The organising fact about Rome is scale of correspondence. Ministries, agencies, national utilities and telecoms operators all run enormous flows of documents between themselves and the public, and most of the useful AI work in the city is somewhere in that flow.
Public administration brings citizen-facing document processing: applications, claims, certificates, appeals. The failure mode here is specific and worth naming — a confidently wrong answer to a citizen is not a bad customer experience, it is a decision someone may act on and later contest. So these systems get built to assist a named official rather than to replace one, with the source document quotable in the response. That is the substance of our public sector work.
Energy and utilities arrive with regulatory filings, grid and asset records, and a long tail of technical documentation that engineers currently search by memory. Retrieval over that is unglamorous and it is usually the highest-value thing available.
Defence and aerospace organisations arrive with the strictest deployment constraints of anyone we speak to, and often the answer is an open-weight model running on their own hardware with no external call at all. We describe what that costs and what it gives up under on-premise AI, rather than pretending it is free.
Telecoms bring volume: support contacts in the millions, where even a small deflection rate is material and a small error rate is a regulatory problem. The engineering is in the routing and the refusal behaviour, not the fluency.
Across all four, the deliverable we are most often asked for after the system itself is the record of how it behaves over time, which is why observability tends to be in scope from the start here rather than added in year two.