Our team engineered multimodal modeling frameworks used by a top global clinical research institution.
These architectures integrate genomic-scale embeddings, biological signals, clinical context, and longitudinal data
into unified risk inference systems built for translational science.
Features include:
• stability under drift
• robust uncertainty quantification
• cross-cohort generalization
• reproducible scientific workflows
We design Databricks-native ML systems for a Fortune 100 government analytics contractor,
supporting nationwide regulatory and operational programs with large-scale model pipelines,
schema intelligence, statistical validation, and governance.
Capabilities:
• multi-stage feature engineering
• automated schema intelligence
• audit and compliance trails
• real-time drift & anomaly intelligence
Artificia AI developed forecasting and resource optimization models for a major U.S. utilities provider. Models combine consumption forecasting, reliability modeling, environmental risk, and supply-chain variance to power high-stakes infrastructure decisions.
Our next-generation behavioral engine detects:
• synthetic identity patterns
• automated evasion systems
• zero-day behavioral anomalies
• long-horizon identity drift
Powered by:
• high-resolution behavioral embeddings
• adversarial vector modeling
• probabilistic anomaly fields
• cross-context signature fusion
Our reasoning systems unify structured data, documents, telemetry, sensor traces, audio, and temporal inference
into a single multimodal reasoning engine with:
• cross-domain understanding
• autonomous workflow navigation
• counterfactual consistency checking
• rigorous uncertainty quantification