10M+ atomized, vectorized, context-complete knowledge units, ready to query.
Small LLMs perform like much larger ones when they're grounded with precise facts.
Cleaner inputs → better outputs. Our atoms remove ambiguity, filler, and noise from text.
Atoms average 4-12x fewer tokens than raw web content, without losing information.
Bring your own docs to our atomization pipeline so proprietary knowledge lives alongside public atoms.
Our pipeline transforms messy unstructured data into AI-ready vectorized atoms.
We ingest terabytes of public corpora, preserving structural metadata while filtering noise.
Complex documents are split into self-contained "atoms" with resolved references and context.
Each atom is embedded into high-dimensional space (384-1024d) for semantic retrieval.
Millisecond retrieval via our global edge network. Ready for your LLM context window.
Simple usage-based pricing. No hidden fees.
Perfect for starting out.
For production applications.
For large-scale deployments.
Join early access + claim your $5 API credit.