xysq.ai

About xysq.ai

xysq.ai is a context engineering platform built around what it calls the Memory Engine — a domain-adaptive layer that ingests raw material and continuously reshapes it into better context for AI rather than storing static embeddings. You feed it PDFs, text, images and documents; it converts them into a context graph of structured, connected knowledge that both humans and agents read. What distinguishes it from ordinary retrieval is the improvement loop: human feedback, self feedback and usage signals drive two kinds of learning — structural learning, which reorganises how knowledge is arranged for reasoning, and retrieval learning, which tunes context selection so every future retrieval is better than the last. The worked example on the homepage is a correction propagating properly: someone states that the v2 endpoint was deprecated in March and everything now routes through v3, and rather than adding a contradictory chunk, the system closes the old fact as a supersession event, enumerates rather than samples every affected hosting page, downweights the stale chunks, and retunes retrieval before the next query. Governance sits over all of it, so every change is auditable and reviewable with human oversight keeping the knowledge trustworthy. Use cases are framed for marketing playbooks, support agents that improve with each resolved ticket, sales copilots that learn team best practice, a personal context lake and a shared team lake. There is a Chrome extension, an SDK, full MCP access and public docs, and the company states data is consent-first and never trained on.

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