In the PROWERB Lab, we research and develop in-house AI and LLM applications. GPU infrastructure, machine learning and integrations come together where they make information useful and support work in a meaningful way.
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Model choice for an LLM application includes task quality, data processing, cost and integration options. Assess the provider for the agreed use.
Language models considered for a project
Language models considered for a project
AI creates value only when data, responsibility and application come together. That is why we do not develop in isolation, but around a concrete operational question: what should become easier to understand, more reliable to decide or less manual?
A team wants to prepare recurring specialist questions faster. In this example, a language model drafts an answer using approved information. The person responsible checks sources, statements and next steps. Choose the model, data access and permitted actions to suit the application.
Define the benefit, permitted sources and excluded actions. A research draft and a binding response are different tasks.
Test typical questions, incomplete inputs and unanswerable cases. Assess correctness, clarity and handling of uncertainty.
Agree review, feedback and monitoring with the domain team. Then define the appropriate scope of use.
The Lab connects research and implementation in one technical environment.
Every AI initiative needs a clear goal and testable boundaries.
We determine the decision or task the application should support.
Data quality, protection needs and suitable model approaches are assessed together.
A prototype is tested for usefulness and reliability against concrete criteria.
Only integration into roles and processes makes the model a useful tool.
On in-house AI and LLM applications, GPU infrastructure, machine-learning processes and their useful integration into operational workflows.
A clearly described task and a way to measure what a better result looks like.
We regularly prepare research findings and experience from our work for professional exchange and specialist contributions.
Briefing example: a service enquiry and its approved order data are the input. The model proposes a category and a draft reply. A responsible person checks the data and wording and approves sending. Describe your input, expected result and the decision that should remain with a person.
