Products
KVRM combines multi-selector intelligence with explicit operating rules. oNeura explores computational biology. LinguaGenesis investigates verified language generation. Different problems, separate evidence.
KVRM / Research-stage · paid workflow evaluations
KVRM combines rules, retrieved cases, and compact learned models to choose among your operational actions. It filters unsupported primary candidates before ranking the remaining evidence, then validates the winner and any fallback. An inspectable Python decision engine—not another required LLM call.
One workflow. Five to ten defined actions. Sanitized historical cases. No production writes in the initial evaluation.
Compare against your existing rules and receive a written integration or no-go recommendation. A paid, fixed-scope evaluation. Price and timing are agreed in writing after scope and data readiness are confirmed.
Simulate a drug from its molecular structure to a body-level readout.
Most biological simulators pick one scale and stay there. Molecular dynamics gives you angstroms but cannot tell you whether a compound harms a patient; clinical models predict outcomes but cannot say why. oNeura runs one stack across seven layers — atoms, molecules, compounds, cells, tissues, organs, organisms — so a target interaction can propagate through channel state and action potentials into an ECG projection, with the evidence chain attached at every step.
Cardiology is just where the receipts are longest. More published trials, more endpoints, more ways to check whether we are right or just sounding right.
open https://oneura.ai # live browser demos, no installWhat this does not claim
Hybrid by design. Not every output currently begins at atoms, and reproducibility is scoped to named tests and configurations rather than universal bitwise determinism. Trial replay is retrospective mechanism-replay evidence unless prospectively frozen on blinded data.
A language system that refuses instead of inventing an answer.
Structure commits meaning, neural realizes language, verifiers enforce truth. LinguaGenesis commits typed entities, roles, source coordinates, and language-law choices before it emits any text, then replays those commitments against content-addressed authority. When a request is ambiguous, unsupported, or tampered with, the path refuses rather than turning an invalid proof into a plausible sentence.
cargo run --locked --features metal -p lg-communicator \
--bin demo -- --data ../dataWhat this does not claim
Active research, not a general-purpose language model. The broad open-generation held diagnostic is currently 0/46 complete cases. The verified results above are deliberately narrower than a language-model or agent benchmark claim.
Earlier work that shipped. Maintained, but not where the roadmap points.
These systems are research-stage, so the useful conversation is about whether the evidence actually covers your problem. Bring the hard question.
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