AI for Document Workflows: Data, Control and Compliance
Classifying invoices, extracting fields from contracts and routing documents are good uses for AI only when the documents remain traceable and the data does not end up where it should not.
Where the Data Goes
Every external model comes with terms you cannot fully control. That is why we host models in the UK or run them inside the client's infrastructure, with verifiable sources and human approval for critical steps. Setup costs more; a data incident costs much more.
Navigating Compliance: AI and GDPR
GDPR compliance is an architectural constraint. Before development, we define where documents are processed, what is logged, how long data is retained and which actions require human approval. Model choice alone does not make a workflow compliant.
For classifying invoices, extracting contract fields and routing documents, a well-configured local model may be more useful than a more capable model the data cannot reach. The choice depends on the task, the data and the required level of control.
Related Services
For document workflows, controlled automation and AI systems with audit trails, see how we approach AI development in Turin. If documents, agents and processes need to talk to ERP, CRM or existing management systems, the critical layer becomes API, MCP and CLI integrations. When the internal operating system also needs to be redesigned, the work may require custom business software.