quelvio.com
human view ↗
---
title: Quelvio · Your company's brain. In everything you do.
canonical: https://quelvio.com/
machine_url: https://quelvio.com/ai
---

# Your company's brain. In everything you do.

Connected to every system — the intelligence context layer for humans and agents.

Connectors: Google Drive, SharePoint, Notion, Slack, Confluence, Teams, Box, Dropbox, OneDrive.

[Create your company's brain](https://enterprise.quelvio.com/sign-up?redirect_url=https%3A%2F%2Fenterprise.quelvio.com%2F)

## How it works

How it works

Better answers start with better knowledge.

  1. 1.Scattered (Internal silos) — Drive, SharePoint, Notion, Slack, Confluence — your team's knowledge is scattered across all of them, and more. Nobody can find what someone already wrote. New hires recreate work that already exists.
  2. 2.Better retrieval (Enterprise RAG) — Better retrieval mechanics, same expertise problem. Keyword matches don't know which author is authoritative. Full-text search doesn't know which answer is current.
  3. 3.Authority-scored, cross-referenced, attributed. (Quelvio) — Acts like a company brain: continuously ingesting, chunking, embedding, ranking, forgetting, and re-ranking knowledge based on authority, freshness, durability, permissions, and usage.

## The brain

This is the brain.

Quelvio continuously understands your company's knowledge across every system — what's authoritative, what's recent, what's persistent, what's temporary, and what should be forgotten.

## For organizations

Trusted organizational memory — for humans and agents.

Most enterprise knowledge lives scattered in videos, PDFs, threads, and people's heads — invisible to search, impossible to synthesize. And as companies deploy AI agents, context becomes the bottleneck. LLMs can retrieve fragments. MCP can connect to tools. Neither creates a persistent, evolving understanding of your company.

Quelvio does. The universal context engine for institutional memory of how your company actually works, served to people and agents in milliseconds. Same brain whether you're 50 or 50,000.

  • -Retrieval: <200ms — across docs, videos, wikis, meetings, chat threads — any format, authority-ranked
  • -Formats ingested: 14+ — PDFs, video, audio, slides, wikis, Slack, Notion — semantically understood
  • -Knowledge map: 48 hrs — full expertise topology, coverage gaps, and risk signals from connector setup
  • -Corpus: TB+ scale — Indexes and synthesizes a brain of multi-terabyte knowledge.

[Memory for your org](/enterprise)

## Pipeline

Why answers come back faster, cheaper, more accurate.

Targeted retrieval over scattered search.

Instead of scanning every document for every query, Quelvio routes each question to exactly the right passages, ranked by who knows what. The result: answers in milliseconds, fewer tokens per query, and citations that point to current expert thinking — not whatever indexed first.

### Every format. One brain.

PDFs and Word docs. Videos and meeting recordings. CRMs, tickets, codebases and databases. Slack threads and Notion pages. Spreadsheets, slide decks, audio interviews, scanned images. Quelvio extracts meaning from all of it — text, transcripts, slide content, image OCR — and folds everything into a single searchable brain.

### The five-step pipeline.

  1. 1.Ingestion — Connect any source. Documents, wikis, threads, spreadsheets — pulled in with permissions intact.
  2. 2.Embedding — Each document split into meaning-based passages, then vectorized — so retrieval happens at the passage level by meaning, not by keywords. Answers cite the exact paragraph.
  3. 3.Indexing — Stored in a vector DB with three layers of context: taxonomy classification (routes queries to the right expertise), authority scoring (ranks by who wrote it), and a cross-reference graph (agreement, disagreement, supersession). Stale content is demoted; current thinking surfaces.
  4. 4.Retrieval — At query time, the top-k matched passages return with citations, authority rank, and lifecycle state — permission-aware end to end.
  5. 5.Synthesis — Quelvio Brain composes the answer over the retrieved passages — every claim traced to its source.

## For autonomous agents

Agents inherit their context. We give them yours.

Most agents today retrieve whatever indexed first. They quote a runbook written in 2024 and miss the rewrite from last week. They cite a Slack thread without knowing it was contradicted by a Notion doc. They surface duplicate content with no idea which version is canonical.

Quelvio gives your agents the same context your senior engineers have — what's current, who said it, whether it's still valid. Same retrieval pipeline, same authority ranking, same supersession detection. Your agents stop guessing.

## Why not just an LLM, or just MCP?

### Generic LLMs

  • -Generic LLMs: Static training data · Quelvio: Continuously evolving company memory
  • -Generic LLMs: Treats an approved policy and a Slack message the same · Quelvio: Distinguishes approved policies from drafts, opinions, and superseded memos
  • -Generic LLMs: Grounds answers in what they were trained on · Quelvio: Grounds answers in your company's context
  • -Generic LLMs: Limited memory governance · Quelvio: Supports forgetting, supersession, and lifecycle control

### MCP-only access

  • -MCP-only access: Connects agents to tools · Quelvio: Builds a living knowledge layer across systems, tools, and files
  • -MCP-only access: Pulls information on demand · Quelvio: Pre-processes and continuously ranks context
  • -MCP-only access: Tool-centric · Quelvio: Context-centric
  • -MCP-only access: Useful for access · Quelvio: Useful for understanding

## One brain. One API. Every AI stack.

For developers · agent builders

Wire any AI agent — or build your own — into the same knowledge layer. The CLI, the MCP server, the REST API, and framework SDKs all front the same retrieval + synthesis pipeline. Direct OpenAI, Anthropic, and Mistral function-calling specs ship in the docs.

Terminal agents · scripts: npm i -g @quelvio/cli

MCP clients · custom runtimes: https://mcp.quelvio.com/http

  • -Claude Code (Terminal · CLI): Anthropic's terminal agent invokes `quelvio query` on any internal-knowledge question. Install the CLI (above), export `QUELVIO_TOKEN`, then drop the skill file into your user-scoped skills directory.
  • -Cursor (IDE · MCP + Rule): Cursor speaks both MCP and the CLI. Add the MCP server for a native in-editor tool call, or drop the Cursor rule shown below for a shell-based path.
  • -OpenAI Codex CLI (Terminal · AGENTS.md): Install the CLI (above), export `QUELVIO_TOKEN`, then drop `AGENTS.md` at the repo root. Codex picks up the Quelvio integration on every session.
  • -Mistral (Function calling · JSON): Mistral's function-calling API is OpenAI-compatible. Register `quelvio_query` as a tool; dispatch tool_calls to POST /v1/enterprise/query. Stringify the tool result before feeding it back as `role: "tool"`.
  • -Any MCP client (MCP · OAuth): Any MCP-compatible runtime — in-house agents, custom builds, OpenClaw — connects via OAuth 2.1 with PKCE; DCR-supported.
  • -REST API (HTTP · Bearer): The knowledge layer underneath your own product. Bearer auth, permission scopes, taxonomy routing — same retrieval pipeline as the CLI and MCP server.

One line of code. Zero friction. Each SDK ships as a single install. No config files, no API wiring — import the retriever, point it at your token, and your framework starts asking the brain.

  • -LangChain: pip install quelvio-langchain · npm i @quelvio/langchain
  • -LlamaIndex: pip install llama-index-retrievers-quelvio
  • -Vercel AI SDK: npm i @quelvio/vercel-ai-sdk
  • -CrewAI: pip install quelvio-crewai

[Integrations docs](https://quelvio.com/docs/integrations/llm-tool-definitions) · [For agents docs](https://quelvio.com/docs/ai-agents)

## Where employees work

Ask from anywhere. Answers cite everywhere.

Your employees don't learn a new tool. Quelvio meets them inside Slack, Teams, or the AI assistants they already use — with answers grounded in your company's own knowledge.

  • -Slack: Invite @quelvio into any channel. Answers post in-thread with full citations — the source document, the author, the date.
  • -Teams: Same experience inside Microsoft Teams. SSO-aware, permission-respecting, cited in-thread.
  • -Claude: Add Quelvio as a Custom Connector in Claude.ai or Claude Desktop. Every conversation pulls cited answers from your company's knowledge via MCP.
  • -ChatGPT: Connect Quelvio to ChatGPT via MCP custom connector. Employees using ChatGPT get answers from your company, not just the web.
  • -Dashboard: The Quelvio web app at {company}.quelvio.com. Full search UI, filters, citation inspector, knowledge graph visualization.

## Sources

Drive, SharePoint, Notion, Slack, Confluence, Box, Dropbox, Teams — and more. Each source is indexed with permissions preserved, so retrieval respects what every individual employee can actually see.

Indexed sources: Drive, SharePoint, Notion, Slack, Confluence, Box, Dropbox, Teams.

## Trust

  • -Permission-aware retrieval: Employees only see results from documents they already had access to.
  • -Tenant-isolated: Your content is never visible to others. Enforced at the database, vector, and storage levels.
  • -No training, ever: Your content is never used to train models. Inference APIs only.
  • -Encrypted & verifiable: Encrypted at rest and in transit. Every consent event logged and independently verifiable.

[Full security & compliance details](/enterprise)

## Get started

Give your AI your company's brain.

[Create your company's brain](https://enterprise.quelvio.com/sign-up?redirect_url=https%3A%2F%2Fenterprise.quelvio.com%2F) · [Read docs](/docs/mcp)

## Machine routes index

Every public Quelvio page is mirrored under /ai/ as server-rendered markdown. Each /ai/ route declares a canonical link back to its human equivalent.

### MCP server

> https://mcp.quelvio.com/http

[Install instructions](/docs/mcp#install)