisions. Internal audit, risk management, and compliance will ask: who authorised this action? Which model decided? On what data? Is the log immutable? Was the four-eyes principle enforced?

We design agent architectures with human-in-the-loop approval gates before any write action, immutable audit trails with configurable retention, RBAC with segregation between front office / risk / compliance / IT, and full LLM call logging: input, output, model version, timestamp — for every inference.

Compliance alignment: DORA (operational resilience, incident logging), MiFID II (decision traceability), AI Act (high-risk system requirements), GDPR (data minimisation, right to explanation).