Healthcare AI is Broken. We're Builting Clintrue, the Causal Engine, to Fix It
The Flaw is "Aggregation Bias"
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".
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.
OUR MISSION: To engineer the global standard for Unified Clinical Intelligence by replacing data chaos with a governed 'Data Refinery.' We empower patients, researchers, and clinicians by converting raw, noisy information into Probabilistic Patient Digital Twins—secured by a forensically traceable 'Golden Thread' of consent and liability that makes AI safe, equitable, and actionable.
OUR VISION: To realize a healthcare ecosystem where clinical truth is mathematically discoverable and universally trusted; moving the industry from opaque correlations to transparent causation, and transforming the world’s fragmented health data into a unified, insurable engine of precision medicine.
A Glimpse Into Our Momentum at Petabyte Scale
72
PATENTS & CLAIMS
2
FINISHED PRODUCTS
2
VALIDATED PRODUCTS
Series A
FUNDRAISING