Long-context AI infrastructure
Stop paying to re-read
the same documents.
lab358 makes long-context AI durable: index your documents once, then reuse and recombine them across chats and agents — with no repeated prefill — all inside your own AWS account.
Available soon on AWS · SmolLM3-3BHow it works
Convert & retrain
A model is converted to a sparse, sub-quadratic architecture and retrained — so context isn't capped and serving runs on far less hardware.
Ingest & index — once
Through the console, API, or an integration, a document is converted, indexed, and stored. A one-time cost per document.
Reuse at inference
The same model picks up any document — or any combination — across chats and agents with no prefill to redo, even days later.
Context, made durable.
Index once, reuse without re-prefill
Convert and index a document once; the model picks it up instantly in any later chat or agent, and recombines documents freely — with no prefill to redo.
Unbounded context
No context-length ceiling. Long context isn't capped by the model's training window.
Sub-quadratic, low-hardware serving
A sparse, sub-quadratic architecture served efficiently means extreme context runs on far less GPU.
Runs in your AWS account
Conversion, indexing, storage, and serving all happen inside your own VPC. Your data and indices never leave your perimeter.
Conversion preserves quality.
Evidence the conversion is lossless on quality — not the product itself. Figures are measured unless labeled illustrative.
| Metric | Result | Status |
|---|---|---|
| Warm reuse vs. cold prefill | Major cost & latency drop on repeat access | Illustrative |
| Scaling vs. standard transformer | Sub-quadratic cost curve | Illustrative |
| GPU footprint at fixed long context | Far smaller instance type | Illustrative |
| Max context demonstrated | No fixed ceiling | Measured |
Your data and your indices never leave your cloud.
Built to clear security review Read about security
Make context durable.
Index once. Reuse forever. Inside your own AWS account.
Investors & researchers: michael@lab358.ai