- Private Equity
- IA
- Business Case
Improve efficiency and productivity on summary and reporting tasks
Implementing AI solutions for employees' daily lives
The Customer
Private Equity player
Background and objectives
This use case relates to a core business activity that is highly time-consuming for an investment fund: the research, compilation and cross-referencing of documents enabling in-depth analysis of portfolio companies or acquisition targets.
Results
Employees can quickly obtain the information they need on a given subject by cross-referencing and synthesizing all available data sources, whatever their format: PDF, Excel, image, video...
- the solution allows widespread access to more professional data: no need to worry about where and how to search, no need to be satisfied with limited corpora.
- It also means more reliable data, thanks to the cross-referencing of sources.
- the solution enables better capitalization of knowledge (like an "augmented EDM") and improved employee productivity
Approach / System
We've developed a conversational chatbot - running on an LLM - , with which we interact in natural language, to facilitate and speed up these analyses.
It uses natural language processing (NLP) to understand and answer users' questions by combining keywords and context to determine the information to be searched for.
Once the user's intention has been identified, the chatbot uses search algorithms to find relevant documents in the database. It then extracts the desired information and returns it to the user, while specifying the sources and location of the information in the document(s) analyzed.
The chatbot is continuously trained through use and user feedback.
Added value Silamir Group
Beyond technological services, cover security, confidentiality, trust and compliance issues
Data security and confidentiality, both internally and externally: include authorization management in the chatbot solution, and ensure that users do not have access to information for which they are not authorized
Trust issue: the chatbot justifies its answer by citing its references, via a link to the documents from which it drew the information.
Legal issue: the chatbot is configured to render the text of source documents literally, without any interpretation or rewording, to avoid any risk of error or approximation.
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