AI engineering for business applications | Anexia

For GenAI integration, we first break the desired workflow down into input, decision and effect. A text summary, a domain recommendation and a binding booking need different controls. For a summary, completeness and reference to sources are the priority. A recommendation additionally needs a traceable basis for the decision. A booking requires clearly limited rights, validated parameters and secured execution outside the model. This distinction determines the actual development scope.

A suitable starting point for AI development includes recurring tasks, enough usable examples and a domain owner responsible for assessment. If these prerequisites are missing, the engagement begins by clarifying them. We also examine whether search functions, rules or conventional automation already cover the need. In AI engineering, options are not included as an abstract technology discussion but as comparable paths to the same work result. The selection takes processing effort, the consequences of errors and integration into account. This allows the decision team to understand why a model function is planned and at which points the system deliberately remains deterministic. For AI engineering, this results in a well-founded definition of functions.

Scope of services for AI engineering

The agreed scope for GenAI integration can include the following results: a use case map, a data flow model, an evaluation dataset, integration interfaces and an operations manual. For each significant result, we define what it is for, who reviews it and in what form it is handed over. This enables development, operations and procurement to assess the same service. Vague umbrella terms are replaced in the offer by specific work packages.

AI development also includes coordinating the necessary interfaces with your team. We clarify access, contact persons and available testing options before the affected implementation steps. Changes to the scope are described with their impact on effort and sequence. This gives you a traceable overview of delivered results, outstanding prerequisites and additional requests.

The key technical decision in AI engineering

The choice between a model API, running your own model and a conventional rule-based implementation follows the data requirements and the actual workflow. For AI engineering, we document the relevant assumptions and justify the chosen option. Significant alternatives are considered against the same functional requirements. This makes later adjustments easier when load, data stock or organisational responsibility change.

A sound decision also takes maintainability and handover into account. For GenAI integration, we examine which skills are available internally and what additional support is needed. This connects technical possibilities with a realistic operating model. The client can see which tasks will arise regularly after the project and which decisions remain with them.

A linguistically convincing answer can be factually wrong and, through downstream actions, cause greater damage than a request that visibly fails. In AI development, we treat this point as a specific review question. A risk is linked to the affected workflow, its possible impact and an appropriate measure. Where prerequisites are still missing, the statement explicitly remains open until the necessary investigation has taken place.

Domain fulfilment rate, share of correctly declined answers, response latency and the effort of human review are evaluated together. For AI engineering, we record the starting situation, the tested configuration and the result. Acceptance relates to the agreed scope. Open limitations are documented visibly and assigned to a next step with a clear owner. This allows your team to distinguish between a proven function, a deliberate decision and an assumption that is not yet confirmed.

Project example as an illustrative scenario

The following scenario is an example of use and not a customer reference. A technical service provider wants to pre-sort incoming fault reports and suggest suitable knowledge articles. In AI engineering, the first step would be to clarify which decisions an assistant may prepare. Classifying a safety-relevant fault and approving a customer reply would remain with the responsible team. Historical reports could form a test set, provided their use and the data they contain are approved for this purpose.

For AI engineering, the project team would examine frequent report types, ambiguous descriptions and previously unknown failure patterns separately. A suggestion would have to make clear which information it is based on. If the system found no suitable basis, a visible handover to the specialist team would be provided. In the pilot, processing time including review, domain corrections and the frequency of such handovers would be recorded. The technical integration would also have to prevent duplicate tickets and accidentally triggered notifications.

The decision on GenAI integration would therefore be based on the entire processing workflow. A text generated more quickly would not in itself be a success. For an extension, those responsible would need to see which reports are reliably supported, which exceptions continue to be handled manually and what support effort arises. An approved integration concept, documented test cases and clear intervention rights would be the intended results of this scenario.

Our experience and our engineering approach

We combine custom software development with the technical tasks of introduction and further development. For AI engineering, we bring the perspectives of application, integration and operations together in a shared workflow. The business requirements remain connected with the technical decisions. You work with a defined scope of services and traceable results.

Our experience should become specifically verifiable for your project. That is why, in a technical discussion, we explain how we examine the relevant risks, which work results are planned and how your team can assess them. For GenAI integration, we align the roles involved with the actual need. Available references are placed in context with the respective project and company relationship. Confidential customer details are not used as general advertising claims.

Documented product reference

The published framework agreement for maas.maker names Anexia Digital Engineering GmbH as the operator of the SaaS platform. We explain the specific relevance to your project in a technical conversation.

Effort for GenAI integration

Major effort drivers are the number and quality of data sources, necessary domain reviews, the scope of integration and the expected request profile. We therefore calculate AI engineering based on a defined scope and explicitly stated prerequisites. An initial analysis step can be useful if key technical information is still missing. Work packages and acceptance criteria can then be agreed on a more reliable basis.

AI engineering can be organised as a limited project, as a joint implementation with your team or with subsequent support. Rights of use, access, documentation and responsibilities are clarified in the contract. Ongoing operating hours, response times and external consumption costs are separate agreements. This keeps it clear which services are provided once and which tasks arise regularly after the introduction.

Scope boundaries of AI engineering

This page covers complete implementation. The pure selection and prioritisation of use cases is explored in more depth in the AI workshop. For AI development, we already state this boundary in the offer. Required additional services are included with their business justification and are not silently assumed. This allows your team to plan suitable internal skills and divide the overall work sensibly between the parties involved.