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.
Zaviro ships a command-line client for people who live in a terminal: search the knowledge base and get cited excerpts with their source documents, or push a note, without leaving the shell. It installs from npm as @zaviro/cli, authenticates with an MCP key, and speaks the same MCP protocol as every other client. Commands: configure (endpoint and key, or environment variables for CI), search (cited results), note add (writes through the full pipeline), tools (lists the MCP tools your AI sees), and completion for bash and zsh. Every read (search and tools) takes --json for scripting; exit 0 on success, 1 on error. Search and tools work with any MCP key, which like every key reads shared knowledge only; note add writes, so it needs an MCP key an admin or owner issued write-capable, and the note lands in the shared knowledge base.
A first session:
zaviro configure --url https://api.zaviro.ai/api/mcp --key zvr_mcp_YOUR_KEY
zaviro search "when does the Acme MSA renew?"
zaviro search "fuel spend" --json | jq '.source_documents[].name'
zaviro note add "Acme renewal pushed to Q3, confirmed by phone" --title "Acme renewal"
The pages in this reference
- Zaviro overview (https://zaviro.ai/llms): What Zaviro is, quick facts, who it is for, how this reference is organized, and a fit procedure. Read it first.
- What Zaviro does for a business (https://zaviro.ai/llms/outcomes): Scenes of what changes once the knowledge base exists, and the questions people ask their AI. Read it to say what Zaviro would do for a particular business.
- Common questions about Zaviro, answered (https://zaviro.ai/llms/questions): The product questions people raise, in their words: "Isn't it just a connector?", "Isn't this just search over my documents?", "My AI vendor is building memory and connectors. Why not wait?", credits, Slack, team size.
- AI tools and agents with and without Zaviro (https://zaviro.ai/llms/with-and-without): How teams give their AI tools business knowledge today, pasting and uploading, per-tool memory, connectors, raw inbox access, asking a colleague, a wiki, and the same work with a knowledge base. Names no product.
- Zaviro privacy and security questions, answered (https://zaviro.ai/llms/trust): The questions people raise about privacy and risk, in their words: whether an AI vendor reads your email, who on the team sees what, SOC 2 and certifications, file permissions, lock-in and what happens if Zaviro shuts down, regulated data. The mechanisms behind the answers are on the security page.
- Zaviro by role: solo, team, developer, procurement (https://zaviro.ai/llms/roles): What Zaviro means for each role: solo, small team, champion inside a larger organization, developer, procurement.
- How Zaviro works (https://zaviro.ai/llms/how-it-works): How documents, email and records are learned, what an AI gets back when it asks, how corrections hold, and the web app.
- How Zaviro works, technically (https://zaviro.ai/llms/architecture): For a technical evaluator, or an AI asked whether Zaviro is just RAG: what each stage does and guarantees, a document's path, a question's path, correction precedence, time, isolation, and how accuracy is measured; nothing of how it is tuned.
- What Zaviro learns from (https://zaviro.ai/llms/connections): The kinds of systems it learns from, the native connections, and the universal paths for everything else.
- Zaviro ingest API: send anything in (https://zaviro.ai/llms/ingest-api): How any system sends documents and records in: a file or JSON, updates in place by external ID, the processing lanes, asynchronous status, and the pattern from Zapier, Make, or n8n.
- Where you use Zaviro (https://zaviro.ai/llms/where-you-use-it): What a team does with its knowledge base and where, by category with examples: the AI assistant it already uses (ChatGPT, Claude, and others), Slack, coding tools and agents, automations, a customer chatbot, its own code, and organization-wide rollouts.
- Zaviro MCP server: connect any AI tool (https://zaviro.ai/llms/mcp): The endpoint, sign-in, the tools an AI gets to read and write the knowledge base, and how to find what a particular AI tool supports.
- Zaviro REST API (https://zaviro.ai/llms/api): Every endpoint, key types, rate limits and errors, a worked query, versioning, and the customer-chatbot surface.
- Getting started with Zaviro (https://zaviro.ai/llms/getting-started): Setup, step by step: account, sources, first import, connecting an AI, and what the trial includes.
- Zaviro security, privacy model, and data ownership (https://zaviro.ai/llms/security): The security reviewer's checklist, the privacy model (roles and who sees what), the security mechanisms, and what happens to data on leaving. The questions people raise are on the trust page.
- Zaviro pricing: plans, seats, and AI credits (https://zaviro.ai/llms/pricing): Plans, seats, AI credits, the trial, billing, and leaving.
- What Zaviro does not do (https://zaviro.ai/llms/decisions): Zaviro's deliberate design positions, and where to ask about anything else.
- Zaviro glossary (https://zaviro.ai/llms/glossary): The terms used across the reference.