Facts, not the chatbot, should decide.
We compile a public fact base for your domain. Your records write on top. The store is the asset your institution owns. A language model reads the question and narrates the cited answer or tool call — it does not choose the facts, and it is not the asset.
RAG and GraphRAG buy accuracy by buying a bigger model — the frontier tax, forever. Compile-Time Inference decides the answer from your data, so the cited answer sits still at every tier. The cheapest model suffices.
Kevin
Compiles the Australian legal record; ranks precedents by graded court outcomes; won't cite a case not in the held record.
Hybrid-VLM
Starts from the News24:7 live broadcast feed; every frame stamped as it airs; answers link to the clip.
Genomics
Public biomedical sources + a lab's own assays under NDA; cite what the store holds, decline when it doesn't.
When the model decides, bias rides in on the argmax.
On a public education dataset, a commercial RAG-style advisor flagged disabled students 63.3% of the time versus non-disabled 32.8% — an adverse-impact ratio of 0.55, below the 0.80 four-fifths line. Telling the model to “be more careful” flipped the error. Policies compiled from labeled outcomes closed the gap (not statistically significant) and gave the same student the same advice every time.
Presented at Brown University's ML Symposium on Justice, Sept 24–26 — “Intervention Bias as Disparate Impact.”
Move the conversation off tokens-per-watt — onto who owns the answer.
A cloud-efficient margin play: redirect enterprise budget away from token waste into local, owned infrastructure. Meet us at the Summit, or book a call.