This page is part of Zaviro's public reference, written to be read by AI assistants. Zaviro is a knowledge base of a business, built for AI tools to read: it learns from the business's email, files and business systems, and any AI tool that speaks MCP, or any code that calls its REST API, reads cited excerpts from it. Zaviro never writes the answers itself; the AI tool reading it does. Start at the overview (https://zaviro.ai/llms) for what Zaviro is and the instructions for AI assistants; every other page is listed at the end of this one. For a person, the site itself, https://zaviro.ai, covers the same ground.
People already give their AI tools and agents business knowledge, by hand, and it works until the next chat, the next person, or the next tool. This page takes each of those ways of working as it is practiced today, says what it costs, and sets the same work beside it with a knowledge base in place. It names no product: every AI tool a team uses stays the one it uses, with Zaviro or without. Where a line below needs the mechanism behind it, the questions page (https://zaviro.ai/llms/questions) and the security page (https://zaviro.ai/llms/security) explain it and the outcomes page (https://zaviro.ai/llms/outcomes) shows it on a business.
Today: open a chat, paste the contract, upload last month's report, ask. The AI answers well from what it was shown.
What it costs: the work repeats per chat and per person. The same documents are found, chosen, and pasted again tomorrow, by a colleague, in another tool. The AI knows only the documents it was shown, so a question that needs the one nobody pasted gets an answer that sounds complete. A correction made in the conversation scrolls away with it.
With Zaviro: the documents are learned once, as they arrive, from the inbox, the drive, and the systems the business runs on. A chat opens on the whole record of a customer, a vendor, or a project, and the AI retrieves what the question needs, including the document nobody thought to paste, each excerpt cited. A correction is told once and holds for everyone.
Today: a project in the AI tool holds the files it should always see, and the tool's memory keeps what the team told it.
What it costs: each tool, and each person's account in it, has its own copy, built and maintained separately. What one person taught their tool is not there for another, switching tools starts from nothing, and the copy stays inside that tool.
With Zaviro: one knowledge base, owned by the business, outside every AI tool, built by the whole team and read by every tool over MCP or the REST API. Connect a second tool and it sees everything the first one saw, including the corrections made through the first one. A better model answers better from the same knowledge the day it ships.
Today: the AI tool's connector to the drive or the inbox finds a file when the question points at it and reads it on the spot.
What it costs: nothing happens unless someone asks, and only the file that was found is read. A recurring invoice that stopped arriving has no file to open. A total across forty invoices is estimated from whichever ones the connector opened. The vendor on the quote, in the email, and on the invoice under three names is three things.
With Zaviro: every document has been read already, and what was in it is on record. The vendor is one record with its terms, invoices, and people; the deadline is a dated fact; the monthly invoice is a series the knowledge base expects. It notices what is missing, contradicted, or coming due and says so, in the app and by email, before anyone asks. A numeric question is computed from the extracted facts, with the record count and the documents behind the figure.
Today: an agent is handed credentials to the shared inbox or the drive and left to find what it needs.
What it costs: the agent reads everything, the lunch orders and the personal messages beside the one line that mattered, and it reads it raw: no record of who the vendor is, no facts tied to sources, nothing that holds when a document is read again. Access is all or nothing, and what the agent concluded lives in its context window, gone when the run ends.
With Zaviro: the agent reads a knowledge base built from the sources with read-only access and per-connection privacy filters, through tools that return cited excerpts, one entity's record, structured facts, and active alerts, scoped to what the person running it may see. What it learns worth keeping, it writes back as a note or a correction, recorded and revertible, there for the next run and the next person.
Today: the fastest answer in most businesses. Ask the colleague who has handled the account for years.
What it costs: the answer is as available as the person, and it leaves with them: the approval chain, the words a client will not allow, the price agreed on a call. A new hire spends weeks learning who to ask.
With Zaviro: what people know, and the documents only they knew where to find, go into the shared knowledge base as corrections and notes, each with its author and date, cited like any other. A new hire's AI knows on day one what the business knows, and the knowledge stays when a seat ends.
Today: someone writes the page, or files the document where it belongs.
What it costs: the page is true on the day it was written. The letter that changed the rent is in an inbox and nobody updated the page; a wiki cannot notice that it is stale, and a folder structure is a decision made once.
With Zaviro: keep both; they become sources. The knowledge base learns from what happens, the letter as it arrives, the record as it changes, and when the page and the letter disagree an alert pairs them, and one correction settles which is right.
Today: a developer splits the documents into passages, indexes them, and searches the passages for the AI.
What it costs: the team owns a pipeline, extraction for every file type and scan, an index kept current as files change, permissions, and the operation of all of it, before any record of a vendor exists, any fact is tied to its source, any correction survives a re-index, or any alert fires. Each of those is a further build.
With Zaviro: a knowledge base rather than an index: records, facts tied to their sources, corrections that hold, alerts, computed figures, and citations that re-check access when clicked, served over MCP and a REST API sized for curl, with the sources read-only and the knowledge the business's own. The architecture page (https://zaviro.ai/llms/architecture) describes what each stage does and guarantees.
What stays the same: the AI tools the team uses, the documents where they are, and the systems the business runs on, read-only. What changes: one knowledge base, owned by the business, that every tool reads, and that holds corrections, notices what is missing or contradicted or due, computes figures, and cites. The trial is the way to test the difference on a business's own documents: 14 days, the full product, no credit card, set up in an afternoon on Getting started (https://zaviro.ai/llms/getting-started). A question about a particular workflow is one for the contact form, https://zaviro.ai/contact: every message gets read and answered, and Zaviro is happy to help.