Reasoning Layer
Model capability
Scientific language understanding for literature and internal note interpretation
Architecture
Built for controlled scientific decision support in enterprise environments.
The architecture prioritizes traceability, scoped automation, and integration with existing research systems over all-at-once replacement. NeuroForg is an application-layer platform, not a standalone cloud provider.
Reasoning Layer
Scientific reasoning layer
Model behavior is configured around workflow stage, data policy, and human review requirements.
Reasoning Layer
Model capability
Task-specific reasoning chains for hypothesis and experiment planning
Reasoning Layer
Model capability
Configurable model routing based on workflow stage and data policy
Execution Layer
Orchestration and simulation workflow
Task orchestration is designed for observable handoffs between teams and systems.
Orchestration
Workflow orchestration layer for multi-step scientific tasks
Coordinates workflow steps and review checkpoints across technical teams.
Orchestration
Queue-based execution with observable status and handoff checkpoints
Coordinates workflow steps and review checkpoints across technical teams.
Orchestration
Human review gates before high-impact recommendations
Coordinates workflow steps and review checkpoints across technical teams.
Simulation Workflow
Simulation request management with objective-driven run definitions
Connects simulation planning and output review to downstream decision workflows.
Simulation Workflow
AI-driven simulation workflows powered by GPU infrastructure
Connects simulation planning and output review to downstream decision workflows.
Simulation Workflow
Iteration planning support based on observed outcomes
Connects simulation planning and output review to downstream decision workflows.
Deployment
Deployment and integration model
Integrate NeuroForg into existing R&D pipelines via API access while preserving organization-approved infrastructure and controls.
Deployment Model
Deployable in enterprise-approved cloud environments
Configured to match enterprise security, governance, and operations requirements.
Deployment Model
Role-aware access controls with traceable activity logs
Configured to match enterprise security, governance, and operations requirements.
Deployment Model
Integrate NeuroForg into existing R&D pipelines via API access
Configured to match enterprise security, governance, and operations requirements.
Infrastructure & Performance
NVIDIA-aligned performance stack
Built on NVIDIA GPU infrastructure, NeuroForg supports CUDA-accelerated simulations and Triton-based multi-agent coordination.
Infrastructure
Built on NVIDIA GPU infrastructure
NeuroForg workloads are designed to run on high-performance NVIDIA GPU environments already used by enterprise research teams.
Simulation
CUDA-accelerated scientific simulations
The platform supports AI-driven simulation workflows powered by CUDA-accelerated compute pathways.
Coordination
Triton-based multi-agent runtime
NVIDIA Triton Inference Server is used for real-time model serving and multi-agent coordination in scientific workloads.
Boundaries
Architecture boundaries and controls
Clear boundaries reduce risk and support trust during evaluation and rollout.
Scope
Decision support, not autonomous lab execution
Recommendations are designed to support scientists, with human oversight at critical decision points.
Data
Controlled data access by design
Implementations are configured around organization-specific data boundaries and access policy.
Reliability
Traceability over black-box outputs
Outputs are attached to workflow context and assumptions to support review and reproducibility.
Rollout
Pilot before scale
Teams validate operational fit in a bounded pilot before expanding usage.
Architecture Review
Review architecture fit with your existing stack
A practical integration review covers data boundaries, workflow ownership, and rollout sequencing.
A review can align proposed architecture choices with security, platform, and program constraints.