Supply Chain & Logistics
The Challenge
Supply chain teams juggle inventory, orders and warehouse operations across systems that rarely talk to each other. Getting a current answer (what is in stock, where, and what to reorder) often means checking several tools by hand, and warehouse routing is still planned with fixed rules.
How NueralSoft Solves It
We connect AI agents to your inventory and supply chain tools through the Model Context Protocol (MCP), so teams can ask questions and trigger actions in plain language. We run these applications with LLMOps practices for evaluation and monitoring, and use reinforcement learning to optimise warehouse robot routes.
Solutions
Working reference implementations across agentic AI, generative AI and machine learning, each available for a private demonstration.
MCP Supply Chain Agent
A conversational agent that reads and updates inventory through Model Context Protocol (MCP) tools, giving real-time inventory visibility and coordinating supply chain tasks from plain-language requests.
Supply Chain LLMOps
A generative AI application for supply chain workflows, built with LLMOps practices for evaluation, deployment and ongoing monitoring so it stays reliable after launch.
Robotic Warehouse Navigation
Reinforcement-learning policies for warehouse robots: path planning, obstacle-aware routing and multi-robot scenarios, with route comparison and operational ROI analysis.
Built for Operations
- GroundedAnswers and actions use your live inventory and system data.
- People approveAgents propose changes; your team confirms them.
- Measured qualityEvaluation and monitoring after launch, not just before.
- Your controlsYour security, access and data controls in production.
See it working on your operations
Every solution on this page is available for a private demonstration.
Request DemoSolutions shown are NueralSoft reference implementations, adapted to each client's data, systems and controls. Private demonstrations available on request.