Financial-filings workflows depend on more than extracting reported values: each number must be placed in the document context that makes it reviewable, comparable, and auditable. In practice, this logical structure—including the relevant section, note, heading, row, column, period, currency, and surrounding text—is often clear to professional reviewers but only partially encoded in inline XBRL markup. As a result, rule-based parsers can work on familiar layouts yet fail silently when document structure departs from expected templates. We present FINSTRUCT, an on-premise LLM system designed for confidential financial-document workflows. FINSTRUCT runs locally on a single 16 GB GPU, requires no task-specific fine-tuning, predicts nine structural fields for each reported fact, and provides reviewer-facing rationales together with repeated-sample agreement signals for human-in-the-loop review. On 1,493 manually annotated facts from five UK FRS 102 filings produced by a single accounts-production stack, FINSTRUCT raises macro accuracy from 50.6% to 74.8% and micro accuracy from 63.1% to 85.1% over a deterministic parser. More importantly for deployment, it recovers structure at 83.5% accuracy on a filing where the rule-based parser silently misses whole sections, and its agreement signal separates more reliable predictions from cases requiring review. These results position logical structure recovery as a practical document-understanding layer for regulated financial automation, rather than a conventional parsing problem alone.