The Mathematical Source of Truth for Human Biology
Our Low-Liability Simulation Engine harmonizes
fragmented data into a"Pristine Graph"
solving the $100B clinical
bottleneck with 0% hallucination.
STAGE I
INGEST
The Probabilistic Harmonization Engine (PHE) ingests heterogeneous, "noisy" data from all sources (EHR, Omics, Wearables). It is a "canonical data engine" that solves the "garbage-in, garbage-out" problem by creating high-fidelity "Probabilistic Patient Digital Twins"
STAGE II
ANALYZE
The Federated Subgroup Analysis (FSA) architecture runs "Clustered Federated Learning" across our distributed network. This "weaponizes heterogeneity" to create "High-Fidelity Pooled Subgroup Models" from the digital twins.
STAGE III
SIMULATE
The Causal Hypothesis Ensemble (CHE) engine runs its generative, N-of-1 HPC query against one of these specific, high-fidelity subgroup models. This "causal simulation" produces the final, low-liability "Ranked Differential Diagnosis" for the clinician.
Pillar 1
THE "MODEL FACTORY" FEDERETED SUBGROUP ANALYSIS (FSA)
This is our advanced "Clustered Federated Learning" (CFL) model. Instead of one "global model," the FSA "weaponizes heterogeneity" by discovering "statistically distinct patient subgroups" across our network. It then trains "specific, high-fidelity 'pooled subgroup models'" using data that "never leaves the hospital's secure environment," solving the "data-hoarding" problem.
Pillar 2
THE "SIMULATION ENGINE" CASUAL HYPOTHESIS ENSEMBLE (CHE)
This is our core, patent-backed "low-liability simulation engine". It is not a predictive "black box". The CHE performs a "generative and simulation-heavy task" to answer the N-of-1 "what if" query. Instead of a single, high-liability "answer," the CHE generates a "ranked differential diagnosis of multiple, competing causal hypotheses" for "human adjudication".
Clintrue: The Engine of the
Next Biological Revolution.
Powered by NVIDIA.
Strategic Partnership: Powering the Biological Source of Truth
Clintrue’s selection for NVIDIA Inception acts as the computational accelerator for our Probabilistic Harmonization Engine (PHE). We are not building a mere "framework"; we are deploying a Low-Liability Simulation Engine powered by a Causal Hypothesis Ensemble (CHE). This partnership ensures we possess the hardware-enforced scale to "weaponize heterogeneity" , transforming fragmented health silos into high-fidelity Probabilistic Patient Digital Twins (PPDTs) —creating a single, forensically traceable computational reality.
The "Impossible" Query: Achieving Mathematical Certainty
We have moved beyond simple data matching to achieve predictive simulation. By stress-testing our engine against the entire U.S. clinical trial history, we have proven that the "impossible query"—which once took weeks of manual research—can now be solved in seconds with 95% real-world accuracy. We are ending the "guessing game" of medicine by providing a mathematical source of truth where clinical outcome is backed by a Low-Liability Simulation Engine powered by a Causal Hypothesis Ensemble (CHE) before the first dose is given.
Bulletproof Integrity: The Forensic Chain of Custody
In the move from correlation to causality, trust is built on mathematical rigor, not just "reliability". By utilizing NVIDIA’s enterprise-grade compute, Clintrue implements a Forensic Attribution Ledger that proves the "Chain of Custody" for every data point. We don't just secure data; we ensure the causal integrity of high-dimensional standardized tensor representations, providing the legal and scientific defensibility required for a $100B+ market opportunity.
The Computational Dead End of "Big Tech Health"
The promise of personalized "N-of-1" medicine has failed. A 17-year gap persists between biomedical evidence and routine clinical practice. This failure is not due to a lack of data, but a fatal flaw in the legacy computational architectures that first-generation AI adopted. We call this flaw Aggregation Bias.
Current AI relies on "monolithic 'global models'" that compress high-dimensional patient data into a "biologically meaningless average." This process creates a model accurate for an "average" patient who doesn't exist, but dangerously inaccurate for the specific heterogeneous subgroups that define real-world medicine.
This architectural failure is a computational dead end—a limit of deterministic modeling that our architecture solves via high-fidelity, probabilistic simulation.
A Glimpse Into Our Momentum at Petabyte Scale
72
PATENTS & CLAIMS
(Core Al Algorithms)
2
FINISHED PRODUCTS
1
VALIDATED PRODUCTS
Series A
FUNDRAISING
Who We Are
Querying The Impossible
The "Impossible" Query
OUR MISSION: To index and harmonize the world's fragmented health data into a single, computable reality. We enable researchers to query the "Impossible" and accelerate cures at the speed of math.
OUR VISION: To become the mathematical source of truth for human biology, where every clinical outcome is backed by a Low-Liability Simulation Engine powered by a Causal Hypothesis Ensemble (CHE) before the first dose is given.
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