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How it works
Convert a model once, index your documents once — then reuse and recombine them across chats and agents with no prefill to redo. Here is the whole pipeline, in buyer terms.
A model is converted to a sparse, sub-quadratic architecture and retrained, with the context-length ceiling removed. Attention can then be served efficiently, hardware needs drop, and context length is no longer capped. Today, that's a single converted model.
For ML buyers — the credibility and mechanism story.
Through the console, an API call (including from another application), or an integration, a user selects a document to be converted, indexed, and stored — supporting multiple versions and edits. This is a one-time cost per document.
The “set it up once” promise.
The same model uses any stored document — or any combination — as part of a chat or agent: the console for testing, the API for production. Because step 2 is persisted, there is no prefill to re-run between uses, even days apart.
Where cost and latency savings land.
Standard tokenizers and an OpenAI-compatible endpoint — point your existing stack at it and keep your tooling.
Index once. Reuse forever. Inside your own AWS account.
Coming soon: hosted models on lab358 Cloud, or self-hosted via AWS Marketplace.