Category Positioning
Vector storage can retrieve similar text. AI builders still have to decide what gets written, which namespace can see it, how agents discover it, and how the team reviews recall quality over time. MemQ packages those operational concerns into the product surface.
MemQ exposes hosted MCP tools for memory operations instead of only a storage API.
The public benchmark story measures retrieval behavior, not just embedding availability.
The editor setup guide gives builders concrete config paths for multiple agent clients.
A vector database can power one retrieval step. Production agent memory also needs write policy, recall boundaries, setup ergonomics, replayability, and a way for teams to trust what is being recalled.
Agent clients need a standard connection path.
Teams need private and shared context boundaries.
Operators need confidence that the right memory appears first.
The marketing wedge should make the distinction explicit: MemQ is not competing on raw storage alone. It is selling the governed layer that turns storage, recall, setup, and usage into a product a builder can adopt.
Every online campaign should end in the same practical loop: save one decision, restart or open a fresh agent session, then recall it through the MCP tools.
Trial Conversion Path
MemQ's online funnel is built for builders who want a working memory loop before they load real project context. Start checkout, create the API key, paste the MCP config, and test recall in the agent client you already use.