AI implementation for businesses
Company AI with deliberate control over your data.
Your AI deployment should reflect where company data may go and who may access it. We plan and implement private AI environments, comparing on-premises infrastructure, private cloud and managed options against your requirements.
Discuss your AI projectA first conversation to check feasibility, scope and the right starting point.
- Sign in with an authorised company account
- Process the request in the selected environment
- Apply the agreed access and retention rules
Architecture example. The actual data boundary depends on the model, infrastructure, connected tools, telemetry and support arrangements.
Where this can help
For businesses that need to assess data location, administrative access and provider dependency before introducing company AI.
Run models on your infrastructure
Evaluate a self-hosted model for an agreed task, including hardware needs, response quality, load and responsibility for updates.
Control external data flows
Document which content goes to which service. Configure approved endpoints and review logging, retention and optional external connections.
Reduce provider dependency
Use documented interfaces and exportable configuration where feasible, and assess how a future model or hosting change would work.
What we can deliver
- Deployment comparison and a documented data-flow architecture
- An agreed model environment and application connection
- Role-based access, secrets configuration and retention settings
- Quality and capacity evaluation plus an operations handover
We agree the scope after reviewing your systems, data and operating requirements. Hosting, licences and ongoing support are priced separately where needed.
From a defined use case to a tested implementation
Define the pilot
Together we choose one use case, review access and data, and agree what a useful result looks like.
Build and evaluate
We implement the agreed scope and test normal cases, exceptions and human handover with your team.
Handover and operation
You receive the agreed documentation and access. Monitoring, maintenance and future changes are defined before launch.
Discuss the implementation directly with the founder
Smooth Flow Tech is based in Horn, Austria, and works with businesses across the DACH region. We discuss your existing software, internal responsibilities and requirements before proposing an implementation.
Meet Smooth Flow TechQuestions before you start
Must private AI run on our own servers?
No. Depending on your requirements, an on-premises installation, private cloud or suitably configured managed service may be appropriate. We compare the data flows and operational responsibilities.
Will our data be used for model training?
That depends on the chosen model, provider contracts and configuration. We review and document these conditions; self-hosting also requires checking connected services and telemetry.
What ongoing costs should we expect?
Infrastructure, model or software licences, usage, monitoring and maintenance can all contribute. A meaningful estimate needs the expected workload, response requirements and operating model.
Explore related AI solutions
Start with one concrete use case.
Tell us what your team does today, where it gets stuck and which systems are involved. We will discuss whether AI is a useful next step.
Discuss your AI project