Separating what generates from what verifies.
Every large language model that produces facts can also fabricate them. The two capabilities live in the same weights and cannot be cleanly separated after training. 2Brains starts from a different premise: language generation and fact retrieval are different jobs that should run on different systems, then be reconciled by a third.
A language model trained on text with all named entities and factual content stripped out before training. It learns grammar, register, and discourse patterns. It cannot learn facts because it never saw any. Replaces the role of the LLM without inheriting the LLM's fabrication surface.
Facts come from a verified knowledge base, accessed through a phase-encoded vector manifold. The retrieval path is reproducible: the same query against the same corpus returns the same evidence, every time. No sampling, no temperature, no surprise.
Sniper Scope reconciles the syntactic candidate against the retrieved evidence and the planner's structural constraints. Every output is classified — verified, unverified, or contradicted — and the classification is signed into a cryptographic audit trail that survives the output downstream.
What this architecture makes possible.
Every verified claim carries a cryptographic link to the source passage that supports it.
An estimated 68% of enterprise queries route to deterministic retrieval, eliminating GPU dependence for that traffic.
When evidence is absent or contradictory, the system says so. It does not fall back to plausible-sounding prose.
Regulators, auditors, and downstream consumers can verify every output against the corpus that produced it.
Detailed technical documentation, including the patent figures and benchmark methodology, is available under NDA.
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