We don’t have a fixed definition of major projects. The key questions are:

– Does the project have the potential to have a significant impact (positive or negative) on your company’s margin? 

– Does the project have specifics that make it riskier: no dedicated Contract Manager, significant delay penalty, short foreclosure time to submit pre-claims, unclear project scope, multiple changes that have not been documented, etc? 

– Do you have written data? If so, how much? Can you gather it? 

Currently, Lili.ai serves the construction, energy, transportation and defense industries. 

Claim Management is an unavoidable process for large scale projects, as they are often discrepancies between what was initially planned and the reality we are faced with (unexpected complexity). The claims process consists of asking for additional remuneration / compensation. Depending on the position of the other party, it can go as far as litigation. 

More and more, especially with the proliferation of NEC and FIDIC contracts, disputes are being dealt with on an ongoing basis, as problems are often cheaper and easier to resolve when detected at an early stage. There is an undeniable link between early resolution of disputes and limited budget overruns.

Detecting problems and making informed decisions at an early stage is an effective way to avoid billions in losses, time-consuming litigation and irreversible stalemates.

With Lili.ai you can quickly find the reason of the disruption and faster produce a robust analysis of the situation. 

We have been analyzing the wordings and documents of the major projects for 5 years; which gives us a unique understanding of major projects terminologies, key concepts and relationships between those. We have created many in-house tools (ontologies, dictionaries, engines, …) that enables us to jump start like no other team and deliver results in less than one week. 

Think of Lili.ai as a tool that learns to identify occurrences of issues related to major project (delay, cost overrun, modification, co-activity, …) by working with different teams and different domains. In the end, projects will become more efficient at detecting issues; and that’s is the end goal of Lili.ai. 

Absolutely, Lili.ai does not intend to replace the project team. However, there is no doubt that machines are better than humans for certain tasks: scanning millions of documents simultaneously to find out which ones are the most similar; suggesting how to refine your query in order to get more results; suggesting (without having lived the project) the most relevant events to read. 

Lili.ai aims to make experts more efficient in their work while reducing the amount of documentation to be read. 

E-discovery tools are generalist tools that are used by specialized lawyers and consultants when reviewing massive data sets. Yet, these tools are very expensive, have no domain knowledge and are not capitalizabble to detect regular problems as they arise. 

Lili.ai’s mission is to disrupt the management of large projects with a semantic search engine as a first step. The goal is to train the machine to monitor written documentation to detect recurring problems as early as possible. Eventually, the goal is to offer the tools and support needed to create a standardized historical database, which will be used for predictive purpose. Lili.ai has already a few tools available for this purpose.

Absolutely, we can start by using existing data. However, there is no doubt that to unlock additional levels of intelligence, there will be a need to change existing habits and processes. 

It only takes us a few days after receiving the data to make the application available to you. The return on investment is immediate because each of your searches will take only a few seconds thanks to the pre-trained templates, the intelligent filter system and the intuitive interfaces.