Bake your own memory
Teach a foundation model one fact, it keeps it, and you are paid every time an answer uses it.
About
Ainize compiles knowledge into a model's own memory table instead of retrieving it. We tested it on questions built from 15 Messari-standardized subgraphs pinned at block 25902936. On vault-asset questions the base model answered 0 of 13 correctly. The same model with one Ainize knowledge patch answered 11 of 13 — and only counting facts it got right across every held-out phrasing it was never trained on. It runs at $0.0000369 per question with zero network calls. On 30 facts deliberately withheld from training it scored 0/30, exactly like the base model, so the gain is the patch and not leakage. ENS mints a patch's name only if training proves it descended from its parent's checkpoint.
How it's made
Subgraph queries over 15 Messari deployments become {prompt, answer} training datasets carrying provenance — which subgraph, which query, which block, row hashes. A patch is a .npz of rows from the model's 51.2B-row n-gram memory table, where a single token addresses only 16 of 320M rows, written into a running vLLM through a live hook with no restart.