Hello NVIDIA Developer Community,
I’m sharing the current architecture of BPM RED Academy HumAI MightHub — a governance-native AI Factory framework designed to orchestrate data, human judgment, AI inference, and enterprise infrastructure into one auditable operating model.
This is not a product announcement.
It is a public technical disclosure of an architecture we are actively developing across regulated use cases, starting with:
- FinC2E — governance-native AML/KYC review-chain and compliance engine
- HumAI TB&C — Track & Board / effort-based credentialing / human performance Digital Twin
Core design principle:
A model produces predictions.
An AI Factory produces decisions that are allowed to exist.
The architecture is built around:
- governed inference workflows
- human-in-the-loop and human-on-the-loop control
- deterministic orchestration logic
- audit-ready outputs
- model and data governance
- multi-cloud and on-prem/hybrid deployment paths
- NVIDIA-aligned infrastructure concepts, including GPU infrastructure, NVIDIA AI Enterprise software, NGC model/artifact management, and future alignment with Mission Control / BCM-style orchestration
The attached diagram shows the current system view:
- Enterprise users: compliance officers, analysts, operations teams, executives, auditors/regulators
- Orchestration layer: workload scheduling, GPU/resource management, policy governance, observability, audit dashboards
- Intelligence layer: proprietary models, open models, RAG/knowledge services, evaluation and guardrails
- Data & workflow layer: collection, preparation, annotation, HITL review, decision engine, feedback loop
- Infrastructure layer: NVIDIA GPU infrastructure, AI Enterprise software, NGC registry, security/IAM/compliance
- Cloud/platform layer: Microsoft Azure, Google Cloud, NVIDIA DGX Cloud where applicable, and on-prem/hybrid deployment
The immediate objective is not autonomous decision-making.
The objective is governed decision support where:
- inference operates inside policy boundaries
- human authority is preserved
- auditability is produced by default
- non-compliant or incomplete outputs are escalated
- regulated organizations can understand not only what was produced, but how, why, and under whose review
I would be interested to hear from developers, architects, and teams working on:
- regulated AI workloads
- inference-time governance
- NIM / TensorRT / NVIDIA AI Enterprise pipelines
- human-in-the-loop orchestration
- auditability and decision traceability
- AI Factory design patterns for finance, defense, healthcare, and compliance environments
Open to technical feedback, architecture critique, and potential collaboration around pilot deployments or infrastructure alignment.
Best regards,
Edin Vučelj
BPM RED Academy