How a Multi-Agent AI
AI System
Analyzes 5,000 Investment
Opportunities in Hours
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Client name
Investment Fund
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Industry
Private Equity
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Location
Europe
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Size
500+
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Duration
October 2023 – present
A major European private equity fund that manages assets worth billions of euros in the healthcare, technology, financial, and industrial services sectors. It invests in Northern Europe, focusing on operational improvements and the long-term growth of its portfolio companies.
Challenges
Every year, the team reviews thousands of potential deals. Each one requires a thorough market analysis and an assessment against dozens of criteria. This is where the core problems emerged.
Time-Consuming Routine
Analysts spent approximately 60 hours per week on market monitoring. Of these, 15-20 hours were spent solely on reviewing news, company earnings reports, and press releases. A valuation could take anywhere from 2 days to 4 weeks, depending on data availability.
Inconsistent Valuation
Different analysts applied different approaches. What one considered a strong opportunity, another would not even view as a viable option. Comparing deals in the pipeline proved nearly impossible due to the lack of a unified methodology.
Fragmented Data in Heterogeneous Sources
Critical information was scattered across different systems – document repositories, CRM, email, Excel spreadsheets, Bloomberg terminals, and financial databases.
Slow Decision-Making
The vast amounts of data that had to be processed manually led to significant delays in decision-making. The team evaluated fewer deals per year, despite constant pressure to increase the deal flow.
Solution
We have created an artificial intelligence platform that goes beyond simply automating tasks to fully enhance the investment deal sourcing process. Its primary interface is a user-friendly chat, similar to ChatGPT, through which users can ask questions and receive ready-made analytical reports.
The platform is built on a four-component architecture:
LLM Core
Utilizes state-of-the-art AI models (including Azure OpenAI, Gemini, Anthropic, and Deepseek) to process investment documents, evaluate opportunities, and extract key data from diverse sources, all within a consistent analytical framework.
Orchestration Layer
This layer features a lead agent that acts as a controller, taking user requests from the chat and dynamically assigning tasks to a network of specialized sub-agents.
Knowledge Integration
The platform creates a unified data environment by seamlessly connecting multiple information sources: internal documents, financial databases, CRM systems, company policies, and web-sourced data.
Specialized AI Agent Network
We built GAIA – five specialized AI agents, each targeting a specific chokepoint. All are built on the LangGraph and LangChain frameworks and are coordinated by a lead agent.
Architecture of AI Agent Platform
Five Specialized Agents
1
Carve-Outs Agent
Watches Factiva and FactSet around the clock for divestiture signals. Groups duplicate coverage using semantic clustering (one announcement typically spawns 20+ articles). Filters against the fund's criteria (deal size, geography, sector fit). Packages findings into Excel dossiers with executive quotes, rationale, timeline, and clickable source links. Pulls in financials, ownership data, M&A history from internal and external databases.
What changed: Monitoring time dropped 85%. No relevant signals get missed.
2
Market Sizing Agent
Walks analysts through defining scope (industry, geography, segments, solution types). Then goes out and collects data from Gartner, IDC, Statista, internal research. When sources disagree (they always do), it reconciles through weighted averages with confidence bands. Calculates TAM/SAM/SOM, models growth, documents every assumption, generates charts in house style. Analysts can tweak any assumption and get instant recalculation.
What changed: Market analysis takes 4-6 hours instead of 3 days.
3
Company Profile Agent
Pulls company data from websites, LinkedIn, Crunchbase, Pitchbook, public filings, internal CRM. Structures everything identically (overview, leadership, financials, M&A history, strategic position). Flags what's missing. Links every fact to its source. Exports to Word in house format. Same structure every time means you can actually compare 50 profiles side-by-side.
What changed: Profile creation takes 90 minutes instead of 10 hours. Data completeness up 40%.
4
Knowledge Search Agent
Conversational search through the fund's entire document history using RAG. Understands what you're actually asking, identifies the relevant sector context, finds applicable frameworks and past analyses. Returns specific excerpts with links and guidance on applying them to your current situation.
What changed: Finding prior work takes 2 hours instead of 6.
5
Business Quality Index Agent
Reads through all due diligence materials and scores against 36 criteria (strategic position, revenue quality, market dynamics, competitive moat, management strength, unit economics, operational capabilities, exit routes, etc). Validates data, calculates weighted indices, flags coverage gaps, generates branded PDF briefings with spider charts and heatmaps. Every score links directly to supporting evidence.
What changed: Full assessment takes 4 hours instead of 40. Investment committee prep accelerated 8X.
Still spending weeks on deal screening?
Services
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Agentic AI custom software development
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Frontend development
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Backend development
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UX/UI design
Tech Stack
Azure OpenAI
Gemini
Anthropic
Deepseek
Azure CosmosDB
Apache Gremlin
Langchain
LangGraph
Microsoft Azure
FastAPI
Chainlit
React
Recoil
Redux
Redux Toolkit
RTK Query
TanStack Query
MUI
Azure OpenAI
Gemini
Anthropic
Deepseek
Azure CosmosDB
Apache Gremlin
Langchain
LangGraph
Microsoft Azure
FastAPI
Chainlit
React
Recoil
Redux
Redux Toolkit
RTK Query
TanStack Query
MUI
Business Value
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Robust Scalability of Analysis
Specialists can now analyze over 5,000 company profiles in a matter of hours, enabling the identification of promising investment opportunities at early stages.
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Acceleration of Key Processes
The preparation time for investment committee presentations has been reduced from days to minutes through the automated generation of slides and data visualizations.
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Focus on Strategic Tasks
By automating routine document processing and data collection, investment analysts free up their time to concentrate on high-value activities: building relationships, conducting strategic market analysis, and developing investment theses.
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Unified Evaluation Framework
The implementation of standardized assessment criteria eliminates subjective variances in team members' approaches, guaranteeing an objective and comparable analysis of investment proposals regardless of their industry or geography.
Contact us
Let's explore how our expertise can help you achieve your goals! Drop us a line, and we'll get back to you shortly.
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