AI assistants
We define the assistant’s role, available information and permitted tools. Human approval and authority boundaries for critical actions are considered from the outset.
AI Infrastructure
Institutional knowledge.
AI that works with it.
We develop AI assistants, institutional knowledge bases and workflow automation around your data, access boundaries and existing operations.
SERVICE AREAS
AI Infrastructure
Make AI part of your operations.
We assess data sources, access permissions and workflows to develop an AI solution suited to your operations.
RAG and knowledge bases
We assess institutional documents for provenance, freshness and access. We design information retrieval and source citation workflows to support answers with relevant institutional sources.
Workflow automation
We plan repeated steps, triggers and exceptions together, including what happens when information is missing, an error occurs or approval is needed.
On-premise AI
We assess deployment options around data control and local computing needs, defining infrastructure, access, operations and maintenance responsibilities.
System integration
We connect AI use with existing data sources and business tools, within the permissions and capabilities those systems actually provide.
Testing and operational readiness
We consider representative scenarios, evaluation criteria and everyday operation together, helping teams understand capabilities and where human control remains necessary.
Our approach
Start with a use case.
Then choose the infrastructure.
Institutional data boundaries
Sources, access permissions and update processes are early decisions. We assess on-premise and other deployment options against these needs. Local deployment does not remove security or data-governance responsibilities.
A controlled starting point
We define the first scenario and evaluation criteria, considering missing sources, ambiguous questions and requests outside permitted authority as well as expected answers. The aim is to state the conditions for appropriate use, not merely show a demo.
Keep the project scope verifiable
Our Hacettepe University project concerns on-premise AI server infrastructure supporting institutional data control, local computing and customised AI use. We do not imply unverified hardware specifications, model architecture or capacity.
AI Consulting (Generative AI)
AI consulting. An actionable roadmap.
Assess the need and data readiness
We review use cases, data sources, access boundaries and human approval steps before implementation. The problem and evaluation criteria come first.
Compare suitable options
We compare ready-made tools, integrations, RAG and on-premise deployment against your needs. No model or technology is the default for every institution; data control, service dependencies and operational responsibilities matter.
Validate with a focused pilot
We assess answer quality, source use and failure modes within a bounded scenario. Expansion decisions follow agreed acceptance criteria, not a demonstration alone.
SELECTED WORK
Hacettepe University.
On-premise AI server infrastructure supporting institutional data control, local computing capacity and customised AI use.
Explore the project scope ↗No unverified hardware, model or capacity specifications are claimed.
Connected capabilities