Quello

About Quello

Quello is a knowledge base tool that aggregates files, documents, and information from multiple work tools into a single searchable workspace. When users ask a question, the tool returns answers with citations pointing to the original sources. It also includes a voice agent for spoken queries and supports MCP so work context can be carried into external AI assistants like Claude, ChatGPT, Gemini, Grok, and Perplexity.

Review

Quello addresses a specific friction point: finding answers when work is distributed across many different applications. Rather than searching each tool individually, users query one workspace and receive responses backed by source links. The tool launched this week and comes from a solo maker, which explains its focused feature set and the emphasis on feedback-driven development.

Key Features

• Unified search across connected tools, files, and documents. Quello pulls content from multiple sources and returns a single answer with citations that show where the information originated.
• Voice agent that accepts spoken questions and responds using the same workspace-bound knowledge. This keeps hands-free workflows tied to the user's own data.
• Built-in document editor with a context-aware copilot. The copilot draws on the entire workspace when helping write or refine documentation, rather than starting from a blank page.
• MCP server integration that sends sourced passages to external AI assistants. The connected assistant stays grounded in workspace content, and sources remain traceable back to the user's files.
• Conflict handling that surfaces multiple sources when files disagree. Quello doesn't silently choose which version is correct; it presents the relevant sources so the user can decide.

Pricing and Value

Quello is currently listed as free. The maker has not published a detailed pricing page or described future plans for paid tiers. The infrastructure choices-Groq for inference to keep per-query costs low, and Cloudflare R2 for zero egress fees-suggest an effort to maintain predictable operating expenses during the launch phase. Whether pricing changes later is not yet defined.

Pros

• Answers always include citations, so users can verify information against the original file instead of trusting a model's output blindly.
• MCP support means workspace context isn't locked inside Quello; it travels with you into other AI tools you already use.
• Conflicting information across files gets surfaced rather than smoothed over, leaving the decision about what's current to the user.
• The voice agent works against the same source-bound knowledge base, which keeps spoken Q&A grounded in actual documents.
• Zero egress fees for stored files mean the maker can serve documents and citations without costs scaling unpredictably with usage.

Cons

• The tool is brand new and built by a solo maker, so long-term reliability, support responsiveness, and feature velocity are unproven.
• Quello only answers from files users have uploaded or connected; it doesn't incorporate general knowledge from the web or external databases unless that information exists in the workspace.
• Teams that need role-based access controls, audit logs, or compliance certifications won't find those features here-the current version is not suited for heavily regulated enterprise environments.

Quello fits individual professionals and small teams who already store their knowledge across multiple tools and want a single place to ask questions with source backing. It's also relevant for anyone who regularly switches between different AI assistants and wants their work context to follow them via MCP. Organizations with strict security requirements or those needing external knowledge sources mixed into answers should look elsewhere for now.