Allivista.
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Examples & cookbook

What to ask, and what comes back. Once Allivista is connected, just ask your AI in plain language — it picks the right tool. No special syntax.

discover read-only

Surface concepts the literature connects only indirectly — a hypothesis engine.

"Use Allivista to discover hidden bridges for Parkinson's disease."
→ bridge concepts (e.g. a pulmonary / vascular link) with strengths, plus a short narration.
"What does the literature indirectly connect to long COVID?"
→ discover(anchor: "long covid,post-acute sequelae,pasc")
"Find Swanson-style undiscovered links around migraine."
→ candidate connections no single paper states outright.
Use it for: hypothesis generation, literature-review starting points, "what am I missing?" Tip: pass comma-separated surface variants (alzheimer,alzheimers,dementia).

verify read-only

Check whether the cortex's structure supports a claimed relationship — a hallucination guard.

"With Allivista, verify whether amyloid-beta drives Alzheimer's."
→ supported_direct (strongly attested) + the bridging concepts.
"Is there a structural basis linking the gut microbiome to Parkinson's?"
→ often supported_indirect — a hypothesis, not a settled fact.
"Does the literature support 'Bitcoin causes Alzheimer's'?"
→ unsupported, zero structural basis — the guard working as intended.
Use it for: sanity-checking a claim before you cite it. Verdicts: supported_direct, supported_indirect, weak, unsupported, anchor_absent.

remember write

Store a note verbatim in your private, end-to-end encrypted memory.

"Remember this in Allivista: the Q3 vendor quote was $48,250; contact Dana Reyes, ext 7731."
→ stored exactly; encrypted on your device, we only ever hold ciphertext.
"Save this transcript to my Allivista memory under 'client-call-0612'."
→ remember(text: "…", title: "client-call-0612") — reuse the title to overwrite.
Use it for: persisting decisions, facts, references, and transcripts across sessions — so they're recalled exactly instead of paraphrased.

recall read-only

Find a verbatim passage across everything you've stored — exact, not fuzzy.

"Recall from my Allivista memory: the Project Halcyon vendor quote."
→ the exact passage you stored, plus which note it came from.
"What did I save about Dana Reyes' extension?"
→ pulls the precise line — search a distinctive literal phrase you know is in there.
Use it for: exact retrieval and anti-hallucination memory. It's a structural lookup (it finds the literal phrase), not embedding similarity — so be literal.

Combine them

• Research loop: discover bridges around a topic → verify the promising ones → remember what held up, with a title.

• Grounded memory: remember a source's claim, then weeks later recall it verbatim and verify it before you repeat it.

What is GeoLang?

Allivista is one application of GeoLang — a language that encodes meaning as geometry.

Instead of squashing words into opaque vectors, GeoLang places concepts as points in a structured space, related by typed faces and co-occurrence bonds. Meaning lives in the structure — inspectable, deterministic, and fast.

The cortex (discover / verify) reads that geometry directly across 4.3M papers — no embeddings, no model guessing, just the shape of what's actually connected.

Your memory (remember / recall) is a per-user keyed GeoLang dialect: every word becomes an opaque structural id under your passphrase. Your machine searches the structure; our servers only ever see ciphertext. That's how recall is verbatim and zero-knowledge at the same time.

The cortex and the encrypted memory are just two dialects. The same idea extends to structural search over any corpus, lossless meaning-preserving compression, and fast machine-to-machine concept exchange. Allivista is where you can use it today — it's the tip of the iceberg.

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