v1.0.3
Platform Boundaries
BHANUJ complements rather than replaces your AI runtime and deployment stack.
The Short Answer
BHANUJ provides a durable AI Governance Control Plane: it versions governed inputs, captures evaluation and execution evidence, reasons over policy and dependencies, and persists explainable decisions, assurance records, and lineage.
It is not a model-serving platform, prompt deployment tool, workflow engine, or replacement for enterprise GRC. Those systems can remain the source of runtime truth while BHANUJ records the governed evidence needed to review AI change.
Where It Fits
| System category | Primary responsibility | BHANUJ’s role |
|---|---|---|
| MLOps platforms | Train, package, deploy, and operate models. | Records governed asset versions, evaluation evidence, policy, and decision context around those activities. |
| LLM observability | Captures traces, prompts, latency, and production behavior. | Makes selected runtime evidence governable, explainable, and connected to deterministic decisions. |
| Prompt management | Authors, tests, and distributes prompt templates. | Can record managed or observed prompt versions, but does not become the prompt deployment system. |
| Workflow orchestration | Schedules and executes application or data workflows. | Runs durable governance jobs; it does not orchestrate a customer’s production workflow. |
| GRC systems | Manage organizational controls, risks, and broad compliance processes. | Provides technical evidence and decision records that a GRC workflow may reference. |
The Boundary Matters
Keep execution ownership explicit
A policy result can inform a promotion or approval workflow, but it does not deploy a model or alter application traffic. This keeps a decision reviewable and prevents governance state from silently becoming runtime control.Read How AI Governance Platform Works for the full platform model.