Enterprise forms are layout-specific observations of real-world entities whose semantics span fields, recurring sections and related documents. Developing enterprise document-understanding systems requires large-scale supervision, but obtaining authentic forms is constrained by privacy concerns, annotation costs, and organization-specific formats. We present , a semantic-first framework that uses a lightweight, task-specific ontology to generate enterprise form packets. The framework separates domain semantics from layout through ontology maps, hierarchical schemas, dependency-aware entity sampling, deterministic validation, and template bindings. It produces rendered forms with field boxes, ontology-keyed extraction targets, grounded question-answer pairs, and validation metadata. We instantiate the framework on three digitally rendered surrogate insurance form types, producing 706 forms. Finetuned vision-language models improve ontology-keyed extraction F₁ by 4.99 points and grounded-QA ROUGE-L by 44.1 points over their zero-shot baselines. Ontology guidance further improves held-out-form extraction F₁ by 36.05 points overall and by 42.67 points on concepts absent from training, while increasing exact cross-form reasoning accuracy by 34.17 points. These results demonstrate the utility of the generated supervision signals for downstream tasks