Put your knowledge to work.
An AI appliance combines computing capacity with an application for your business. We turn it into a tool that makes internal information accessible or prepares recurring document tasks. The task determines the technology, and your team stays in control of the results.
Start with a specific task.
An illustration of how the systems work together.
Many businesses already have the knowledge they need. It is spread across manuals, project folders and departmental documents. We look at how your team uses this information today and where an AI application could usefully support their work. That gives us a clearly defined first use case.
Before a larger investment, we test that use case with representative documents and questions from your everyday work. Together, we define what makes an answer useful and when manual processing is still required. You can see early on whether the application helps and what introducing it is likely to involve.
Find information and verify answers
For internal knowledge search, we connect approved sources to the application. Answers reference the underlying documents so that staff can read the source and assess each statement. We also account for how updated versions are added and what happens to outdated or withdrawn content.
In document workflows, the application can classify content, extract information or prepare a draft. Decisions that require professional judgement remain subject to an agreed review. We design that handover so your team can see the original information and correct uncertain results before they enter the next step.
Make deliberate choices about data access
Local models can run on your own hardware. The architecture defines where data is processed and whether external services may be called. This includes integration with existing user accounts and access rules, which must also be respected when searching across your internal documents.
Running locally does not replace careful security work. We therefore plan administrative access, logging and the handling of stored queries from the outset. Together, we determine what information the application needs, who may see results and how permission changes are reflected in the search itself.
Work out operating costs before buying
Suitable hardware depends on the model, the volume of documents and expected concurrent use. We also consider response times and peak demand. A cost-efficient solution needs to match actual requirements. Beyond the purchase price, the assessment covers integration, energy and support, as well as any external usage costs.
Handover includes documented configuration and an agreed operating process. We establish how models and data collections are updated and how backups work. Our support is available around the clock with short response times. Changes are checked against known test cases so that quality remains assessable after an update.
Example
Preparing a technical response
The service team searches several manuals before each response. Product versions differ, and finding the relevant passage takes time. New staff also depend on experienced colleagues to help them locate the right information.
An internal application searches approved documents for the selected product version and gathers relevant references. The team checks each passage in the original source and decides which information to pass on to the person making the enquiry.
Search time can decrease while the source of each statement remains traceable. A pilot using real enquiries can show whether the time saved and the quality of results justify implementation and ongoing operating costs.
Before we begin.
Do we need to train our own model?
Not necessarily. For many tasks, an existing model can be connected to your approved documents. Testing the use case will show whether further adaptation is useful. We assess that effort separately from hardware and integration.
Can the application run without external AI services?
Yes, a solution that processes everything locally is possible. We first check whether it can deliver the required task with sufficient quality and speed. External calls are only included where they are an explicit part of the agreed architecture.
How do we handle incorrect answers?
A source reference alone does not make an answer correct. We test typical and difficult cases, provide access to original sources and define which results need expert review before use. Feedback helps us improve the application where it matters.
What should your AI help with?
Tell us about a recurring task and the information your team needs to complete it. Together, we can work out whether your own AI appliance makes sense.