Research
Building the context and governance infrastructure that makes agents deployable inside real organizations.
Enterprise AI does not fail because models lack intelligence. It fails because models arrive at an organization blind — no structured account of what was decided, what changed, who owes what to whom — and ungoverned: no principled answer to what they are permitted to do about any of it. Every serious deployment eventually hits both walls. N71's research program exists to remove them.
Context Is Infrastructure, Not Retrieval
The industry treats organizational context as a search problem: embed everything, retrieve what's similar, hope the model sorts it out. We believe context is an infrastructure problem — closer to a database than a search engine. Meaning must be resolved when information arrives, not guessed when a question is asked. Provenance must be preserved, not discarded at ingestion. Time must be a property of every fact, not a sort order. And permission must travel with the context itself, because an agent that can read everything and is allowed to do anything is not a product — it's an incident report waiting to be written.
Grounded, or it doesn't ship
A memory system's most dangerous output is a confident answer to a question its corpus cannot support. Most systems fail open — they generate something plausible. N71 fails closed: when the evidence cannot support an answer, the system refuses — explicitly — rather than restating stale facts in a confident voice. In enterprise settings, knowing what the system doesn't know is worth as much as what it does.
The model is swappable; the memory is not disposable
Because organizational memory lives in the substrate rather than inside any vendor's context window, the agent on top is a replaceable part. Swap one model for another and the incoming agent inherits the full memory — with no migration step and nothing lost. What your organization knows is never hostage to which model runs this quarter.
What we've measured
MEME benchmark evaluation. MEME (KAIST AI · Tübingen · NAVER, 2026) is the first benchmark to isolate the two memory tasks that matter most in evolving organizations: Cascade — when an upstream fact changes, do dependent facts update? — and Absence — does the system know when it doesn't know? Field averages are 3% and 1%. On the full 100-episode suite, N71 leads the field: Cascade 0.628, Absence 0.42, and 0.574 overall — an order of magnitude above the field on the two tasks that matter, and the highest overall of any memory system in the study. We publish the losses too. Full numbers on the benchmarks page.
A note on disclosure: we publish measured results — the things that make a claim falsifiable. How we achieve them stays ours. We publish what we'd want to be held to, not what we'd hand a copycat.
N71 is context infrastructure for the agent era.