We deliver enterprise AI through custom model engineering, open-source LLM optimization, grounded RAG architectures, and production-grade MLOps. Our solutions span healthcare,
Domain-specific models engineered through supervised fine-tuning of open-source LLMs and proprietary architectures, optimized for precision-recall trade-offs, inference latency constraints, and production cost economics within regulated deployment contexts.
Neural architecture compression through knowledge distillation, post-training quantization, and structured pruning to achieve production-grade inference performance within stringent compute, memory, and power envelopes characteristic of edge and embedded deployments.
Retrieval-augmented generation architectures incorporating dense vector retrieval, hybrid search mechanisms, and provenance tracking to deliver verifiable, citation-backed outputs with systematically reduced hallucination rates and enhanced factual grounding.
Real-time perception pipelines engineered for object detection, multi-object tracking, semantic segmentation, and scene understanding with deterministic latency guarantees suitable for safety-critical autonomous vehicle, drone, and robotic platforms.
Heterogeneous sensor integration architectures combining camera, LiDAR, radar, and IMU modalities through early and late fusion strategies to achieve robust environmental perception across degraded visibility, adverse weather, and safety-critical operational scenarios.
Advanced driver assistance capabilities including lane-keeping assistance, collision mitigation, pedestrian detection, and adaptive cruise control engineered with functional safety rigor conforming to ISO 26262 ASIL-B/D requirements and regulatory homologation standards.
Automotive-qualified inference engines optimized for heterogeneous compute platforms including NVIDIA Drive, Qualcomm Snapdragon Ride, and domain-specific accelerators, achieving deterministic real-time performance within strict thermal, power, and functional safety constraints.
Clinical decision support systems for radiology, pathology, and diagnostic imaging workflows incorporating explainability mechanisms, regulatory-grade validation documentation, and HIPAA-aligned deployment architectures for privacy-preserving healthcare AI.
Production-grade vision systems for automated defect detection, dimensional metrology, anomaly identification, and quality assurance deployed on factory floor edge infrastructure with deterministic inference timing and industrial-grade reliability specifications.
Embedded vision systems enabling autonomous navigation, visual odometry, object tracking, and environmental mapping for unmanned aerial platforms operating in GPS-denied, bandwidth-constrained, and compute-limited operational envelopes.
End-to-end model governance infrastructure encompassing continuous integration, automated retraining pipelines, statistical drift detection, shadow deployment validation, and comprehensive observability for maintaining production model reliability and performance.
Comprehensive lifecycle safeguards for large language model deployments incorporating differential privacy techniques, adversarial robustness validation, output filtering mechanisms, and policy-aligned behavioral constraints for regulated enterprise environments.
HIPAA-compliant intelligent document processing for prior authorization workflows, clinical documentation analysis, claims adjudication automation, and member correspondence understanding with complete audit trails, explainability provisions, and regulatory alignment.
AI systems architected to satisfy HIPAA, ISO 26262, UN R155, SOC 2, and vertical-specific regulatory frameworks through comprehensive documentation workflows, traceability mechanisms, validation artifacts, and continuous compliance verification processes.
Multi-environment deployment orchestration spanning public cloud, private cloud, on-premise data centers, and edge infrastructure with unified security postures, compliance controls, scalability patterns, and operational monitoring aligned to enterprise governance requirements.