About Messagepedia

Every content collaboration tool on the market requires the same tradeoff: to work together, you must store your content in someone else's cloud. In an era of AI-driven data mining, rising regulatory scrutiny, and routine breaches, that tradeoff no longer makes sense.

Messagepedia is the Collective Intelligence App, a cloud-free content collaboration solution built for the AI era, which eliminates that tradeoff entirely.

Messagepedia uses peer-to-peer (P2P) networking so content stays on users' own laptops and servers, never in someone else’s cloud. Every interaction is protected by end-to-end encryption, and users retain full control over what they share, with whom, and for how long. The result is a collaboration experience that's private by architecture, not just by policy.

This approach also unlocks capabilities that cloud-based tools can't match. Because content always stays on users' devices, Messagepedia delivers AI-powered features like automatic content summarization without compromising security, privacy, or control. And without cloud storage, there are no file size limits, no storage caps, and no recurring storage costs — resulting in significant cost efficiencies for teams of any size.

Messagepedia is built for professionals who handle sensitive information — from financial advisors and attorneys to accountants, consultants, and enterprise teams managing critical business data with colleagues, customers, and partners. If your work depends on confidentiality, Messagepedia treats privacy as a requirement, not a premium feature.

Teams use Messagepedia to sync files, collaborate on shared content, communicate securely, and manage information across organizations — all from a single, encrypted workspace that stays under their control.

Founded in 2023 by veterans of the collaboration and content management software industry, Messagepedia is led by Sri Chilukuri, CEO, and Tao Liang, CTO, with support from Larry Augustin, Rohan Chilukuri, Jonathan Dorsey, Ord Elliott, Terry Hicks, Ethan Liang, Michael O’Boyle, and Tommy Tam.