Warehouses have never been more automated, more data-rich, or more operationally demanding than they are now—yet they still rely on systems that can’t keep up. Throughput is rising, SLAs are shrinking, and fleets of AMRs, conveyors, and sensors expand every year. But beneath that technological surface, most sites still rely on a familiar trio: a Warehouse Management System (WMS), a handful of dashboards, and the institutional knowledge available.
Supervisors are left to manage 12+ classes of equipment, thousands of shift tasks, and a constant flood of telemetry—without any unified intelligence to interpret it all or guide the next move.
This post introduces the NVIDIA Multi-Agent Intelligent Warehouse (MAIW) Blueprint for the missing layer. This NVIDIA-aligned, open source AI command layer sits above WMS, Enterprise Resources Planning (ERP), and IoT infrastructure to transform scattered data into real-time, actionable operational intelligence.
The problem: Warehouses without a “brain”
Despite years of investment in WMS and ERP systems, automation fleets, safety hardware, RFID, scanners, cameras, dashboards, and BI tools, most warehouses still lack one critical capability: a system that can reason across all of it.
Operational knowledge remains scattered. SOPs, SDS sheets, LOTO procedures, and OEM manuals sit in dense PDFs. WMS, ERP, LMS, maintenance, and incident systems all hold different pieces of the puzzle. Telemetry from PLCs, AMRs, IoT sensors, and charging stations streams in continuously but stays disconnected. And individuals often retain the most valuable insights such as shift notes, context, and other institutional knowledge.
On a routine day, this fragmentation creates friction. But during peak volume, equipment failures, or safety events, it becomes a real liability. Maintenance teams troubleshoot with incomplete telemetry. Supervisors assign tasks without a unified view of staffing, equipment status, or workload. Safety alerts go unnoticed, incidents are under-reported, and procedures stay buried in PDFs no one has time to read.
The result is predictable: more downtime, inefficient tasking, slow problem resolution, safety gaps, and expensive automation that operates as isolated islands rather than a coordinated system.
Warehouses don’t need more dashboards—they need a real-time decision layer that can understand natural-language questions, pull evidence from data and documents, coordinate specialized agents, recommend actions with justification, and operate under strict safety and compliance guardrails. That is the role of an AI command layer.
The solution: An AI command layer
The Multi-Agent Intelligent Warehouse delivers a unified AI command layer for modern warehouse operations, transforming fragmented systems, documents, and telemetry into real-time, actionable intelligence. By orchestrating specialized AI agents across equipment operations, workforce coordination, safety, forecasting, and document intelligence, the platform enables warehouses to move from reactive management to proactive and adaptive decision-making.
- Unified warehouse intelligence: Connects WMS, ERP, IoT, documents, and telemetry into a single AI-driven operational view.
- Faster, explainable decisions: Multi-agent AI delivers real-time, evidence-backed recommendations operators can trust.
- Higher throughput, less downtime: Proactively optimizes labor, equipment, and maintenance to reduce disruptions.
- Safer, more compliant operations: Continuously monitors incidents, SOPs, and environmental signals to improve safety response.
- Foundation for physical AI: Enables the transition from reactive workflows to perception-driven, autonomous warehouse operations.
Design goal: An AI assistant for the entire warehouse
The goal behind MAIW is to build a production-grade reference system that:
- Demonstrates how the NVIDIA AI stack (including NVIDIA NIM, NVIDIA NeMo, NVIDIA cuML, and NVIDIA cuVS) can power an operational assistant.
- Provides a multi-agent architecture that mirrors warehouse roles: Equipment, Operations, Safety, Forecasting, Document Processing.
- Unifies retrieval-augmented generation (RAG), forecasting, and document AI into a single workflow.
- Ships with real security, monitoring, and guardrails, not just a prototype chatbot.
- Is open source and extensible, so customers and partners can adapt it to their own environments.
The MAIW is a complete system with API, UI, agents, connectors, observability, and deployment assets.
MAIW core technology stack
MAIW is built end-to-end on the NVIDIA AI Enterprise platform. It is powered end-to-end by NVIDIA AI Enterprise applications, combining advanced language models, fast retrieval, document intelligence, and GPU-accelerated analytics in one cohesive system.
At the reasoning layer, LLM NIM drives the assistant’s intelligence: Llama 3.3 Nemotron Super 49B handles complex operational decision-making, while NVIDIA Nemotron Nano 12B v2 VL adds vision-language understanding for documents and images. Outputs are grounded by a high-performance retrieval layer built on Llama Nemotron Embed QA 1B and Milvus with cuVS, enabling fast, GPU-accelerated vector search.
For documents, a streamlined NeMo Retriever pipeline performs OCR, normalization, extraction, validation, and indexing—turning PDFs, images, and multi-page BOLs or invoices into structured data that the system can reason through.
All data flows through a hybrid RAG architecture. Structured telemetry lives in PostgreSQL/TimescaleDB, unstructured content is handled through vector search, and a hybrid router chooses the best strategy for each query. Redis caching keeps responses consistently under a second.
Forecasting is powered by a NVIDIA cuML-accelerated ensemble of six models, tuned with Optuna and achieving strong performance (~82% accuracy, 15.8% MAPE).
It’s all wrapped in a production-grade application stack:
- FastAPI backend
- React frontend
- Full Prometheus and Grafana observability
- NVIDIA NeMo Guardrails to ensure safe, compliant behavior across all interactions
How the multi-agent intelligence layer thinks and works
MAIW isn’t a single assistant—it’s a coordinated team of specialized AI agents, each trained to handle a different part of warehouse operations. LangGraph choreographs how they work together, while the Model Context Protocol (MCP) gives them a shared layer for tool access, external system calls, and real-time data retrieval.
A user’s query passes through guardrails, intent routing, memory lookup, retrieval, and tool execution before returning a safe, grounded answer. The full workflow shown in Figure 2 captures how these pieces come together.
| Agent | Actions |
| Planner and general | Routes intent, breaks tasks into steps, and selects the right agents; handles simple queries directly |
| Equipment and asset ops | Tracks and manages forklifts, AMRs, and conveyors; checks telemetry, maintenance, and utilization |
| Operations coordination | Manages tasks, waves, staffing, and KPIs; diagnoses bottlenecks and executes fixes |
| Safety and compliance | Enforces SOPs and regulations; handles incidents, checklists, and alerts |
| Forecasting | Predicts demand and stockout risk; generates and pushes replenishment recommendations |
| Document processing | Runs OCR and extraction on BOLs, invoices, and receipts; indexes structured results for retrieval |
MAIW core AI services
MAIW core AI services include intelligent document processing, safety, security, and observability.
Intelligent document processing
The intelligent document processing pipeline uses NVIDIA NIM and multimodal foundation models with quality-based orchestration to deliver enterprise-grade accuracy at scale. Documents are ingested and preprocessed with NeMo Retriever, then processed through Intelligent OCR and layout extraction using NeMoRetriever-OCR and Nemotron Parse to produce structured, high-fidelity representations. A small vision-language model (Nemotron Nano 12B VL) performs visually grounded field extraction and document classification, with post-processing normalization into schema-compliant JSON.
Embeddings generated with a NeMo Retriever embedding model are indexed in Milvus to enable semantic search and downstream RAG. For high-value or low-confidence cases, a large language model (LLM) judge validates consistency, accuracy, and completeness, scoring extraction quality. An intelligent routing layer then automatically decides whether documents are auto-accepted, flagged for quick review, sent for expert review, or rejected for reprocessing—optimizing cost, latency, and accuracy while maintaining a continuous feedback loop for system improvement.
This feedback loop is anchored around the LLM judge and intelligent routing stages. After initial extraction by the small vision-language model, the LLM judge evaluates each document for consistency, completeness, and confidence, producing scored results and quality explanations. These scores drive the routing engine, which determines whether a document is auto-accepted, sent for lightweight human review, escalated to expert review, or rejected for reprocessing.
When documents are corrected—either automatically or by human reviewers—the validated outputs are fed back into the system as normalized and scored metadata, updating the document store, embedding index, and quality signals. Low-confidence or rejected documents are rerouted to earlier stages (OCR, layout extraction, or small LLM processing), enabling targeted reprocessing rather than full pipeline reruns. Over time, this closed-loop flow continuously improves extraction accuracy, routing thresholds, prompt strategies, and model selection policies, allowing the system to adapt dynamically while minimizing cost and latency at scale.
Safety, security, and observability
An AI command layer only works if operators trust it. MAIW is built with that principle at the foundation.
Keeping every interaction safe with NeMo Guardrails
The NeMo Guardrails implementation uses a dual approach: the NeMo Guardrails library (v0.19.0) with Colang for programmable guardrails, and a pattern-based fallback for reliability.
The GuardrailsService (src/api/services/guardrails/guardrails_service.py) selects the implementation through the USE_NEMO_GUARDRAILS_SDK environment variable, with automatic fallback if the library is unavailable.
When library mode is enabled, the NeMoGuardrailsSDKService wrapper initializes LLMRails from a Colang configuration (data/config/guardrails/rails.co) that defines 88 protection patterns across five categories: jailbreak detection (17 patterns), safety violations (13 patterns), security violations (15 patterns), compliance violations (12 patterns), and off-topic queries (13 patterns).
The library uses NVIDIA NIM endpoints (configured in data/config/guardrails/config.yml) with OpenAI-compatible models, and input safety checks are performed by calling rails.generate_async and detecting refusal responses:
# SDK Input Safety Check
result = await self.rails.generate_async(
messages=[{"role": "user", "content": user_input}]
)
is_safe = not self._is_refusal_response(result.content)
Security model: Controlled access by design
The JSON Web Tokens (JWT) implementation (src/api/services/auth/jwt_handler.py) provides stateless authentication with HS256 tokens that include user identity and role information, with key strength validation (32-byte minimum) to address CVE-2025-45768. This foundation enables role-based access control (RBAC) through the CurrentUser context class and FastAPI dependency injection, where tokens are validated for signature, expiration, and type, then decoded to extract user roles and permissions.
The system maps granular permissions (INVENTORY_WRITE, OPERATIONS_ASSIGN, SAFETY_APPROVE, and so on) to five role levels (ADMIN, MANAGER, SUPERVISOR, OPERATOR, VIEWER), allowing declarative endpoint protection through require_permission and require_role dependencies:
# JWT token with role → RBAC enforcement
user_data = {"sub": str(user.id), "role": user.role.value}
access_token = jwt_handler.create_access_token(user_data)
@router.get("/admin/endpoint")
async def admin_endpoint(user: CurrentUser = Depends(require_admin)):
# Only SYSTEM_ADMIN permission holders can access
Observability: MAIW as essential production infrastructure
Prometheus and Grafana provide real-time visibility into how the system behaves: API latency, vector search performance, cache efficiency, agent response times, forecasting accuracy, and even equipment telemetry. By instrumenting MAIW like any critical warehouse service, SRE and operations teams can monitor, debug, and improve the AI layer with confidence.
Get started with the Multi-Agent Intelligent Warehouse
There are two methods to get started with MAIW:
- Create a Brev instance
- Visit the GitHub repo at NVIDIA-AI-Blueprints/Multi-Agent-Intelligent-Warehouse
The GitHub repo is structured as a complete, runnable reference implementation:
- Backend: FastAPI services, retrieval stack, memory, adapters, guardrails
- Frontend: React dashboard with chat, forecasting, and monitoring views
- Infrastructure: Docker Compose, Helm charts, and setup scripts
- Data and scripts: SQL schemas, demo data, forecasting pipelines, document pipelines
- Docs: Architecture notes, MCP integration details, forecasting docs, deployment guide, PRD
The following is a typical local setup:
git clone https://github.com/T-DevH/Multi-Agent-Intelligent-Warehouse.git
cd Multi-Agent-Intelligent-Warehouse
# Environment and infrastructure
./scripts/setup/check_node_version.sh
./scripts/setup/setup_environment.sh
cp .env.example deploy/compose/.env
./scripts/setup/dev_up.sh
# Initialize database & demo data
source env/bin/activate
python scripts/setup/create_default_users.py
python scripts/data/quick_demo_data.py
python scripts/data/generate_historical_demand.py
# Start services
./scripts/start_server.sh # API (http://localhost:8001)
cd src/ui/web && npm install && npm start # Frontend (http://localhost:3001)
Transforming warehouse complexity into control
Supply chains are becoming more volatile, more automated, and more data-rich—and warehouses are a key part of supply chains. The current stack—WMS plus dashboards plus human heroics—cannot scale indefinitely.
An AI command layer provides a path forward, including:
- A single operational “brain” that can reason across systems
- Explainable recommendations instead of opaque heuristics
- Faster incident response with better evidence
- Safer operations with codified guardrails
- Better use of existing automation and data investments
The Multi-Agent Intelligent Warehouse is a working, open source implementation of that command layer, built on the NVIDIA AI platform and aligned with the broader NVIDIA blueprint strategy.
If warehouses are already operating at the edge of complexity, MAIW shows how to pull them back—from reactively managing challenges to proactive, data-driven, AI-assisted operations.
Learn more about the Multi-Agent Intelligent Warehouse.
