Fastio is a product of VividEngine, positioned as project workspace-style cloud storage for teams and AI agents. Its core goal is to give each project a unified place to store files, members, and AI agents, and to enable agents such as Claude, ChatGPT, Codex, and Cursor to work from the same files as team members.
Purpose Fastio is used to integrate project files, team members, and AI agents into one workspace. Users can continue using their existing cloud storage and only sync or import the portion of files that the team and agents need, without giving up their existing storage solution. It emphasizes "no migration": connect existing data, make that data available to agents, and coordinate agent work without replacing the storage the team relies on.
Main capabilities
- Project workspaces: A workspace contains the files, members, and activity of a project. People and agents are added once at the project level, and project members get what they need without copying files or managing permissions file by file. Each project's files, members, and activity are isolated from one another; when personnel change, files remain in the project.
- Connect existing files: Folders in Google Drive, Dropbox, Box, or OneDrive can be kept in sync one-way or two-way, a folder can be imported once, or files and folders can be uploaded directly. The project works from the same copy.
- Connect any agent: After Claude, ChatGPT, Codex, Cursor, or any MCP client connects once, it can search the workspace, read and write files in it, and continue from where the previous agent stopped. Agents can search projects by content and metadata, read, write, and organize files, and use file events to keep work up to date.
- Documents to data: Files are indexed for search as soon as they arrive. After users describe the fields they want, Fastio extracts those fields from any selected set of files to form a table that both the team and agents can filter, and can filter and sort documents by file content, providing agents with the specific details needed for a task.
- Multi-agent collaboration: People and agents share the same workspace, visible file locks show who is writing, and version history preserves every version to prevent overwriting one another. Access permissions are granted once at the project level.
How to use it The typical process is: create a workspace for a project; sync or import the relevant folders from existing cloud storage, or upload files directly; add team members and the AI agents that need to be used; after an agent connects once, it canSearch, read, and write files in this workspace. In the official description, its own team uses the workspace to retain knowledge from deprecated tools, archive old projects, and maintain a "company brain" for agents containing plans, decisions, and reference files, so that every agent starts from the same context.
Official benchmark performance In a contract renewal review task involving 2,163 files, the same agent used the Fastio workspace and each provider's own storage respectively, with Claude Opus 5 (medium effort, Cowork environment), taking the median of three runs per group. Fastio took 9:10 (range 8:10 to 9:14), while the others were 16:34, 35:00, 21:30, and 31:44; Fastio used only 38 tool calls and read 13 documents, while the other solutions used 497 to 782 calls and read 156 to 511 documents; the cost per task was $10.14 (range $9.65 to $10.79), while the other solutions were $23.76, $54.48, $45.83, and $27.63 respectively. The official claim is that the same agent was 74% faster and 81% cheaper than answering only through Dropbox.
Security design Fastio is designed around how teams use chatbots and agents today: files are encrypted at rest and in transit, access is scoped to each project, and every action by people and agents is logged. Access permissions follow the project workspace rather than individual user accounts, which the official description says simplifies security and reduces the risk of files leaking to personal cloud drives.
Use cases and limitations Use cases include: teams that need multiple AI agents and the team to share the same set of project files; organizations that want to keep their existing cloud drive without migrating everything; collaborative projects that need project-based access control and retained operation logs and version history; and workflows that need to extract document fields into filterable tables. Regarding limitations: official materials do not provide specific pricing, plan tiers, free quotas, clients for each platform, or storage capacity limits, and these are not yet available; the benchmark data for each provider is from the official self-testing, and the runtime environment is the Claude Opus 5 medium effort Cowork scenario, so actual performance may vary depending on the task and usage.
