intelligence.semurg.io

Your AI, next to your data.

Building, not yet serving natively

Semurg Intelligence runs models natively on the substrate: weights are uniform containers on the same one store as everything else, no sidecar store and no second path. The same engine that serves a database page serves a model expert the same way, keeping the working set warm. Run an open-weight model on CPUs you already own, with nothing leaving the building, and put the model beside the knowledge it reasons over.

What you will build here

Sovereign CPU model servingStream model weights from disk through the same substrate that serves your data, on commodity CPUs, no GPU and no egress. Models too large for memory stream in; shared expert weights are stored once.

GraphRAG on one copyChunks, embeddings, entities, and relationships as containers kept together. A search seeds a short graph walk that fetches the exact passages, in one process. Change a fact and the next answer reflects it, with no re-index window.

Agent memoryEach memory is a container on one substrate. Recall blends similarity, relationship links, and recency in one query, and the model backend runs locally, so the agents carry no per-token API bill.

Honest status

✓Done, substrate-native weight logistics: bit-exact, no-sidecar, uniform.
●In progress, E4B native serve on the substrate (bring-up on the reference node), and the collapse of model weights into the one store.
○Queued, interactive throughput on target CPU silicon, and larger mixture-of-experts serving.

We frame this as a sovereignty-and-economics capability, not a tokens-per-second record. Single-user speed on CPU is bounded by memory bandwidth; a GPU wins raw single-stream latency. What Semurg changes is where the model runs, what it costs, and that it never leaves your control. Frontier-scale model serving is a proven research capability that is currently parked on specific hardware; it is not something we demo as a product.

We are not going to show you a demo that is not real. When native serve is measured and honest on this node, it appears here with the hardware and the numbers, the same measured-internally, reproduce-it-yourself standard as data.semurg.io.