PDF extraction

Many PDFs already contain a text layer from the authoring app. Local extraction pulls that layer so CasperWasp can index and display content. Because this step is free, you can validate readability early — open a known paragraph, confirm names and numbers look right, then proceed to AI-assisted surfaces.

Scanned PDFs without a text layer are a different problem. Extraction cannot invent accurate text from pixels alone without OCR upstream. If chat citations look hallucinated or summaries feel generic, inspect the text layer first. Most “model problems” on PDFs are actually extraction problems.

Healthy text extraction also protects collaboration. When teammates see the same underlying content, disagreements focus on meaning instead of whether someone copied from a corrupted export.

Ask for the classes of facts your process needs: parties, effective dates, term and renewal, termination rights, payment mechanics, liability and indemnity themes, confidentiality, data protection, SLAs, acceptance criteria. The more your prompt mirrors your intake checklist, the more useful the list becomes.

Treat each extracted bullet as a candidate. Open the PDF — or use chat citations — to confirm wording before the bullet becomes a negotiation position or a ticket. Extraction accelerates listing; verification prevents confident mistakes.

Structured highlights shine in handoffs. A junior teammate can extract, a senior can verify the top ten items, and Document Studio can hold the living issues list with owners and statuses. That beats a chat thread nobody can audit next quarter.

A common loop: extract text → summarise for altitude → extract key points for the checklist → chat the ambiguous items with citations → draft the issues memo in Document Studio. Each stage has a job; skipping verification between stages is where teams get hurt.

When extraction returns a dense list, cluster items by owner (legal, security, commercial, ops) before the meeting. The list is raw material for an agenda, not the agenda itself.

If you later translate the PDF for regional reviewers, extract again or translate the verified checklist carefully. Do not assume a translated narrative summary replaces a verified obligation list.

No extractor is omniscient across exotic schedules, poorly labelled exhibits and contradictory amendments. Build a human completeness pass for high-stakes deals: search for defined terms, skim schedules, confirm cross-references the list might have missed.

Ambiguous bullets should be labelled as questions, not facts. “Possible auto-renewal — verify section 8.2” is more honest than a false-precise date. CasperWasp keeps humans owning legal interpretation for exactly these edge cases.

Prefer exporting verified findings into Document Studio with clear owners. Extraction without operational follow-through is just another artefact rotting in a sidebar.