AI-Driven M&A Deal Sourcing at the AnBridge Platform

A B2B Web Platform for Smarter Buyer-Seller Matching

AI-Driven M&A Deal Matching for the AnBridge Platform

M&A advisors act as intermediaries between sellers looking to exit and buyers seeking investment opportunities. However, their previous deal sourcing process is highly manual and inefficient, leading to mismatched deals and low customer satisfaction rate. To address this, Dot Point built a B2B platform that leverages AI to empower deal sourcing and speed up deal execution. I designed a solution that automates buyer-seller matching and provides data-driven insights to help advisors make smarter and faster decisions.

My Role
Product Designer

Timeline
Sep - Dec 2024

Team
1 Product Manager 1 Product Designer 2 Software Engineers 1 Business Analyst

50% efficiency improved in deal sourcing

Smarter decision-making with AI insights

Higher buyer satisfaction with relevant deals

50% efficiency improved in deal sourcing

Smarter decision-making with AI insights

Higher buyer satisfaction with relevant deals

The Abridge platform is targeting a $230 billion market in SME M&A. In China, 315K SMEs with retiring owners lack succession plans and need M&A solutions.

Dot Point is a fintech startup transforming Merge & Acquisition and investment trading through AI-driven automation. They developed  two innovative products:

  • Dot Point: A proprietary trading firm specializing in APAC retail trading. It provides capital funding and strategic support, helping traders optimize and streamline financial transactions.

  • AnBridge: An AI-powered M&A platform designed to accelerate deal sourcing and execution for SMEs in China. It empowers M&A advisors with AI-driven deal matching and faster, more scalable transaction execution.

HiveSpark AI 1.0 Redesign: Evolving beyond content creation to process management

System Map

Mergers and acquisitions (M&A) happen when one company joins with or buys another to expand, gain new technology, or improve efficiency. M&A advisors play a key role by connecting sellers and buyers, facilitating financial, informational, and strategic exchanges. They help sellers find the right buyers, guide negotiations, and ensure smooth deal execution.

To dive deeper into users’ pain points, I conducted 5 user interviews with M&A advisors from investment banks and advisory firms. Through interviews and workflow analysis, I found that their deal sourcing process is inefficient, manual and fragmented, leading to mismatched buyers, reduced deal opportunities, and lower customer satisfaction rate.

Target User Group: M&A Advisors Managing SME Deals

User Journey Map

Data Gathering – Time-consuming process with low-quality matches.

Buyer Shortlisting – Manual categorization, slow prioritization.

Buyer Matching – 70% mismatched deals, missed opportunities.

Buyer Outreach – Frequent rejections, wasted effort, lost credibility.

How might we leverage AI to streamline deal sourcing, improve match accuracy, and enhance decision-making for M&A advisors?

How might we use AI to streamline buyer data organization, prioritize high-potential matches, and provide advisors with actionable insights in real time?

🚨 Before: Manual & Inefficient Process

❌ Manual Filtering – Advisors spend hours reviewing buyer lists and investment criteria.
❌ Scattered Data – Buyer information is spread across spreadsheets, emails, and databases.
❌ Inconsistent Matching – Decisions rely on judgment, leading to mismatched deals.

🚀 After: AI-Driven Buyer Matching System

✅ Automated Recommendations – AI filters and ranks buyers instantly.
✅ Centralized Data – A structured system replaces fragmented tracking.
✅ Smart Insights – AI match scores improve accuracy and decision-making.

To address these inefficiencies, I designed an AI-powered matching feature that automates buyer recommendations, centralizes data, and enhances matching accuracy with customized criteria.

  1. Choose Targeted Seller Clients

  2. Customizable Seller Criteria

  1. AI Recommend Matched Buyers,

  2. Identify Potential Buyers and Add to Master list

For the AI Matching setup & recommendations, I explored four different versions.

Pros: Clear list for easy scanning; quick chart insights for match distribution.

Cons: Users can feel cluttered with extensive data. Limited contextual info of sellers.

Pros: Maximizes the display of various matches; Allows for a focused and clear view of each buyer company.

Cons: Less efficient for high-level analysis; overwhelming for users who need quick insights.

Pros: Visual prioritization (high, medium, low), simplifies decision-making, intuitive grouping.

Cons: Grouping by matching degrees may miss nuances and restrict user control over criteria.

Side Panel provides clear seller contextual info.

  • Lack detailed buyers data for various analysis.

  • Ranking and comparing matches remains challenging, delaying decisions.

Ranking and comparing matches remains challenging, delaying decisions.

Lack detailed buyers data for various analysis.

Side Panel provides clear seller contextual info.

Iteration based on usability testing and users feedback gathered from 3 advisors.

Final Design

Key Improvements

1.Enhanced Context: Clear seller info in the side panel with detailed buyer data.

2.Simplified Ranking: Easier ranking by matching degree for prioritization.

3.Easier Comparison: Horizontal and vertical views for faster decisions.

Flow Gif( list project- setup-recommended buyers, details

1️⃣ User Input – Users set seller criteria, AI instantly filters and ranks buyers.

2️⃣ AI Recommendations – AI generates ranked buyer lists, users refine results with filters.

3️⃣ Match Analysis – Advisors review profiles, AI explains match logic for trust.

4️⃣ Final Selection – Users shortlist buyers, AI learns to improve future matches.

How might we improve buyer-seller matching accuracy to ensure only relevant deals are sent to buyers, increasing engagement and reducing wasted effort?

🚨 Before: Manual & Inefficient Process

❌ Irrelevant Outreach – Buyers receive mismatched deals, leading to low response rates.
❌ Time-Intensive Filtering – Advisors manually shortlist buyers, wasting time.
❌ Missed Opportunities – High-potential buyers are often overlooked, reducing deal success.

🚀 After: AI-Powered Matching System

✅ Automated Screening – AI ranks buyers based on investment criteria.
✅ Personalized Recommendations – AI suggests relevant buyers, increasing engagement.
✅ Real-Time Match Analysis – AI explains match rationale, improving trust and efficiency.

To solve this, I designed the AI Matching Analysis tool. This feature gives users in-depth insights into matches, allows them to evaluate buyer companies and investment criteria, and helps them understand the rationale behind each match, ensuring better alignment and decision-making.

Assess buyer company Info and Investment criteria

Evaluate matching analysis and get detailed insights 

To find the most effective design for the AI Matching Analysis, I explored four different versions:

Pros: Clear list for easy scanning; quick chart insights for match distribution.

Cons: Users can feel cluttered with extensive data. Limited contextual info of sellers.

Pros: Maximizes the display of various matches; Allows for a focused and clear view of each buyer company.

Cons: Less efficient for high-level analysis; overwhelming for users who need quick insights.

Pros: Visual prioritization (high, medium, low), simplifies decision-making, intuitive grouping.

Cons: Grouping by matching degrees may miss nuances and restrict user control over criteria.

Side Panel provides clear seller contextual info.

  • Lack detailed buyers data for various analysis.

  • Ranking and comparing matches remains challenging, delaying decisions.

Iteration based on usability testing and users feedback gathered from 3 advisors.

1.Highlight strengths, weaknesses, and key matches with visual benchmarks.

2.Provide formula to explain how to calculate the matching degree.

3.Analyze multiple buyers on one page.

Final Design

Key Improvements

  1. More detailed matching score and matching rationale

  2. Customized weighting and percentage for key dimensions

  3. Quick actions for sending teasers to buyers and adding them to the master list

Flow Gif( list project- setup-recommended buyers, details

1️⃣ AI Ranking – AI ranks buyers by industry, revenue, and fit, helping advisors prioritize.

2️⃣ Match Analysis – AI scores matches with explanations, reducing manual work and increasing trust.

3️⃣ Customizable Criteria – Users adjust weightings, AI updates scores for accuracy.

4️⃣ Quick Actions – Advisors send teasers or schedule meetings, AI learns to improve matches.

Key Learnings & Reflections

  1. Design for Complex Workflows: M&A involves advisors, buyers, sellers, and external partners, each with distinct needs. I learned how to structure scalable workflows, map out clear user flows, and prioritize key interactions within a data-heavy platform to ensure efficiency.

  2. Build for scalability and future growth: I became more strategic in creating solutions that evolve with business needs, expand functionality over time, and integrate seamlessly with industry workflows.

  3. Bridge UX and business impact: I learned to align UX decisions with business goals, market needs, and stakeholder expectations. Moving forward, I will continue to drive design decisions that balance user needs with business success, ensuring a seamless and impactful experience.