Run a large language model (LLM) in your own data center or private cloud. AI that complies with KVKK and banking regulations, doesn't depend on the internet, and has predictable costs.
An on-premise LLM means running a large language model on your company's own servers instead of a cloud service. Questions, documents and answers never leave the corporate network; which user can access which data is determined by your organization's permission rules.
ErucaAI first tries to resolve a request in tiers that don't need a language model: structured queries, a semantic cache, FAQ cards written by your experts, and ready-made recipes. The language model is called only when needed. This way, even an ordinary server can serve many users, and per-token cloud bills disappear.
You can also run hybrid if you like: routine work stays in-house, and the cloud model is consulted only for tasks you allow and with masked data.
Structured queries, semantic cache, FAQ cards and recipes; the language model runs only when needed.
Quantized open models run on ordinary servers; GPUs are added once the need is proven.
Open models licensed for commercial use are selected, updated and versioned on-premise.
Per-task egress rules: you decide which work may go to the cloud and which fields are masked.
The model can't see any record the user can't see in the ERP; every call is tied to a personal AI identity.
Response time, resource usage, cost and the full request-response history on a single dashboard.
Uninterrupted service with deployment across multiple servers, queuing and load balancing.
If you prefer, we run installation, updates and monitoring in your dedicated environment.
We review your infrastructure, choose the right model and hardware with you, and can also take over operations after installation.
Regulated institutions whose customer data must not leave the organization.
Organizations with strict confidentiality obligations working on patient and research data.
Institutions operating on restricted or air-gapped networks.
Companies that want to protect their technical know-how and trade secrets.
To start, a server with a modern processor and 32–64 GB of memory is enough. As the number of users and the workload grow, memory or GPUs are added; we make that call together based on shadow-run measurements.
For most enterprise tasks, namely document data extraction, classification, summarization and Q&A over your own data, local models adapted to your data perform well enough. For harder tasks, a cloud model can be used as a fallback where you allow it.
Because personal data isn't transferred outside the organization or abroad, cross-border transfer obligations don't arise; your organization sets the rules for access, retention and audit logging. We recommend doing the legal assessment with your KVKK advisor.
Yes. The model, indexes and all components run on the corporate network; updates can be brought in from outside in a controlled way.
With cloud services, cost grows linearly with usage. With an on-premise deployment, cost is largely fixed; in addition, thanks to the tiered engine, a significant share of requests is answered without ever reaching the language model.
Pick a real business problem; in a short pilot we connect your data, measure in shadow mode and review the results together.
Describe a real business problem; we'll define the pilot scope together.
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