On-prem RAG over regulatory documents, citizen-facing assistants, with strict data sovereignty.
The public sector is one of the most complex terrains for AI adoption: extraordinary data sensitivity, high regulatory expectations, constrained budget. Here on-prem and air-gapped capability is not optional, it's a baseline requirement.
Our deployments work within these constraints: data never leaves your own infrastructure, every decision is auditable, and the solutions fit existing administrative processes.
On-prem and air-gapped options, aligned with industry-specific regulations.
Every deployment tied to a concrete business metric, not an AI project, a business project.
Every decision traceable, every answer cited, every access logged.
Industry-specific use cases, terminology, integration patterns, not generic AI.
These are the most commonly deployed use cases in this vertical. Each is calibrated to the industry's actual needs, with measurable ROI.
Internal assistant that asks back over the full applicable legal and regulatory collection: with citation, source reference, hallucination-free.
External, citizen-oriented surface: form helper, process explainer, multilingual. On-prem, audit-logged.
Classification, extraction, prioritization of large incoming volumes, automatically, with caseworker quick review.
In the public sector data sovereignty is a requirement, not a preference. The solution must work on a closed network, every step of decision support must be traceable, and the system auditable. We therefore recommend local deployment exclusively here.
We start where case volume strains statutory deadlines: registration, classification, summarisation. The aim is faster assignment, not replacing the substantive decision.
In the public sector data cannot leave the controlled environment, so we recommend local deployment only. We record that the system must work without an internet connection.
We measure on completed cases where correct classification is known. The case officer checks every proposal, so accuracy is quantified before live rollout.
Every AI-supported step is traceable: what went in, what the system proposed, who approved it. This is a condition of rollout, not a later addition.
These are the applications that pay back fastest in this sector. Each can be deployed independently.
| Use case | What it does | What it delivers |
|---|---|---|
| Case handling | Classifying and summarising submissions | Faster assignment |
| Legislation search | Across current regulation, with citations | Consistent reference basis |
| Front-office support | Draft responses for case officers | Shorter handling times |
| Document anonymisation | Preparing freedom-of-information responses | Automated redaction |
| Internal knowledge base | Search across procedures | Fewer colleague queries |
On case files already completed, where correct classification is known. The case officer checks every proposal, so accuracy is quantified before live rollout. Measurement runs on a closed network, on a local deployment.
For classification and summarisation it performs reliably, especially when the system works from the office's own records. It makes no substantive decision: it speeds assignment, and every step stays traceable with its input and approver.
Yes. Local deployment exists precisely for this: the model runs on the organisation's own infrastructure with no outbound connection and no token-based billing. In public administration and critical infrastructure environments this is often not an option but a baseline requirement.
In two layers. First, data does not leave the infrastructure, so there is no transfer. Second, the model accesses files with the user's privileges rather than an all-seeing service account, so existing access rules remain in force. The anonymisation use case is specifically designed to filter out personal data.