The knowledge-silo problem nobody fixed
Every enterprise has the same hidden warehouse: thousands of PDF contracts in legal, scanned HR personnel files in a shared drive, lab notebooks in R&D, vendor invoices in finance, equipment manuals in operations, board-meeting policies in compliance. The volume is staggering, the structure is zero. Searching across these documents is a project everyone files under "someday."
Classic enterprise content management (ECM) tools, SharePoint, OpenText, Documentum, were built to store files, not read them. They give you folders, ACLs, and full-text search on filenames. They cannot tell you which of 4,000 contracts has an MFN clause. They cannot summarize a 200-page MSA. They cannot answer "what equipment did we buy from Vendor X in 2023 and at what serial numbers?" without three analysts and a week.
What intelligent document processing actually solves
Intelligent document processing (IDP), document AI, document intelligence platform, the labels overlap. The core idea is the same: combine OCR, layout parsing, semantic indexing, and retrieval-augmented generation (RAG) to turn unstructured documents into structured, queryable, citable knowledge.
- OCR turns every page (including handwritten R&D notes and scanned contracts) into machine-readable text.
- Layout parsing preserves tables, headings, signatures, stamps, and figures so structure survives.
- Hybrid search blends BM25 (full-text) with vector embeddings (semantic) so "termination clause" and "early exit terms" return the same hits.
- RAG agents sit on top of all of it and answer specific questions with page-level citations.
How Aarkiv ships this for enterprise teams
Aarkiv is the document operating system for enterprise teams. Three production modules today, all running on one private engine:
Private Cloud
Upload millions of documents. Aarkiv parses every page, builds a hybrid full-text + vector index, and gives your team one source of truth with page-level citations. Folders, ACLs, OCR, search, and per-document chat in one screen.
Darvi, the document agent
A context-aware RAG agent that plans, retrieves, cites, and answers across one document, a folder, or the whole corpus. Private workspace, never trains on your data. Multi-modal across PDFs, images, and handwritten pages.
Invoice extraction
Drop in PDF invoices, receipts, or a ZIP of bills. Define the fields you want (vendor, amount, dates, anything) and get a clean Excel sheet. Works on born-digital and scanned PDFs alike.
Use cases by department
Every team has the same fundamental problem (documents nobody can search) wearing a different costume. A short tour of who uses Aarkiv:
- Legal: contract analysis, MFN clause review, NDA discovery, e-discovery dumps, patent portfolios.
- HR: personnel files, offer letters, severance policies, benefits filings, employee handbooks.
- R&D: electronic lab notebooks (ELN), handwritten experimental notes, SOPs, technical reports, peer-reviewed papers.
- Finance & AP: invoices, purchase orders, receipts, expense reports, AP automation pipelines.
- Compliance: audit binders, certification packs, regulatory filings, ISO 27001 and SOC 2 evidence.
- Operations: equipment manuals, maintenance logs, vendor specs, safety procedures.
- Customer success: support email archives, contract amendments, custom-build specs.
Private by default
Three security postures matter: data residency, training boundary, and blast radius. Aarkiv runs in your environment (cloud, on-premise, or VPC), never sends your documents to a public LLM for training, and isolates every file on disk with chmod 0600, hardened ZIP extraction, single-session auth, and ZIP-bomb / path-traversal / symlink defenses by default.
Getting started
The fastest path is also the cheapest: sign in with Google, drop in your worst PDF, and ask Darvi a real question. Every new account gets ten free pages, no card required. When you are ready to scale to thousands or millions of documents, our team configures a private deployment in your VPC.
FAQ
What is enterprise document intelligence?
Turning unstructured business documents (PDFs, contracts, HR files, lab notebooks, scans) into a structured, searchable, queryable knowledge base by combining OCR, layout parsing, hybrid search, and RAG agents.
How is this different from a regular DMS?
A DMS stores files. A document intelligence platform reads them, indexes them semantically, and adds an agent that answers questions with page-level citations.
Is Aarkiv private?
Yes. Runs in your environment, never trains on your documents, hardened isolation by default.
Which file types?
PDF (born-digital and scanned), PNG, JPG, WEBP, Office docs, ZIPs of any of the above.
How fast can I evaluate it?
About 30 seconds. Sign in with Google, drop in a PDF, ask Darvi a question.