Nūl provides an Agentic AI platform for fashion supply chain inventory optimization. The system uses real-time analytics and predictive recommendations to automate demand planning, allocation, and replenishment cycles. This capability helps brands eliminate overstock, prevent stockouts, and maximize profitability across their operations.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Fashion brands often struggle with inaccurate demand forecasting and inefficient inventory management, leading to overproduction, excess waste, and lost revenue due to stockouts. Traditional methods rely heavily on spreadsheets and manual processes, resulting in errors and delayed decision-making across the supply chain. This misalignment between production and actual demand contributes to both financial losses and environmental concerns.
Solution
Nūl provides an AI-powered inventory optimization platform that helps fashion brands minimize waste and maximize profitability by improving demand forecasting and streamlining inventory allocation. The platform uses a multi-agent system, incorporating a fashion-specific large language model (LLM) to provide contextual intelligence. By integrating real-time data from various sources, including POS systems, ERP systems, and store visit data, Nūl offers actionable insights and automated recommendations for in-season adjustments, out-of-season planning, and comprehensive business intelligence. The system continuously learns from every transaction and interaction, refining its models to improve decision accuracy over time.
Target Audience
Nūl targets fashion brands, planners, merchandisers, and management teams seeking to reduce waste, optimize inventory, and improve financial outcomes through AI-driven decision-making.
Features
- Agentic AI architecture with a Multi-Agent Coordination Protocol (MCP²) for intelligent coordination and context-driven optimization
- Adaptable Data Fusion Engine that ingests data from POS systems, ERP systems, Excel sheets, and store visit data
- Model Context Protocol, a fashion-specific LLM that provides deep contextual intelligence to AI agents
- Demand Prediction Agent that uses machine learning to forecast SKU-level demand at store and category levels
- Stock Optimization Agent that identifies understocked and overstocked locations and suggests rebalancing
- Logistics Optimization Agent that determines the most cost-efficient store-to-store transfers and replenishments
- Integration with existing workflows to enable real-time execution of decisions, including store-to-store transfers, restocking triggers, and markdown recommendations