Clarit.ai develops Engram, a model-native memory system designed to give language models persistent state during inference. Rather than replaying earlier conversation through prompts, Engram saves model state so applications can restore it in about 2 milliseconds and process only new tokens. The company reports 93.8% fewer tokens processed per turn and says the technology has been validated across five architectures, from 4B to 120B parameters, using models from IBM, NVIDIA, and Alibaba.

Clarit.ai presents Engram for engineers building AI applications and for end users who want models that can remember. Its site offers access to the Engram project, benchmark information, and a GitHub repository, alongside an ecosystem, blog, and company overview. Engram is positioned as infrastructure for stateful inference in Mamba and hybrid state-space language models, aiming to reduce repeated computation while preserving conversational context. The available material focuses on this memory technology and its performance claims; it does not specify pricing, deployment options, or a broader catalog of products and services.