The transformation of tender processes through AI demonstrates how technology can create a competitive advantage in the financial sector.
KEY FIGURES
50% increase in number of tenders processed
30% improvement in success rate
38 billion dollars in assets under management
ABOUT THE CLIENT
A global leader in asset management, iM Global Partner manages a portfolio of 38 billion dollars across 16 locations in Europe and the United States. Its specialization in strategic partnerships with independent management companies makes it a reference player in the international financial sector
CHALLENGE
In a sector as competitive as asset management, the ability to effectively respond to tenders is crucial. iM Global Partner faced a significant challenge: processing an increasing volume of multilingual documents while maintaining impeccable response quality. The precise extraction of questions and building a robust knowledge base from existing documents required an innovative solution capable of adapting to the specificities of the financial context.
SOLUTION
At the heart of the system, an intelligent matching process combines the power of embeddings for document vectorization with the capabilities of generative AI Mistral Nemo. This alliance automatically identifies the most relevant matches between tenders and financial products.
For RFP (Request For Proposal) processing, we implemented an intelligent RAG (Retrieval-Augmented Generation) system. This system deeply analyzes questionnaires, searches for relevant information in the knowledge base, and generates contextualized responses. The entire platform is deployed directly on iM Global Partner's infrastructure, thus ensuring the security and confidentiality of sensitive data.
Intelligent AI Matching
Use of embeddings for document vectorization
Automatic matching via generative AI (Mistral Nemo)
Enriched and contextualized knowledge base
Automated RFP Processing
Analysis of tender questionnaires
RAG (Retrieval-Augmented Generation) system for relevant information search
Generation of contextualized responses
Security and Compliance
On-premise deployment on client infrastructure
Protection of sensitive data
Complete control over information processing
EXAMPLE PROMPT TO NORMALIZE RFPs
The strength of the following prompt lies in its combination of different prompting techniques, creating a particularly effective tool for analyzing financial tenders. At its core, the design includes few-shot learning which uses a concrete case to illustrate exactly the expected format. This approach is reinforced by methodical structuring in steps, following a chain-of-thought principle that guides the analysis in a logical and sequential manner. The prompt also relies on a system of explicit constraints and precise specification of the JSON output format, ensuring standardized and easily exploitable results. The whole is framed by a clear definition of role and context, ensuring that the analysis always remains aligned with the specific objectives of financial tenders.
This multifaceted approach thus creates a robust and precise system, capable of efficiently processing tender responses while maintaining a high level of standardization and reproducibility, essential elements in the financial sector:
Objective
Read and analyze documents to identify key investment parameters according to four main categories:
Asset Classes
Investment Styles
Capitalization Categories
Geographic Focus
Analysis Steps
Asset Classes
Examine references to asset classes
Note all identified asset classes
Must match the list of specified values
Investment Styles
Examine references to investment strategies
Note all identified styles
Must match the list of specified values
Capitalization Categories
Examine references to market capitalization
Note all identified categories
Must match the list of specified values
Geographic Focus
Examine references to geographic regions
Note all identified locations
Must match the list of specified values
Optional Parameters
Identify optional preferences or acceptable alternatives
Note items that are:
Preferred but not mandatory
Acceptable but not priority
Permitted as secondary options
Categorize optional items within the four main categories
Handling Ambiguities
Ignore any unclear or ambiguous mentions
Include only clearly identifiable items
Output Format
The result must be formatted in JSON with the following categories: