Infrastructure
Built on NVIDIA GPU infrastructure
NeuroForg workloads are designed to run on NVIDIA GPU environments already used by enterprise research teams.
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.
Control model
Human review gates
Data stance
Policy-aligned access
Orchestration
Observable workflow steps
Rollout
Pilot before scale
1. Scientific reasoning layer
Model behavior is configured around workflow stage, data policy, and review requirements rather than a single generic prompt-response pattern.
Scientific language models
Fine-tuned for interpreting research literature, internal notes, and domain-specific nomenclature instead of generic chatbot responses.
Task-specific reasoning
Different workflow stages use different reasoning approaches. Hypothesis generation, experiment design, and result interpretation each get purpose-built chains.
Configurable model routing
Data sensitivity and workflow stage determine which models handle each task, so teams can keep internal data within approved boundaries.
2. Workflow orchestration layer
Scientific workflows are managed as observable, multi-step processes with explicit checkpoints and team accountability.
Multi-step workflow engine
Scientific tasks rarely fit a single prompt-response pattern. The orchestration layer manages multi-step processes with observable checkpoints.
Queue-based execution
Every task has a visible status. Team leads can see what's running, what's waiting, and where things are blocked.
Human review gates
Before any high-stakes recommendation becomes action, a qualified human reviews it. The platform flags uncertainty and surfaces context.
3. Simulation and compute layer
Simulations are tied to explicit objectives and feed back into the next decision cycle with documented outcomes.
Objective-driven simulation
Define what question a simulation should answer before it runs. Results are evaluated against pre-set acceptance criteria.
GPU-accelerated compute
Workloads run on your existing NVIDIA GPU infrastructure. We orchestrate; you own the hardware and the data.
Outcome-based iteration
Simulation results feed directly into the next planning cycle with structured summaries of what worked and what to try next.
4. Integration and deployment layer
Deploy inside existing enterprise environments and connect through APIs so adoption can be staged without disrupting mature workflows.
Your cloud, your rules
Deploy inside your enterprise environment so data stays where your security team requires.
Role-based access
Computational scientists, lab leads, and program managers each see what's relevant. Every action is logged for audit.
API-first integration
Connect NeuroForg to your existing pipelines, databases, and tools. We're designed to slot in, not take over.
Infrastructure & Performance
NVIDIA-aligned performance stack
Built on NVIDIA GPU environments, NeuroForg supports CUDA-accelerated simulations and Triton-based model serving patterns.
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.
5. Architecture boundaries and controls
Clear boundaries reduce adoption risk and help teams evaluate trust, ownership, and operational fit before scaling.
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 architecture choices with security, platform, and program constraints.