When public AI does not fit your requirements
Engineering designs, internal correspondence and contracts cannot always be sent to an arbitrary external service. Banning AI leaves teams without a practical tool. A private environment creates an alternative with defined rules for use.
Architecture follows the workload
We assess tasks, languages, concurrency and acceptable response times before proposing a model and hardware. An on-premise server, private cloud and disconnected environment have different costs and operating dependencies.
- Evaluate models on representative Bulgarian and English tasks.
- Separate application, data and model-serving network zones.
- Plan controlled upgrades, monitoring and recovery.
Evidence before expansion
A pilot measures answer quality, peak demand and infrastructure constraints. External services, telemetry and model licences receive explicit review. Local deployment alone is not a security guarantee.
From assessment to operations
Start with one bounded workflow and approved data. A measurable pilot leads to technical acceptance, operator training and an agreed support scope. An internal technical-manual assistant is one illustrative scenario, not a claimed customer result.
The information flow
- Employees
- Access control
- Private model
- Approved data
Models and documents run on your infrastructure. Network policies determine permitted connections.
Practical questions
Do we need our own GPU server?
Not always. The answer depends on model size, request volume, response-time requirements and permitted deployment options.
Can it run without internet access?
Potentially, with suitable models, local dependencies and an offline update process. This must be verified for the chosen architecture.