N71
N71 is a shared context and organizational knowledge-graph product for AI agents. It connects company systems, maps people, projects, decisions, and other business objects, then exposes that governed context to agents so each agent does not start from a blank chat.
Teams using Claude, Cursor, Codex, ChatGPT, and internal agents often repeat context manually and risk each agent carrying a different view of the business. N71 matters because it frames agent memory as a governed company graph with provenance, ontology induction, scoped reads, and change-aware context rather than a loose pile of pasted summaries.
The official N71 site describes the product as a company brain that maps AI fit and orchestrates agents, with published benchmark claims and links to research and pricing. N71 Technical Report TR-2026-03 explains ontology induction, type-adoption gates, provenance, and temporal vocabulary handling. Product Hunt launch material adds the shared-context-over-MCP positioning and user questions about authorization scope, graph writes, evidence, provenance, and entity resolution.
- Give multiple AI agents one shared company context layer.
- Expose organizational knowledge to agents through MCP.
- Reduce repeated briefing across coding, sales, operations, and research agents.
- Evaluate graph-memory governance: source citations, write provenance, identity resolution, and ontology drift.
N71 targets the context-drift problem across many agents. Instead of briefing each agent separately, the product promises one living graph built from company tools and evidence. Agents can then read the graph over MCP and trace answers back to sources.
- Context asset: tools, decisions, projects, people, and history are mapped once instead of pasted into every chat.
- Agent access: the Product Hunt maker post says agents such as Claude, Cursor, ChatGPT, or custom agents can read from the graph over MCP.
- Governance question: every read and write needs scoping, provenance, and authorization because a shared graph can spread bad or sensitive context quickly.
N71 research argues that organizational memory depends on knowing the organization's own nouns. The technical report says workspaces start from a small seed vocabulary and extend through evidence-backed type proposals, promotion gates, rejection states, deprecation, and versioned ontology history.
The public reader questions are not only "does it remember?" but "can it scope access, cite sources, prevent bad writes, resolve duplicate entities, and retire stale facts?" Product Hunt discussion is useful because it surfaces those governance questions directly, while official N71 pages and research should control the factual claims.
N71 FAQ
Page-level questions for N71.
What does N71 mean by shared context for AI agents?+
N71 means that multiple agents read from one company knowledge graph instead of each chat or agent keeping its own private, drifting memory. The graph is built from connected tools and evidence, then exposed to agents over MCP with source citations and governance claims.
Why is access control important for N71-style shared memory?+
A shared graph can make every agent smarter, but it can also expose sensitive context to the wrong agent if scoping is weak. Readers should verify whether reads are scoped by user, role, source, node, edge, and task before connecting high-trust business data.
How does N71 avoid a messy or stale knowledge graph?+
N71 research describes ontology induction with promotion gates, provenance, deprecation, and versioned ontology history. That means new business-specific types are supposed to enter the graph through evidence-backed governance rather than arbitrary model output.
Is Product Hunt enough evidence for N71 facts?+
No. Product Hunt is useful for launch context and user questions, especially around authorization, provenance, and entity resolution. Product facts should come from the official N71 site and research pages.