System Overview

All datasets (genomic sequences, assay outputs, trial results, production metrics, etc.) are harmonized into a common ontology. This gives researchers, clinicians, and operators a shared environment capable of powering advanced analytics, model training, and digital-twin simulations.

As the system aggregates data, Echo’s intelligence layer becomes increasingly predictive, forming the backbone for autonomous scientific operations driven by AI models trained directly on the unified data layer.

Core Layers

Data Integration Continuous ingestion from lab instruments, clinical systems, and ERP platforms. This stream becomes the training feed for scientific models.

Semantic Ontology A unified graph that contextualizes all biological, clinical, and manufacturing entities — essential for model interpretability and robotic execution.

Governance Privacy, lineage, and access control built in at the architectural level.

Analytics Secure workspaces for computation, enabling scientists to develop models directly on harmonized data.

Knowledge Store Version-controlled datasets and models, creating a memory system for autonomous scientific agents.

Simulation Engine Digital twins that predict biological behavior and complex research scenarios. This provides the simulation environment that supports model guided experimentation and future robotic execution.