How context becomes useful.

Keep each corpus isolated, read it through multiple frozen embedders, preserve provenance, then expose only the context the AI needs.

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Ingest

A creator archive, legal file set, call library, or video corpus is cleaned into text, images, audio features, metadata, and source references.

Separate

Each database stays isolated so context does not contaminate other work. Search can cross databases only when the question calls for comparison.

At N=13, the counting identity gives an up to 91-signal ceiling per input. Pairwise-MI EXP-2 audit pending.

Verify

Answers and generated outputs carry source paths, receipts, or centroid checks where the system can provide them. G_tau is a cosine-threshold predicate, not a ranking claim.

Founder explainer

See one corpus become a scoped training artifact.

Watch on YouTube· 11:49

Case 1: Shakespeare signal-density walkthrough

This is the implementation-language version of the method page: Shakespeare text, a frozen embedder panel, derived labels, and a LoRA artifact. Read it as a Case 1 walkthrough; the site keeps the realized multiplier separate from the N=13 constructive ceiling and EXP-2 remains pending.

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Counting identity

With 13 frozen embedders, the identity gives an up to 91-signal ceiling per input. Pairwise-MI EXP-2 audit pending.

Have one context problem?

Send the audience, data type, target task, proof bar, and sharing limits. The first pass should stay bounded.

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