AI-supported document automation

Not only capture documents, but also analyze, check and automatically process their content.

End-to-end digitization thanks to AI-based document automation

Where intelligent document capture ends, automation begins.

Many organizations have already taken the first step – documents are digitally captured, recognized and classified. But the real efficiency gain comes when this information is automatically processed, checked and converted into decisions.

AI-supported document automation allows complex processes to be controlled digitally: Documents are not only recognized, but their content is understood, evaluated and further processed using rule-based or AI-controlled workflows.Artificial intelligence takes on tasks that could previously only be performed by humans – such as checks, reconciliations, approvals or evaluations.

Complete automation of inspection, approval and routine tasks

Automatic enrichment and validation of data

AI agents that prepare context-related decisions or make them themselves

Seamless integration into workflows, specialist procedures and file logics

Audit-proof, traceable process execution

Use cases for AI-supported document automation

Automated application review

Incoming applications are checked automatically - e.g. for completeness, plausibility or eligibility criteria. AI agents recognize exceptions, carry out regular checks and forward processes to specialist processors if necessary.

Intelligent invoice and document processing

After entry, invoices are automatically checked for correctness of form and content. Data comparisons with master data or order information are carried out in real time, and approvals are made automatically according to defined rules.

Contract and document review

AI recognizes critical clauses, contract deviations or deadlines - and marks them for automated review or approval. Workflows transfer the results directly into approval processes.

Automated correspondence and response generation

AI models automatically create and send response letters or notifications based on document content or process contexts - legally compliant and traceable.

Quality assurance and validation

Automated checking mechanisms check whether documents are filed completely, correctly and in accordance with process specifications. Human checking tasks are systematically supported or replaced by AI agents.

Knowledge-based process automation

AI links information from documents, databases and systems, recognizes patterns and derives recommendations for action - e.g. for escalation, prioritization or follow-up actions.

We support you in taking the next step - from
intelligent capture to complete automation of your document processes

Analysis of processes and automation potential

Development of AI agents for testing, approval or classification tasks

Development of workflow and rule models based on BPMN 2.0

Integration in DMS, ERP and specialist processes

Operation, training and continuous optimization

AI-supported document automation with it-novum

We bring together many years of experience in document management, workflow design and AI integration. Our approach: automation with responsibility – comprehensible, explainable and tailored to your specialist logic.

Combination of AI, process knowledge and open source expertise

Automation of tasks that were previously carried out manually

Traceable and audit-proof process execution

Customizable workflow logics with BPMN 2.0

Easing the burden on employees through digital review and approval processes

Scalable architecture for future AI use cases

Drei Open Source Workflow Engines im Vergleich

Welche Engine passt zu Ihrem Anwendungsfall: Activiti, Camunda, Flowable

Stehen Sie vor der Herausforderung, die richtige Technologie für Ihre Prozessautomatisierung zu wählen? Feature-Listen allein helfen hier selten weiter – entscheidend ist Ihr konkreter Anwendungsfall.

Im Webinar am 05. März 2026, 10:00 – 10:45 Uhr

präsentieren wir Ihnen ein praxisnahes Entscheidungsmodell, mit dem Sie:

  • Open-Source-Engines systematisch einordnen,
  • teure Fehlentscheidungen und Architekturfehler vermeiden
  • und die drei Technologien Activiti, Camunda und Flowable fundiert vergleichen können.