A public institution works under a double pressure. The volume of cases, applications and enquiries keeps growing, while residents have grown used to the service standard they know from banking or e-commerce and now expect the same from a public office. In the middle sits a clerk who spends most of the day on repetitive work: retyping data from forms, sorting documents, answering the same questions again and again. That is exactly where public sector automation returns value fastest, because it does not touch the substance of a decision, it frees up the time around it.
In our experience, the key is to separate what can be automated safely from what must stay in human hands. We have no ambition to replace the officer who makes an administrative ruling. Instead, we build the layer that prepares the case, organises the data and offers suggestions, so the decision lands faster and on a fuller basis.
Where does administration actually lose time?
Before we reach for any tool, we map where the hours leak away. Usually the problem is not in the decision itself, but in everything around it. A document arrives through several channels at once, lands in the wrong department, waits to be retyped into the system, and meanwhile the resident calls to ask about status and ties up another employee.
Three sources of waste then stand out clearly: manual data transfer between the channel and the system, misrouting of cases, and handling enquiries whose answer is always the same. Each can be reduced on its own, without a sweeping IT revolution, which for a public body is often a hard precondition.
How does AI classify and extract data from documents (OCR, IDP)?
The first area we usually start with is documents. Intelligent document processing (IDP) combines OCR with models that understand the content, not just read the characters. Such a mechanism recognises the type of letter, pulls fields out of it and maps them to the right slots in the system.
In practice we build a pipeline that:
- recognises the document type (application, appeal, certificate, invoice) and routes it onward,
- extracts the key fields (applicant data, dates, amounts, case numbers) from files of varying quality,
- validates the data against registries and flags doubtful cases for manual review,
- passes a structured result to the line-of-business system instead of forcing a retype.
Crucially, the model does not “guess in the dark”: every field it reads carries a confidence score, so anything below the threshold lands with a person, not in the database.
How can an AI agent support the citizen on the first line?
The second layer is contact with the resident. An AI agent on the first line (L1) answers typical questions about procedures, required documents, deadlines and the status of simple cases. It does not invent content, because we base it on a RAG (retrieval-augmented generation) architecture: the model answers solely from the office’s verified knowledge base and can point to the source of its answer.
Such an assistant works in the channels residents already use and relieves the helpline of repetitive questions. The most important part, though, is the escalation mechanism: when a question goes beyond the knowledge base, concerns an individual case or requires a ruling, the agent hands the contact to a clerk together with the conversation context. This is a competence we genuinely develop within the RITS Group: we have already built an AI-based knowledge assistant for an insurance-sector client, so the “RAG plus human escalation” pattern is well known to us.
What does automated case triage involve?
Triage is a first read of a case and its routing to the right place. The model reads the submission, recognises its category and proposes a department, a priority and a deadline. Urgent matters do not wait in the general queue, and incomplete ones immediately trigger a flag about a missing attachment.
We treat triage as a recommendation, not a verdict. The automation organises the inflow and proposes an assignment, but the final word belongs to a coordinator who can change it with a single click. That arrangement combines the pace of a machine with human accountability.
How do you automate back-office processes (workflow, RPA)?
In the back office, the greatest value comes from combining two approaches. A workflow engine moves a case through successive stages and watches deadlines, while RPA (robotic process automation) takes over tedious operations in systems that lack a convenient API.
Here we mainly automate the dull, error-prone tasks:
- transferring data between applications that do not talk to each other,
- generating repetitive letters and confirmations from templates,
- updating statuses and notifications for the resident,
- assembling the case file from different registries into a single view.
Such robots run in the background, around the clock, and do not tire on the thousandth application. They also leave a clear trace of every operation, so nothing happens off the record.
How do you ensure a human in the loop, transparency and compliance?
This is the section we place above sheer effectiveness in the public sector. An administrative decision affects a citizen’s rights and obligations, so we do not hand it to the model. AI prepares the material, proposes and suggests, but a human rules.
We design these solutions around a few principles. Every action by the automation is logged and reproducible, so you can show when and on what basis something happened. The agent’s answers are grounded in sources, not in the model’s opaque “intuition”. We process data in line with personal-data protection requirements and sector regulations. Such transparency is not an add-on but the precondition for even discussing AI in a public office.
Which AI and data skills do we provide?
We do not sell a box labelled “AI”. We bring the people who can build such a solution and keep it running after go-live. In the RITS Group we combine software engineering with real AI and data practice (including our AI/LLM/data teams), and we have already delivered GenAI, RAG and LLM projects for commercial clients.
On a project for a public institution we typically staff:
- AI/ML and NLP engineers who design document classification and the RAG layer,
- data engineers who clean and integrate the sources the model is to rely on,
- process automation specialists (workflow, RPA) wiring up the back office,
- an architect and backend developers who connect it to the line-of-business systems.
We say it honestly: we do not yet have an AI deployment in public administration. We do, however, have analogous experience from sectors with similar demands around documents, compliance and customer service.
How do we do it at RITS?
We work as a nearshore partner from Poland, so we operate in the same time zone and understand the local regulatory context. We start with a workshop where, together with the institution’s team, we pick one high-volume, low-risk process and prove value before scaling anything.
From there we move in stages: from a pilot on a carved-out process, through measuring the effect, to a gradual rollout. We shape the team around the specific scope and keep the engagement flexible, from augmenting an existing IT team to a dedicated team running the topic end to end. We laid out the differences between these approaches in our article on IT engagement models.
Where to start with automation and AI in the public sector?
Start from the one process that hurts the most, not from a grand strategy. The best candidate has high volume, a repeatable flow and low risk of error, for example the initial classification of applications or answering typical enquiries. That defines the pilot scope, the data needed and the roles on the team.
Set the rules on human in the loop and compliance up front too, because in administration those decide success, not the technology itself. Everything else falls into place around those two decisions.
Thinking about automation and AI in public service handling? Let’s talk - we’ll pick the first process and match a team to your institution.