Public sector
Public institutions handle large numbers of documents, applications and records. An AI system that runs on the institution's own infrastructure and shows the source behind every result can speed up finding information, processing incoming requests and preparing decisions.
Challenges
Data stays inside the institution
Citizen data and institutional documents often have to stay on the institution's own infrastructure, so the models have to run there too.
Every result needs a visible source
Staff can rely on a suggestion when they can see the document or record behind it. The same trail is needed for any later review.
Information is spread across systems
Documents, records and correspondence are often kept in different systems and formats. For AI to help, that information has to be reachable from one place.
Typical problems
Access to institutional knowledge
Assistants that search legislation, regulations, internal correspondence and archives, and give each answer together with the document it is based on.
Handling applications and requests
Classifying requests that arrive by petition, email or phone, routing them to the right unit and drafting replies for staff to review.
Extracting data from documents
Reading forms, reports and scanned documents and turning them into structured data that can go into the institution's systems.
Planning with data
Forecasting demand and workload from the institution's own records, spotting unusual situations early and supporting resource planning.
Maps and field data
Tracking land use, construction and changes to infrastructure from satellite and aerial imagery.
Our research areas
See all research areasWorking with sensitive data
Training and running models on sensitive records without the data leaving the institution.
Foundation models
Open-weight language models adapted to Turkish and to the language of official documents.
Multimodal understanding
Reading scanned documents, forms and tables together and turning them into data that can be searched and processed.
Interpretability & evaluation
Measuring how often suggestions are right, and showing the document or record each one is based on.
Related services
All services- AI assistantsAssistants that answer from your own documents and systems, with sources and access control.
- AI agentsAI agents that carry out defined tasks in your systems, within permissions and approval steps you control.
- Multimodal AIApplications that combine text, images, audio and documents to complete one defined task.
- Time series forecastingForecasts of demand, load, capacity and sensor values that show how certain they are.
- Geospatial AIMaps, change detection and site analysis from satellite, aerial, drone and location data.
- LLM deploymentLanguage models running on your own servers or private cloud, sized and secured for your workload.
Work with us
Work usually starts with one unit or one process, and the system is installed on the institution's own infrastructure. Once the results are measured, the scope can extend to other units.
R&D projects
For problems with no proven solution yet. We test approaches on your data and work toward a prototype in agreed milestones. At each milestone we tell you plainly what looks feasible and what doesn't.
Software development & consulting
For problems with known solutions. We build production software on proven methods, or advise your team on architecture, tooling and model choice.
Funded research
Joint R&D projects backed by national and international research funding, with industry and academic partners.
Corporate training
Training programs tailored to your team. Topics include everyday work with AI assistants, product development, integration and strategy.
Frequently asked questions
Can the system run on our institution's own servers?
Yes. Models run on your institution's servers, and the data does not leave the institution.
How is citizens' personal data protected?
Personal data is processed in line with KVKK and, where relevant, GDPR, and access follows your institution's permission rules. How each result was produced is also recorded.
Does the AI make the decisions?
No. The system finds information, classifies requests and prepares drafts. Staff always make the decision.
Where should a public institution start with AI?
Usually with a high-volume task whose results are easy to measure, such as a common type of request or a frequently used source of information.
Who runs the system after handover?
Your institution's own staff. The system is handed over with its documentation, to the people who will run it. We can also provide support after that if needed.
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