Harness True Biointelligence
AI-enabled, precision engineered. AZKAR is a vertically integrated clinical trial genomics intelligence platform empowering investigators finding high-likelihood responders by fusing genomic variant signals with clinical criteria.
AZKAR.
Our vision is simple: the right patient, the right trial, the right place, the right time.
Faster recruitment
Pre‑identify patients most likely to meet trial criteria, reducing screen failures, site burden, and enrollment timelines.
Faster recruitment
Pre‑identify patients most likely to meet trial criteria, reducing screen failures, site burden, and enrollment timelines.
Faster recruitment
Pre‑identify patients most likely to meet trial criteria, reducing screen failures, site burden, and enrollment timelines.
Single Pane of Glass
From raw genome upload to ranked trial lists, one secure workflow for sponsors, CROs, and investigators.
Single Pane of Glass
From raw genome upload to ranked trial lists, one secure workflow for sponsors, CROs, and investigators.
Single Pane of Glass
From raw genome upload to ranked trial lists, one secure workflow for sponsors, CROs, and investigators.
Deeper Insights
Variant‑ and phenotype‑level ML layers provide critically precise biological evidence, interpretable to clinicians and regulators.
Deeper Insights
Variant‑ and phenotype‑level ML layers provide critically precise biological evidence, interpretable to clinicians and regulators.
Deeper Insights
Variant‑ and phenotype‑level ML layers provide critically precise biological evidence, interpretable to clinicians and regulators.
Scalable Infrastructure
Rare disease diagnosis, drug trial stratification, population genetics, all scaled off the same core AZKAR engine.
Scalable Infrastructure
Rare disease diagnosis, drug trial stratification, population genetics, all scaled off the same core AZKAR engine.
Scalable Infrastructure
Rare disease diagnosis, drug trial stratification, population genetics, all scaled off the same core AZKAR engine.
Seamless Integration, Supercharging Statistical Power with Immaculate Intent.
One Platform, Specialist Solutions.
One Platform, Specialist Solutions.
We built precise classifiers that integrate variant-level feature matrices with phenotype mappings. These models predict whether a genetic variant is likely pathogenic.
This layer predicts the probability of a patient being eligible for a specific trial. It fuses genotype-derived features with structured criteria from trial protocols. The output is a ranked list of trials, prioritizing those with the highest likelihood of inclusion.
Turning genomic signals into therapeutic strategy, AZKAR ingests large variant datasets, disease-specific cohorts, and functional annotations to automatically surface genes, pathways, and molecular mechanisms enriched in affected populations. Using multi-omic feature integration and model-agnostic statistical enrichment, the module identifies high-confidence therapeutic targets, ranks them by perturbation impact, and cross-references druggability, existing compounds, and structural biology databases.
Precision epidemiology at computational scale, this module enables real-time stratification and analysis across entire biobank-sized cohorts. AZKAR profiles variant burden, ancestry-adjusted allele frequencies, penetrance estimates, and polygenic modifiers to detect subpopulations with distinct disease trajectories or treatment responses. It supports cohort comparison, case–control association scans, founder-mutation detection, and longitudinal trend analysis.
We built precise classifiers that integrate variant-level feature matrices with phenotype mappings. These models predict whether a genetic variant is likely pathogenic.
This layer predicts the probability of a patient being eligible for a specific trial. It fuses genotype-derived features with structured criteria from trial protocols. The output is a ranked list of trials, prioritizing those with the highest likelihood of inclusion.
Turning genomic signals into therapeutic strategy, AZKAR ingests large variant datasets, disease-specific cohorts, and functional annotations to automatically surface genes, pathways, and molecular mechanisms enriched in affected populations.
Precision epidemiology at computational scale, this module enables real-time stratification and analysis across entire biobank-sized cohorts. It supports cohort comparison, case–control association scans, founder-mutation detection, and longitudinal trend analysis.
We built precise classifiers that integrate variant-level feature matrices with phenotype mappings. These models predict whether a genetic variant is likely pathogenic.
This layer predicts the probability of a patient being eligible for a specific trial. It fuses genotype-derived features with structured criteria from trial protocols. The output is a ranked list of trials, prioritizing those with the highest likelihood of inclusion.
Turning genomic signals into therapeutic strategy, AZKAR ingests large variant datasets, disease-specific cohorts, and functional annotations to automatically surface genes, pathways, and molecular mechanisms enriched in affected populations. Using multi-omic feature integration and model-agnostic statistical enrichment, the module identifies high-confidence therapeutic targets, ranks them by perturbation impact, and cross-references druggability, existing compounds, and structural biology databases.
Precision epidemiology at computational scale, this module enables real-time stratification and analysis across entire biobank-sized cohorts. AZKAR profiles variant burden, ancestry-adjusted allele frequencies, penetrance estimates, and polygenic modifiers to detect subpopulations with distinct disease trajectories or treatment responses. It supports cohort comparison, case–control association scans, founder-mutation detection, and longitudinal trend analysis.
Variant Analysis
Variant Analysis
Trial Targets
Trial Targets
Drug Discovery
Drug Discovery
Cohort Analytics
Cohort Analytics
Variant Analysis
Variant Analysis
Trial Targets
Trial Targets
Drug Discovery
Drug Discovery
Cohort Analytics
Cohort Analytics
Developer Mode. A first for the Industry.
Developer Mode. A first for the Industry.
Full-stack research sandbox built on the AZKAR engine. See below.
Full-stack research sandbox built on the AZKAR engine. See below.
AZKAR
Experimental
Developer Mode exposes a controlled environment for advanced users, data scientists, bioinformaticians, and translational researchers, to run custom models, upload experimental annotations, construct pipelines, and integrate external datasets. As well as modify, extend, or stress-test the AZKAR engine itself.
Rare Disease Boosting
Users can upload a small amount of high-confidence variants
AZKAR applies hierarchical bayesian calibration internally
The model becomes temporarily optimized for that disease area
API/SDK
Full suite of dedicated APIs
Python & R SDK bindings
For external research & pipeline integration
Custom Feature Engineering
Users can build and test their own features, from custom conservation scores to proprietary splice impact metrics
AZKAR automatically recomputes medians and shows performance impacts
Feature-level R&D coded within the UI
Rare Disease Boosting
Users can upload a small amount of high-confidence variants
AZKAR applies hierarchical bayesian calibration internally
The model becomes temporarily optimized for that disease area
API/SDK
Full suite of dedicated APIs
Python & R SDK bindings
For external research & pipeline integration
Custom Feature Engineering
Users can build and test their own features, from custom conservation scores to proprietary splice impact metrics
AZKAR automatically recomputes medians and shows performance impacts
Feature-level R&D coded within the UI
Model Slotting
Plug in external models
The sandbox runs side by side predictions and produces a conglomerate of evaluation metrics
ROC/AUPRC, confusion matrices, feature importance deltas, AZKAR becomes a benchmarking environment for your algorithmic models
Audit Trails
Users can upload a small amount of high-confidence variants
Every experiment is logged, from model versions to outputs to evaluation metrics
Crucial for GxP environments, clinical labs, and regulatory filings
Versioning Everything
Model version control
Feature-set version control
Dataset version control
Experiment snapshots
Model Slotting
Plug in external models
The sandbox runs side by side predictions and produces a conglomerate of evaluation metrics
ROC/AUPRC, confusion matrices, feature importance deltas, AZKAR becomes a benchmarking environment for your algorithmic models
Audit Trails
Every experiment is logged, from model versions to outputs to evaluation metrics
Anyone can re-run an experiment months later
Crucial for GxP environments, clinical labs, and regulatory filings
Versioning Everything
Model version control
Feature-set version control
Dataset version control
Experiment snapshots
And More
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Your Questions, Answered
Your Questions, Answered
Can I upload free text along with data for trial eligibility?
Can we run AZKAR inside our cloud?
How do we prevent model bias?
How is AZKAR priced across modules?
Can I upload free text along with data for trial eligibility?
Can we run AZKAR inside our cloud?
How do we prevent model bias?
How is AZKAR priced across modules?
Can I upload free text along with data for trial eligibility?
Can we run AZKAR inside our cloud?
How do we prevent model bias?
How is AZKAR priced across modules?
Can AZKAR integrate directly with hospital EMRs?
How long does onboarding take?
Can investigators see why a patient was ranked?
How can I contact support?
Can AZKAR integrate directly with hospital EMRs?
How long does onboarding take?
Can investigators see why a patient was ranked?
How can I contact support?
Can AZKAR integrate directly with hospital EMRs?
How long does onboarding take?
Can investigators see why a patient was ranked?
How can I contact support?