Where does AI help public institutions?
In public institutions, the value of AI is rarely a flashy chat screen. It is the time won back from regulations, paperwork and applications. That has to happen with the data under the institution's control and the decision left with the civil servant.
Regulation and internal-rules assistant
Staff ask questions in plain language over regulations, circulars, procedures and manuals, and the system shows each answer with the document it relies on.
Staff stop losing time hunting for the right clause.
Document and application routing
Incoming documents and applications are sorted by type and routed to the right unit; unclear cases go to staff.
Routing gets faster, and staff still make the final call.
Field extraction from documents
The required fields are read from scanned forms, petitions and attachments and written into the existing workflow.
Less manual data entry and fewer missed fields.
Knowledge and stakeholder platforms
Platforms that bring an institution's technical documents, announcements and stakeholder communication into one searchable place.
Industry, academia and government look at the same source.
Our RAG and enterprise semantic search page explains how document-based assistants work, and our page on in-house AI solutions covers the on-premise deployment model.
Where does the data stay?
A public buyer's first question is legal, not technical: where does the data go? So we choose the deployment model from the institution's data classification, not from the technology.
| Deployment model | Where the data and model run | When it fits |
|---|---|---|
| On-premise | In the institution's own data centre; can run on a closed network | Sensitive data, closed-network requirements or no data allowed to external services |
| Dedicated private environment | In an environment reserved for the institution, with its location fixed by contract | Data location must be guaranteed without a hardware investment |
| Hybrid | Sensitive documents on premises, the rest in the dedicated environment | Documents of different sensitivity levels are used together |
The choice starts with a data classification that shows which document types may be processed where. Our article on on-prem and sovereign AI for Turkish enterprises summarises the wider debate.
KVKK and data security
Public institutions are data controllers that process personal data, and their obligations under Türkiye's Personal Data Protection Law (KVKK) apply to AI systems too. We build systems with engineering controls that help you meet those obligations:
- Permissions are inherited from the source. User permissions come from the institution's identity management system; a document a member of staff cannot open is not opened to them through AI either.
- Every query is logged. Who asked what, which documents the system relied on and what it answered are kept for audit.
- Personal data can be masked. The data classification decides which fields are masked before processing.
- No general model training on institutional data. The commitment that institutional data will not be used to train general-purpose models is written into the contract.
- People make decisions that affect citizens. The system produces summaries, suggestions and routing; decisions that create rights or obligations are made with a civil servant's approval.
These controls support compliance technically; the legal assessment remains with the institution's own legal unit and data controller. Our article on KVKK's generative AI guide explains what the authority's guidance means for engineering teams, and Turkey's AI regulation: where it actually stands gives the wider picture.
Let's look at which deployment model fits your institution's data classification.
Book a callDelivery that fits your procurement process
In a public project a good model is not enough; the proposal, delivery and acceptance have to fit the institution's procurement and approval processes. We plan delivery backwards from that reality:
- A clause-by-clause response. We prepare the proposal to answer every clause of the institution's technical specification, and say plainly if a clause cannot be met.
- An itemised proposal. Software development, installation, any licensed components, training and maintenance appear as separate items.
- Written acceptance criteria. Acceptance test scenarios and pass conditions are written together before delivery, so acceptance rests on measurement rather than debate.
- Documentation. Installation, user and administrator guides and an architecture document are part of the delivery.
- Source code and model ownership. Who owns the source code and any model adapted with institutional data, and which open-source components are used, are stated separately in the proposal.
- Training and maintenance. User and administrator training and the scope of post-launch maintenance and support are defined from the start.
SYD HUB: delivery at public-sector scale
A forum platform for a ministry
- Institution
- Turkish Ministry of Environment, Urbanisation and Climate Change
- Problem
- Industry's green transformation needed one place where industrial facilities, lead officers and officers, academics, universities and public institutions could meet.
- Approach
- We designed and built a forum platform for industry's green transformation: announcements, surveys and discussions in one shared feed, with sector categories and working groups on the same platform.
- Result
- The platform is live on the ministry's own domain: sydhub.csb.gov.tr. Usage figures belong to the institution and are not shared.
On this project we worked with technical specifications, acceptance criteria, internal approval cycles and documentation expectations. See our SYD HUB case study for the details.
How we start
- Introductory call
We listen to the need, the existing systems and the constraints. The call is free; if you prefer, we sign a non-disclosure agreement first.
- Needs and data inventory
We settle which documents and systems will be used, the sensitivity of the data and the deployment model.
- Pilot scope and acceptance criteria
Start with one unit and one use case, with success criteria written before the pilot.
- Pilot
The system runs in the institution's environment on real documents and is measured.
- Rollout
An accepted use case is extended to new units and document sets on the same infrastructure.
- Maintenance and support
Performance is monitored, and the system is kept current as documents and regulations change.
If it is not yet clear which use case to start with, the fixed-scope consulting engagement in our AI consulting service puts that decision in writing.
What drives the price?
In public projects we show every cost item separately in the proposal. The main factors behind the total are:
- The deployment model, and the hardware needed for an on-premise installation.
- The number of systems to integrate: document management, identity management, records and application systems.
- The number of users and units, and expected concurrent use.
- The state of the documents: the share of scanned paper, format variety and data preparation effort.
- How deep the security, logging and documentation requirements go.
- The scope of training and maintenance.
We quote in writing once the scope is clear, and after go-live work under a maintenance and support agreement with a written scope.
If your specification or needs statement is ready, let's read the scope together.
Book a call for your institution