Imagine a digital assistant that reads every single email, meeting note, and report for a massive construction site. It looks for 'weak signals'—small warnings that something is going wrong—long before they become disasters. It's like having a super-powered alarm system that tells you a project is drifting off track before you lose millions in lawsuits.
Large-scale projects often fail to detect early warning signs in documentation, leading to aggravated issues and massive financial penalties.
A Big Data platform with NLP/ML pipelines and Microsoft 365 connectors for real-time analysis of unstructured project documents.
If you are an infrastructure developer dealing with projects over €100 million — this project developed an AI monitoring tool that detects risks in real-time from emails and reports. This prevents the aggravation of issues that lead to massive financial penalties.
If you are a waste management operator dealing with complex engineering builds — this project developed a Big Data platform that analyzes unstructured text. It helps you identify problems early to avoid claims that average €54 million globally.
If you are a law firm dealing with claims litigation for large projects — this project developed NLP technology that extracts actionable insights from project documentation. This allows for better-documented claims and more efficient litigation processes.
Based on available project data, specific pricing details for the Lili.AI solution are not provided.
Yes, the project delivered a Big Data platform designed for distributed, parallel execution on large datasets using a scalable cloud-agnostic infrastructure.
Based on available project data, the specific licensing terms are not mentioned, but the technology is developed by an SME (LILI.AI).
The system includes developed connectors specifically for enterprise systems, with a focus on Microsoft 365 integration.
The project period runs from 2022-06-01 to 2025-05-31, with the goal of reaching TRL9.
The project is led by a single French SME, LILI.AI, representing a 100% industry ratio. This lean structure suggests a highly focused commercial drive, moving from TRL6 to TRL9 without the overhead of academic partners, focusing directly on industrial application and deployment.
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