Applied AI, LLM and Data Engineering

We design AI components as parts of operational systems. A typical implementation receives input from email, an API, a document queue or a business application, applies classification or language-model processing, then forwards structured results to CRM, ERP, case-management or employee review workflows. The AI component is only one part of the architecture.

LLM applications can support request interpretation, document extraction, internal assistants and controlled response generation. Machine-learning components can be used for classification, anomaly detection, forecasting or predictive-maintenance scenarios. Evaluation criteria, confidence thresholds and escalation paths are defined for the business process instead of relying on model output alone.

Where external AI processing is not appropriate, we can design on-premise or controlled private environments. This can be relevant for banking, industrial data, internal documents or organizations with strict security requirements. Architecture work covers model hosting, retrieval components, access control, logging, prompt management, integration APIs and operational monitoring.

Human review is kept in the process where decisions require accountability or where model confidence is insufficient. Green, review and escalation states can be represented explicitly so that AI processing does not hide operational risk from employees.

AI projects often depend more on data and system access than on model selection. We therefore identify source systems, data ownership, document formats, data quality and transfer restrictions early. Integration work can include CRM, ERP, mail systems, enterprise APIs and internal databases, with traceable interfaces between source data, model processing and downstream actions.