What data may the AI use?
We establish which documents and records the application may use, whether they may be sent to external services and who can access the results.
A knowledge search needs access to approved documents. A customer-portal assistant may also need current records from the CRM or ERP. We design these connections with your IT team and enforce access rules in the application.
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Test the value with a prototype
Where would AI genuinely help? Is the right data available? What risks need managing? And what can actually be integrated with the systems already in use? Those are the questions worth settling first.
Together, we choose a workflow to test in a prototype. Your team reviews the results using its own cases: how much work is still needed to correct or complete the results, and what goes wrong? We use that evidence to assess development effort, operating costs and legal requirements, including the EU AI Act where applicable.
Open source and locally hosted models
For locally hosted models, we assess licensing, hardware requirements and task-specific performance. Open interfaces make it easier to change models and services. We compare local deployment with an external API using your data, response-time requirements and budget.
Search manuals, product information and policies
A product specification is in a data sheet, the instructions in a manual and an exception in an internal policy. Answering a customer’s question often means searching several documents.
AI-assisted search can retrieve relevant passages from these sources and draft answers with links to the originals. We also check for outdated or conflicting documents and identify material restricted to particular user groups.
Classify enquiries and prepare follow-up actions
Email and free-text enquiries often contain several requests and incomplete details. An AI feature can organise the information and prepare a draft for further processing.
We integrate these features into the existing application. Staff can see the original enquiry alongside the suggestion and correct or add information.
An assistant could identify missing details in an enquiry, retrieve relevant product data and prepare a case for the responsible team. For each workflow, we define which actions it may take and which require confirmation.
Access controls, approvals and failure handling
Permissions are enforced in software. Instructions to a language model are not sufficient to prevent unauthorised data access or actions.
Changes to records and other consequential actions follow the agreed approval process. When information is missing, sources conflict or a service fails, the application needs to show the problem and allow the task to be handled manually.
Changing models and exporting data
Changing the model can affect answer quality, response times and cost. We document which parts of the application depend on a provider and how documents, configuration and results can be exported.
A provider change calls for fresh checks of integrations and output quality. We compare on-premises systems, cloud services and combinations of the two, including suitable European providers.
How we build software now
Prototypes and model tests on our own hardware.
AI also changes the work of building software: research, prototypes, implementation, testing and documentation.
We use it where it improves the work without compromising security, maintainability or architecture. Our own hardware, including an NVIDIA DGX Spark, gives us a separate environment for prototypes and controlled experiments. That lets us explore capabilities without involving live client systems or unnecessarily sending sensitive data to external services.
More time for prototypes and user testing
When AI reduces repetitive coding work, we can put working prototypes in front of teams earlier. They can try a workflow before committing to its full implementation.
Bring a recurring task and examples of the documents involved. We can discuss what is worth prototyping and which data and integrations it would require.