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The shared knowledge layer for AI-native teams.

A company runs on shared context: what it knows, what it decided, why it chose this over that. That context now forms inside AI tools and scatters across them. xysq is the layer that holds it, shared on your team's terms.

Capture. Memory. Skills. The foundational block for AI-native companies.

Working knowledge is split across three places, and nothing holds all three.

AI sessions hold what your team is figuring out, in Cursor, Claude Code, ChatGPT. Docs and specs hold what got written down, in Notion, Google Docs, READMEs. Communication holds what got decided, in Slack, Linear, email. The information already exists. It is just not connected, current, or trusted across all three.

No teammate and no AI tool ever sees all three. So the team re-asks, re-derives, and contradicts itself. CEOs say things at all-hands that engineers never internalise. Support sees a pattern that product hears about from sales after the renewal closes. Every AI tool you add does it faster, not slower.

The teams that close this gap are shipping twice as much. The rest lose roughly 5.3 hours a week per person recreating work that already exists.

Three pillars. One shared brain.

We build the bedrock first. Capture earns trust, memory earns the graph, skills earn the compounding. The order matters.

Consent-first, always

Capture

  • Deep integrations with Cursor, Claude Code, GitHub, Linear, Slack, Notion, and Granola, plus the AI sessions your team runs every day.
  • Bidirectional from day one: we read history, keep it in sync, and write back when it is time.
  • Nothing flows in by default. Every source, scope, and agent is something you explicitly turn on.

One product. Three altitudes.

For engineering leaders

New joiners reach productive in two weeks, not six. Internal AI agents stop hallucinating company context.

For product and operations

Cross-team signals reach the right owner in days. Roadmap contradictions surface before they cost a launch.

For the C-suite

Strategy decided at the all-hands reaches the engineer writing the code that week. Not next quarter.

What it does today.

Skills that compound

Repeated work becomes a reusable skill, generated from your team’s own memory. The next onboarding doc, review, or spec writes itself, to your standards, not a generic template.

Agent-readable memory

Your existing AI tools get the truthy source for free. No new app to learn. Claude, Cursor, Copilot, your internal agents, all smarter overnight.

Audit and provenance

Every claim links to its source. Every action is timestamped and attributed. Tamper-evident, exportable, audit-ready from day one.

Commitments.

Consent first, always.

The user controls the data. Per-source, per-scope, per-agent. Built into the foundation, not bolted on as a feature.

Provenance on every claim.

Every fact links to its source. Every action is timestamped and attributed. Auditable from day one. The compliance moat is the trust moat.

Humans in the loop on the things that matter.

Tiered autonomy. We nudge before we act. Policy, security, legal escalations always require human judgment. The audit log is tamper-evident.

Neutral across the tools you already use.

We are not a wiki. We do not compete with Notion or Confluence. We make them better. Our business model rewards being neutral across them, not capturing you on a surface.

People will change.
Teams will evolve.
Organisations will grow.

Knowledge should persist through all of it.

xysq is building the shared knowledge layer, the foundational block that carries individuals, teams, and entire organisations forward across time. The substrate that makes a company legible to itself, and to every AI tool it has deployed.

Building this with us.

Investors, design partners, and future operators who want to see the staged path in detail can book a 25-minute call. We share the deck ahead of the conversation.