M&A AI Research & Pipeline Tracker
Enabling efficient deal sourcing and tracking with automated data enrichment and a purpose-built interface
Challenges
Fragmented Research: M&A research was fragmented across spreadsheets and static documents, leading to inefficiencies
Manual Data Handling: Deal teams spent excessive time manually gathering, cleaning, and reviewing company data
Tracking Issues: No consistent way to track companies across multiple portfolio companies and sourcing strategies
Missing Centralized Management: Lack of a unified system to manage custom project-level metrics, assessments, and deal pipeline status
Collaboration Barriers: Collaboration between Partners and portfolio M&A teams was hindered by disjointed tools and workflows
Solutions
Unified Web Platform: Built with Python (Flask) and JavaScript on Azure, the platform centralizes M&A sourcing and collaboration for Partners and portfolio teams
Customizable Projects: Supports tailored tags and metrics, enabling segmentation by strategy, theme, or investment focus
AI-Driven Enrichment: Automatically enriches uploaded companies using financial and company data APIs, with AI models that extract custom attributes, summarize business details, and translate foreign content
Research Interface & Pipeline Tracker: A structured UI lets teams explore enriched profiles, edit assessments, leave notes, and seamlessly move companies into a CRM-style pipeline view for tracking statuses, actions, timelines, and deal progress
Values
Time Savings: >80% reduction in manual research time, driven by AI-enhanced enrichment, freeing teams to focus on analysis and deal qualification
Wide Adoption: Adopted across portfolio M&A teams and investment Partners, creating a consistent and efficient sourcing workflow
Scalability: Built for scale, leveraging Azure infrastructure and modular architecture for easy future extensions
Roles
Data Engineer, Cloud Engineer
Technologies
Sectors
Private Equity