Every complex event leaves a signature. We reconstruct it from the data that already exists.
A different class of compute.
ORIGIN //
ORIGIN reconstructs the structural signature of chemical and biological events from observable data alone — without access to the agent, custody of the sample, or consensus from any institution.
Works on novel events. No training data required.
From the data that already exists.
Outputs are deterministic, traceable, and mechanistic.
The same inputs produce the same answer, every time.
CASCADE //
CASCADE reconstructs the structural architecture of disease from clinical and genomic data alone. Each tumor traced from founding event through therapeutic vulnerability.
Works on single samples. Operates on the data clinicians already have. Variants, clinical history, treatment context. No proprietary inputs. No new sequencing required.
Outputs are deterministic, traceable, and mechanistic. Every causal claim traced to source data with confidence ratings.
Validated Applications
{01}
Defense & Intelligence
ORIGIN validation.
Threat attribution under time constraint.
{02}
Therapeutic Development
CASCADE validation.
Disease architecture, reconstructed from genomic data.
{03}
Biosurveillance
ORIGIN validation.
Pathogen reconstruction from surveillance data.
What We Do That Other Systems Cannot
Our systems surface causal signals through deterministic convergence. Multiple properties make them deployable where conventional machine learning fails.
{01}
DETERMINISTIC
Same inputs in. Same answer out.
No drift. No guessing.
Auditable. Defensible. Admissible.
{02}
GENERALIZABLE
Works on novel events. No training data. No historical examples. Validated across five categorically distinct domains without modification. Detected Zika emergence in dengue surveillance 22 months before official recognition.
{03}
MECHANISTIC
AUDITABLE Every output traced to specific statistical tests, effect sizes, and significance levels. Defensible under technical review. No black box.