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Logistics • Active

Depot: Warehouse

AI-powered warehouse operations — inventory optimization, pick path planning, demand forecasting, and automated stock replenishment.

Live system: AI-powered warehouse management system that optimizes inventory levels, pick paths, and replenishment. Integrates with WMS and IoT sensors for real-time visibil…

+40%
Pick Efficiency
99.5%
Inventory Accuracy
70%
Stockout Reduction
30%
Labor Cost Savings
Free 20-min discovery + 48h ROI audit Production-grade, not a demo3 weeks to first value
✓
✓
✓
WMS integration live • Gradual rollout by warehouse zone • Borderline flagged
Why this vs status quo
Do nothing
+40% lost
Manual Logistics
Build in-house
6–12 months
Hire + maintain
Depot
99.5%
Weeks, not months
Inventory Optimization • Pick Path Planning
Save ~10×

What ships

  • Inventory Optimization — AI-driven stock level optimization with safety stock calcula…
  • Pick Path Planning — Optimized warehouse pick paths reducing travel time and impr…
  • Demand Forecasting — ML-powered demand prediction for inventory planning and stoc…
  • Stock Replenishment — Automated reorder triggers based on demand patterns and lead…
  • Warehouse Analytics — Real-time dashboards for inventory levels, turnover rates, a…
  • Space Optimization — AI-recommended slotting strategies to minimize travel and ma…

Human signs every critical step • Audit trail included

Sample output & architecture

Depot — Production Blueprint
Dated 18 Aug 2026 • #DEP-2026-041
Pick Efficiency: +40%Inventory Accuracy: 99.5%Stockout Reduction: 70%Logistics
Agents
Inventory Manager • Pick Optimizer • Demand Forecaster
Stack
Azure AI Foundry, Azure IoT Hub, Python, FastAPI
Workflow preview
1. Data Collection2. Demand Analysis3. Inventory Optimization4. Pick Path Generation
Data flow: WMS Data → Demand Forecasting → Inventory Optimization → Pick Planning → Replenishment Execution → Performance Tracking
Azure AI Foundry + LangChain • Azure AI Foundry hosts forecasting and optimization models. v1.0 • Audit trail

Why Azure AI Foundry + LangChain wins

Azure AI Foundry hosts forecasting and optimization models. LangChain orchestrates warehouse workflows with tool integration for WMS and IoT systems.

Alternatives: AWS Bedrock + LangGraph, Google Vertex AI + AutoGen

How Depot works — Real-time to first value

Data Collection → Demand Analysis → Inventory Optimization → delivery. Full 6-step trail collapses below.

01 • Real-time
Data Collection
Aggregate inventory, order, and warehouse data from WMS.
02 • < 1 hour
Demand Analysis
AI analyzes demand patterns and forecasts future requirements.
03 • < 30 minutes
Inventory Optimization
Calculate optimal stock levels and reorder points.
Full 6-step audit trail
1
Data Collection • Real-time
Aggregate inventory, order, and warehouse data from WMS.
2
Demand Analysis • < 1 hour
AI analyzes demand patterns and forecasts future requirements.
3
Inventory Optimization • < 30 minutes
Calculate optimal stock levels and reorder points.
4
Pick Path Generation • < 5 minutes
Generate optimized pick paths for pending orders.
5
Replenishment Execution • Automated
Trigger automated replenishment and monitor fulfillment.
6
Performance Tracking • Ongoing
Monitor KPIs and adjust strategies based on results.

Which Logistics use case is yours?

Tap your model — see the exact hinge we test.

Daily Operations

Optimize daily pick paths, replenishment, and inventory management.

Do nothing
+40% wasted
  • • Manual Logistics process
  • • No audit trail
  • • No evidence for inspector/investor
Build in-house
6–12 months • Hire team
  • • Hire + train + maintain
  • • No re-run if product changes
  • • Hard to show investor quickly
Depot — 99.5%
6 steps • Agents • Evidence
  • Inventory Optimization
  • Pick Path Planning
  • Daily watch + re-run

Multi-agent mesh — orchestrated

Live system presentation

Depot · Agent Mesh

ORCHESTRATORLogisticsInventory Manag…ACTIVEPick OptimizerNODE 02Demand Forecast…NODE 03Space PlannerNODE 04123456
Step 1Real-time

Data Collection

Aggregate inventory, order, and warehouse data from WMS.

WMS integrationOrder feedInventory countsHistorical data

Objections, answered

If it’s not answered, you’ll hesitate. So we answer it here.

AI-powered warehouse operations — inventory optimization, pick path planning, demand forecasting, and automated stock replenishment. Delivered as 4 specialized agents + 6-step workflow. You get Inventory Optimization + Pick Path Planning and a dated evidence trail.
Production in 3 weeks • Audit trail

Get Depot live — without the Logistics drag.

AI-powered warehouse operations — inventory optimization, pick path planning, demand forecasting, and automated stock replenishment.… Implementation: WMS Integration → Demand Engine → Optimization Layer → Production Launch.

Questions? Talk to a human • Responses in < 24h • Human signs every critical step

Evidence + audit trail•Azure AI Foundry + LangChain•Deterministic — LLM only for rationale