# Check a PDF is text-based before sharing it with AI Canonical: https://opendataroom.com/guides/text-pdf-ai-pack Updated: 2026-10-17 Use a PDF with embedded readable text and review the extracted preview. A file that looks legible on screen may still be only a photograph of a page. ## How OpenDataRoom helps Market: Mobile users, Advisers, Founders Use a PDF with embedded readable text and review the extracted preview. Keep source documents organised in drops, like folders, and select named packs for each audience. OpenDataRoom manages access and request records; the recipient uses their own AI for analysis. Current AI tools return extracted text, not complete original files. - [Saved-file selection on your phone](/guides/iphone-files-to-pack): Save an authorised attachment to Files or Downloads and select it in the browser. No access to the original chat or mailbox is required. - [Text preview before sharing](/guides/why-was-my-file-refused): Inspect readable content before approval. Choose a text-bearing source when a scan or unsupported file is refused. - [Named recipients and separate packs](/guides/separate-packs-per-audience): Choose who can connect. Prepare separate packs when people are allowed to see different documents. ### Workflow impact A readable PDF pack with extraction gaps identified. Keep the selected evidence available through one authorised connection instead of assembling another set of attachments for each question. ### Bring these documents - Text-based brief.pdf - Extraction check.txt - Missing appendix note.docx ### Prompt for your own compatible AI Using only this approved pack, use a PDF with embedded readable text and review the extracted preview. Start with this source question: Can the AI answer questions about the scanned appendix? Cite the file and passage. Distinguish supplied facts, assumptions, missing evidence and contrary evidence. Do not infer human reading or investment intent from request records. Check the AI response against the originals, then use the reviewed result in your own meeting, report or handover. ## Frequently asked questions ### What would this change in my workflow? A readable PDF pack with extraction gaps identified. Keep the selected evidence available through one authorised connection instead of assembling another set of attachments for each question. The practical task is to use a PDF with embedded readable text and review the extracted preview. ### Which documents should I include? For the example on this page, start with Text-based brief.pdf, Extraction check.txt, Missing appendix note.docx. Use the approved versions relevant to the task and include source dates, definitions and any known gaps. Review the prepared text and disclosure scope before sharing. The fictional examples illustrate the structure; OpenDataRoom does not supply or verify your underlying research. ### Does OpenDataRoom perform or verify this work for me? Your own compatible AI works from the material you choose. For this task—use a pdf with embedded readable text and review the extracted preview—you or the responsible reviewer must check the answer against the originals. OpenDataRoom provides document access and request records; it does not certify the evidence, perform an independent review or approve the result. ### How does the recipient connect, and who gets access? Choose your own AI or invite the intended email recipient. For a private invitation, the recipient verifies the authorised email and completes the required approval flow, then adds the connection in a compatible AI app’s settings. Forwarding an invitation alone does not authorise a different email. Permission covers the selected pack; create separate packs when audiences may see different documents. ### Can I keep the files and end someone’s access? Your wallet contains source drops, like folders. Each named pack shares selected files or passages from one drop. The source drop and its access packs have separate deadlines. You can keep the source until its displayed retention deadline while ending a recipient’s future access. Choose a sharing window, revoke it earlier when needed, and approve an extension deliberately. Expiry cannot recall material that an AI app or recipient has already retrieved. ### What happens when I update or remove a file? After you review and accept a drop update, fresh authorised requests receive the current versions of files already selected for their packs. This does not force an AI to fetch the files again or refresh its existing chat context. New files do not join those packs automatically. Review changed selected passages explicitly. Removing a file or its permission stops future retrieval, and access-change notices are queued for affected recipients. Provider acceptance is not proof of inbox delivery. This cannot erase content already retrieved. ### What if my files or AI app are not supported? Check the compatibility guide and try the public service-information sample first. An app must support the required MCP connection and authorisation; pasting a URL into ordinary chat does not install it. Use supported text-based files and inspect the preparation report. Originals are retained in encrypted storage, but the current AI tools return extracted text, not complete original files. Charts, images and layout are not faithfully delivered by those tools. Image-only scans and standalone images are refused; no OCR, audio transcription or spreadsheet recalculation is provided. Current pilot limits are listed on the pricing page. ### What information is recorded about use of the pack? The ledger records requests received by the service, authorised connection references, requested source information and preparation outcomes. Recipients can also deliberately submit a request, comment or note after approving the exact text. The owner sees that message, the verified connection identity and time in Recipient inbox for up to 90 days, unless deleted sooner. Received does not mean read or answered; no agent or automatic research is started. It does not record the entire AI conversation. Search text is supplied by the AI and may differ from the human question. An AI can reuse retrieved material without making another request. Literal search can miss relevant material; neither a search nor a file request proves that the AI reviewed the entire pack. Owners can investigate no-match searches before deciding whether to clarify wording, repair extraction or add information. A no-match result alone is not proof that a document is missing. ### Who can read my files in the hosted connection? Private-library file names and contents are encrypted in the browser with your wallet key. A hosted AI connection uses a separate access copy that you approve and that the service can decrypt for authorised retrieval. The receiving AI gets the content it retrieves and applies its own data policies. An optional desktop connector can decrypt locally, but it requires installation and is not a universal mobile connection. Review the security and compatibility pages before choosing a route. ## Fictional example documents These are invented teaching documents, not customer evidence. ### Text-based brief.pdf Fictional PDF contains selectable body text with a stated document date. ### Extraction check.txt Example check confirms the body text is present. A separate scanned appendix contains no embedded text and is excluded. ### Missing appendix note.docx The pack does not include the scanned appendix. No OCR or reconstruction has been performed. ## A source question Can the AI answer questions about the scanned appendix? Illustrative answer: Not from this pack. The appendix is absent and no OCR was performed. Obtain an authorised text-based version if it is needed. Standalone images and PDFs without embedded text are refused. Check tables and reading order even when extraction succeeds; a successful upload does not establish source completeness. ## Sources and further reading ## Related workflows - https://opendataroom.com/guides/zip-data-room-ai-pack - https://opendataroom.com/guides/whatsapp-document-ai-pack Admission is by invitation. Join the waiting list: https://opendataroom.com/signup Check compatibility and the separate public service-information sample: https://opendataroom.com/compatibility ## Try the fictional example https://opendataroom.com/example-demo.html?example=text-pdf-ai-pack