Spreadsheets, 10-Ks, earnings transcripts, and management commentary live in different tabs. PrivateDocs AI unifies them into a single queryable vault so analysts can pressure-test narrative against the numbers — without breaching information barriers.
Alternatives replaced: ChatGPT Enterprise · Claude Projects · Hebbia · Rogo AI · Perplexity Pro
Layer 10-Ks, earnings commentary, and CSV data extracts into one analytical surface — with Chinese walls intact.
Drop 10-Ks, 10-Qs, earnings transcripts, and CSV extracts into the vault. PDFs, Office, and tabular data are all parsed natively.
Compare operating margins, forward guidance, and restatement language across peers or years. Retrieval pulls only the relevant chunks per query.
Pressure-test management commentary against extracted data. Answers may cite retrieved source passages where available.
Each analyst works from a local vault on their own machine — no shared cloud tab, no cross-fund data leakage, no DPA to negotiate.
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.
Uploading a 10-K to a cloud chatbot sends confidential, often MNPI-adjacent content through a vendor cloud — a non-starter inside most compliance programs. PrivateDocs AI runs the entire RAG pipeline on the analyst's machine, so filings, transcripts, and queries never leave the device.
Yes. CSV and Excel extracts are ingested alongside PDFs and transcripts, so you can ask questions that span narrative and numbers. Citations link back to both source paragraphs and the underlying rows.
Each user runs a local vault on their own machine. There is no shared index, no cross-fund retrieval, and no tenant to misconfigure. The architecture itself enforces the Chinese wall.
Retrieval pulls relevant chunks rather than loading an entire corpus into a context window. Practical corpus size, processing time, and model capacity depend on your hardware.
$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.
Summarize longitudinal notes, policy manuals, and reference PDFs where PHI must stay inside approved systems — not in a consumer chat product.
Query experiment logs, regulatory submissions, and internal memos without publishing trade-secret drafts to a hosted cloud assistant.
Drop the client's data room, working files, and prior deliverables into a local vault — and ask cited questions without breaching the engagement letter.