AI Agent

Inventory Management AI Agent

Never run out of stock or tie up cash in excess inventory

"Why are we out of stock again?" — the question that ends today. Optimal inventory levels with AI-powered demand forecasting.

Benefits

Perfect Inventory Levels Without the Guesswork

Manual inventory planning causes stockouts that lose sales or overstock that ties up cash. Spreadsheet forecasting ignores demand patterns. Reorder timing is reactive. This agent optimizes everything automatically.

95%

In-stock rate

40%

Inventory carrying cost

85%

Forecast accuracy

Agentic Flows

Complete Inventory Optimization

Automated workflow: demand forecasting with ML models, safety stock calculation by SKU, reorder point optimization, purchase order generation and routing, supplier order transmission, receiving and cycle counting.

How it works

AI-Powered Demand Forecasting

The agent analyzes historical sales patterns with ML forecasting, accounts for seasonality and trends automatically, calculates optimal reorder points by SKU, generates purchase orders when inventory hits thresholds.

Self-Learning Agents

The agent improves with every task, adapting to outcomes, applying feedback, and self-correcting using Constitutional AI.

Upto 98% Accuracy

As a result of constant feedback loops, Beam AI Agents refine their approach with every cycle, leading to 98% accuracy across flows.

Smart Model Switching

We call it ModelMesh. Each agent selects the right model for the task, balancing speed, accuracy, and cost in real time.

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Self-Learning Agents

The agent improves with every task, adapting to outcomes, applying feedback, and self-correcting using Constitutional AI.

Upto 98% Accuracy

As a result of constant feedback loops, Beam AI Agents refine their approach with every cycle, leading to 98% accuracy across flows.

Smart Model Switching

We call it ModelMesh. Each agent selects the right model for the task, balancing speed, accuracy, and cost in real time.

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Integrations

Connects to Your Operations Stack

Direct integration with NetSuite, SAP, Oracle, Microsoft Dynamics, Shopify, BigCommerce, and WMS platforms. Supplier connections via EDI, email, and portals. Real-time inventory visibility everywhere.

Implementation

Optimizing Inventory in 10 Days

Connect your ERP, WMS, and supplier systems. Configure lead times and service level targets. Most teams have automated reordering running within 2 weeks of project start.

Step 1

Agent Discovery

Conduct workshop(s) with relevant stakeholders to identify and prioritize use cases and map the requirements

Step 1

Agent Discovery

Conduct workshop(s) with relevant stakeholders to identify and prioritize use cases and map the requirements

Step 2

Agent Setup

Develop and launch your first agent with basic logic and integration (process scoping and recording, test dataset of 30-50 examples, baseline agent running and testing with target output mapping)

Step 2

Agent Setup

Develop and launch your first agent with basic logic and integration (process scoping and recording, test dataset of 30-50 examples, baseline agent running and testing with target output mapping)

Step 3

Agent Training

Test performance and gather feedback from process users (agent optimization up to 80% against expected output, variable-level accuracy measurement and optimization, integrations setup, and feedback API interfacing)

Step 3

Agent Training

Test performance and gather feedback from process users (agent optimization up to 80% against expected output, variable-level accuracy measurement and optimization, integrations setup, and feedback API interfacing)

Step 4

Agent Live

Extend to more workflows and client teams (live and continuous monitoring, human-in-the loop interfacing, node auto-tuning for automated prompt enhancements, >90% accuracy improvement, weekly business logic improvements)

Step 4

Agent Live

Extend to more workflows and client teams (live and continuous monitoring, human-in-the loop interfacing, node auto-tuning for automated prompt enhancements, >90% accuracy improvement, weekly business logic improvements)

Key Features

Smart Inventory Management

Demand Forecasting

ML models predict future demand by SKU. Accounts for trends, seasonality, and promotions.

Auto Reorder

Generates POs automatically when inventory hits reorder points. Sends to suppliers directly.

Multi-Location Optimization

Optimizes inventory across warehouses and stores. Suggests transfers to balance stock levels.

Demo

See Inventory Optimization in Action

Watch the agent forecast demand for 500 SKUs, identify 20 approaching stockouts, generate POs for optimal reorder quantities, and send orders to 5 suppliers automatically.