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Knowledge and Document AI
Upmark Labs builds AI for teams whose knowledge is spread across documents and people, and for teams that process documents by hand: assistants that answer from your own sources with citations, and workflows that sort, extract and route incoming documents. Each build has a written scope and a fixed price. Order from the Store, or talk to an expert.
What problems does Knowledge and Document AI solve?
Most teams already hold the answers they need. Those answers sit in drives, wikis, policy files, chat threads and the memory of a few colleagues, so the same questions come up again and someone stops their own work to answer them.
The other half of the problem is paperwork. Documents arrive by email and upload, and a person opens each one, decides what it is, types the details into another system and files it.
- Staff cannot find the current policy, process or answer, or cannot tell which version to trust.
- Decisions and lessons from meetings and threads are lost once the conversation ends.
- Nobody knows which questions the documentation cannot answer.
- Incoming documents are classified, keyed in and filed by hand.
- Key terms, dates and obligations are buried across a contract library.
- Long reports and complex PDFs must be read in full before anyone can act on them.
What does Upmark Labs build in this area?
Knowledge builds answer questions from the sources you connect, cite where each answer came from, and respect permissions so each user only sees what they may. Document builds read incoming documents, extract the details into structured fields with confidence scores, send low-confidence items to a review queue and write the results to your system of record.
Several knowledge builds also come as editions connected natively to Google Workspace, Microsoft 365, Notion, Confluence or Slack. Industry editions adapt AI Company Knowledge Hub and AI Document Extraction for sectors such as legal operations, financial services, logistics and construction.
- AI Knowledge Agent answers staff questions from your documents, with citations to the source.
- AI Company Knowledge Hub brings company documents, wikis and drives into one cited question-and-answer assistant.
- AI Policy Q&A answers employee questions about policies with the exact source clause.
- AI Knowledge Gap Finder finds the questions your documentation cannot answer yet.
- AI Document Extraction pulls structured data out of documents and into your systems.
- AI Contract Intelligence extracts key terms, dates and obligations across your contract library.
How does a project run?
Every product has its scope written down before you buy: the interface or intake route, the sources or systems connected, the testing, the rounds of changes, the handoff and the support period. The price and a fixed delivery time are shown on each product, and you can order online or talk to an expert first.
Running costs sit in your own accounts. For knowledge builds that is mostly AI model usage, a vector database and hosting. For document builds it is mostly OCR and AI usage plus storage, which grow with page volume.
- Intake: you share access to your sources or systems, sample questions or documents, your rules and a named decision-maker for approvals.
- Build: we configure the workflow, prompts and guardrails, and connect the agreed systems.
- Test: we run the agreed test scenarios and send you the test report before launch.
- Launch: the build goes live in your environment, with an admin guide and a recorded handoff session.
- Support: post-launch support follows, then an optional monthly maintenance plan covers monitoring, fixes and changes.
How do you choose the right build?
Start with what your team does today. If people ask questions and need answers they can check, choose a knowledge build. AI Document Q&A and AI Policy Q&A suit a defined set of documents in a single channel, such as a website widget, Slack or Microsoft Teams. AI Knowledge Agent and AI Company Knowledge Hub suit a larger body of content across your drives and wikis, with permission groups.
If people search rather than ask, AI Internal Search ranks results across your internal tools, and AI Document Search finds documents in an archive by meaning, not just keywords. If the work is handling documents, pick the build for the step that takes your team the most time: intake, classification, extraction, validation, comparison, routing, approval or archiving.
If your need sits outside a product's scope, add a custom requirements note at checkout and you receive a written Change Quote before any extra work starts.
Knowledge and Document AI in the Store
AI Knowledge Base Builder
Turns scattered documents into a structured, maintained knowledge base.
Browse the Store by category: Knowledge & RAG Document Operations
Questions buyers ask
How do I know an answer from a knowledge build is right?
Every answer cites the source it came from, so the reader can open the document and check it. Before launch, we test the build against sample questions you supply with the answers you expect, and you receive a test report. Answers are drawn from the content sources you connect, and a content refresh schedule keeps them current as those sources change.
Can people see answers from documents they are not allowed to open?
Knowledge builds are permission-aware, so users only see what they may. At intake you give us your user list, your permission groups and your access rules, and we set up sign-in with your workspace admin. If your access model needs more permission groups than the scope includes, add a custom requirements note at checkout and we quote the extra work before it starts.
Which tools do knowledge and document builds connect to?
Knowledge builds typically connect to Google Drive, SharePoint, OneDrive, Notion, Confluence, Slack, PDFs and websites, and answer through a web app, Slack or Microsoft Teams. Document builds typically work with email inboxes, upload folders, Google Drive, SharePoint, Dropbox, Google Sheets, Airtable and your ERP or CRM. Editions connect natively to Google Workspace, Microsoft 365, Notion, Confluence or Slack. Each product page states which connections its scope covers.
What happens to a document the AI is not sure about?
It goes to a person. Document builds extract details into structured fields with a confidence score, and low-confidence items are routed to a review queue instead of going straight into your system of record. You name the person who reviews them, and you supply the validation rules the build checks against. Final decisions stay with your team.
What do we need to provide, and when?
For a knowledge build: access to your content sources, your user list and permission groups, and sample questions with the answers you expect. For a document build: real sample documents of each type, redacted if needed, the fields to extract, your validation rules and access to the destination system. Both need a named decision-maker. Send everything soon after ordering, because late intake moves the delivery date.
What does it cost to run a build after launch?
The build price is fixed, and running costs are paid in your own accounts, not through us. Knowledge builds mostly incur AI model usage, a vector database and hosting. Document builds mostly incur OCR and AI usage plus storage, which grow with page volume. AI usage during build and testing is also billed to your accounts. Each product page shows its typical running cost estimate.
Not sure whether you need answers or document processing?
Tell an expert what your team keeps asking and what it still types in by hand, and we will point you to the build that fits or scope a custom one.