From our lab to the world

Enterprise AI fails not because models are weak, but because memory infrastructure is broken. Cielara Reasonara is a graph-structured causal memory system that gives AI agents persistent, structured knowledge, including the latent constraints, prior commitments, and causal context that standard retrieval systems miss entirely. Achieving 94% accuracy on enterprise-scale benchmarks with 98% fewer tokens than full-context approaches, Reasonara is the only memory system that explicitly addresses cognitive memory: the class of retrieval problems where the right answer depends on what the agent already knows, not just what the query asks.

RCML is about representation. CLIP-style models squash a multimodal artifact into one fixed embedding and reuse it no matter what you're asking. RCML conditions the embedding on the relation you're querying, so the same sample looks different in different contexts. On relation-conditioned retrieval that's roughly 31% better Hit@5 than the CLIP backbone, and it holds in zero-shot, fine-tuned, and out-of-domain settings.

SkillLens is about reuse. Most systems treat a learned skill as a flat prompt block you either dump in whole or rewrite from scratch. We organize skills into a graph and pull only the pieces that fit. On ALFWorld it beat every skill-reuse method we tested (AutoSkill, EvoSkill, the vanilla baseline), lifting success from 45.00% to 51.31% with 50 steps tasks.

LARGER is about navigation. Hand the agent a dependency graph of the whole codebase+context before it opens a single file. GPS instead of driving down every street. We saw +13.9 points on localization, about 10% faster, and 30 to 40% fewer tokens.