Lagrange.AI provides an artificial intelligence platform that utilizes process mining algorithms to automate data processing and optimize supply chain networks. The technology enables real-time monitoring and analysis, helping companies reduce operational costs by 30% and carbon footprints by 20% while enhancing network visibility by 80%.
Funding
$202.2K 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.
RAFounders
Product
Problem
Many companies struggle with supply chain uncertainties and disruptions, leading to inefficiencies, increased operational costs, and a larger carbon footprint. Integrating and processing data from disparate sources across the supply chain is often slow and complex, hindering real-time visibility and informed decision-making.
Solution
Lagrange.AI offers an AI-powered platform that automates data processing and optimizes supply chain networks, enabling companies to scan, monitor, and improve their operations. The platform uses advanced process mining algorithms to analyze distribution networks, identify areas for improvement, and provide real-time insights. By leveraging AI, companies can accelerate data integration, enhance network visibility, reduce operational expenses, and lower their carbon footprint. The no-code interface simplifies the process of gaining insights, even without specialized knowledge or programming skills.
Target Audience
The primary target audience includes supply chain directors, heads of operations, and other professionals in manufacturing, consumer goods, and related industries seeking to improve supply chain resilience, sustainability, and efficiency.
Features
- AI-powered process mining algorithms for automated data processing and analysis
- No-code platform interface for easy access to insights without programming skills
- Real-time monitoring of supply chain operations to quickly respond to changes and disruptions
- Support for various data formats, sizes, and sources for simplified data integration
- Simulation and optimization techniques to redesign distribution networks
- Identification of areas for improvement to reduce costs and carbon footprint