SCNET · Enterprise IT · Ankara, Türkiye
Enterprise AI solutions are production systems that turn organizational data into intelligence that can be governed and verified, combining source ownership, access control, evaluation, observability, cost management, human approval, and safe failure behavior. Sanal Çekirdek builds that complete system, not just the model layer.
Before AI, the meaning, quality, ownership and permitted use of data must be clear. Source systems, data dictionaries, access rules, quality controls and lifecycle are designed around the model requirement.
RAG is more than loading documents into a vector database. Chunking, metadata, permission filtering, hybrid search, reranking, source attribution and answer evaluation are designed for the task. Knowing when not to answer is part of the system behavior.
Not every problem requires model training. We first measure what prompting, RAG and tool use can achieve. If domain language or task behavior requires deeper adaptation, CPT and SFT datasets are prepared under controlled conditions and evaluated against the baseline model.
AI agents are not treated as unconstrained digital employees. They are governed workflows with access to specific tools and decisions. Permission boundaries, human approval, action logs, budgets, timeouts and rollback are defined from the outset.
RAG is usually the first choice for current, attributable enterprise knowledge. SFT is useful for task format and behavior. CPT may be considered when deeper domain-language adaptation is justified. The decision should be based on evaluation, not preference.
They can provide deployment control, clearer data flows and adaptation flexibility. They also increase responsibility for infrastructure, running the models, updates, security and performance. Total cost of ownership must be assessed against usage volume.
Technically yes, but permission, impact and reversibility determine whether it should. Low-risk actions may be automated. Financial, legal, security or high-customer-impact steps require human approval and strong audit records.
Sovereign AI means that data, models, infrastructure, access and operating responsibility can be controlled according to the organization's sovereignty and compliance requirements. It is an architectural and governance approach, not a single product.
The first step is not model selection; it is a clear use case and measurable definition of success.
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