Trade-secret drafts, patent literature, and regulatory submissions can't live in a consumer chat history. PrivateDocs AI keeps experiment logs, internal memos, and filings in a local vault so reviewers can ask grounded questions without exposing proprietary prose to a vendor cloud.
Alternatives replaced: ChatGPT Enterprise · Claude Projects · AnythingLLM · Glean · AWS Bedrock Knowledge Bases
Query experiment logs, regulatory submissions, and internal memos without publishing trade-secret drafts to a hosted cloud assistant.
Add experiment logs, internal memos, patent PDFs, and supported files to the vault. Advanced Parsing can OCR image-only PDFs; verify results for figures and scans.
Documents are embedded and stored in a local ChromaDB vault. Proprietary prose, claims, and figures never touch a vendor cloud.
Cross-reference findings from the latest trial with existing patent literature, or trace a claim from lab notebook excerpt to filing language.
New reviewers get up to speed by asking questions over long, dense technical PDFs — with cited passages instead of a 600-page reading list.
Inference, embeddings, and vector search run on the user's machine. No vendor data path to audit.
Every response links back to the exact source paragraph or row, so verification never means re-reading the binder.
Retrieval pulls only the relevant chunks per query — your index can be terabytes, not pages, with no per-token billing.
Hosted RAG platforms index your documents in a vendor cloud and route every query through hosted embedding and LLM endpoints. For trade-secret drafts, unpublished patent literature, and regulatory submissions, that egress is a non-starter. PrivateDocs AI runs the entire pipeline on the researcher's machine.
Scanned PDFs can use OCR with Advanced Parsing. Handwriting recognition and OCR quality are not guaranteed, so review results against the original notebook.
Every answer links back to the source paragraph in the underlying notebook, memo, or filing. Reviewers can verify a claim, prior-art reference, or experimental result against the original document without re-reading the whole binder.
Retrieval pulls relevant chunks per query, but practical corpus size and performance depend on local storage, memory, and model capacity. Test the intended portfolio before rollout.
$249 one-time license. No recurring fees. Document content stays local for AI processing.
Chat with supported data-room files — NDAs, purchase agreements, and disclosure schedules — without sending document content to a third-party AI cloud.
Layer 10-Ks, earnings commentary, and CSV data extracts into one analytical surface — with Chinese walls intact.
Summarize longitudinal notes, policy manuals, and reference PDFs where PHI must stay inside approved systems — not in a consumer chat product.
Drop the client's data room, working files, and prior deliverables into a local vault — and ask cited questions without breaching the engagement letter.