Webinar Replay: Three AI Due Diligence Use Cases
You've heard the sales pitch covering what AI can do for PE due diligence. Now see how it's actually built.
In this 45-minute technical session, our AI engineers will build three production-ready AI-powered private equity workflows from scratch. You'll watch them create systems for automated competitor analysis, company research synthesis and DDQ similarity scoring using natural language workflow design.
This is a working session showing exactly what goes into moving AI from concept to production-grade deployment: the prompts, the logic, the edge cases, the outputs. Designed for PE technology leaders who want to understand what production-ready AI implementations actually require.
What we covered
Over 45 minutes, you'll watch our engineers build three complete AI workflows specifically for PE:
Automated Competitor Analysis
Systematically track competitor activity across deal announcements, portfolio company developments, sector concentration and strategic moves, automatically identifying patterns and emerging investment themes. Enables investment teams to spot high-growth sectors months before they become crowded and avoid overpaying in overhyped markets.Company Research Synthesis
Aggregate information from multiple sources (financials, news, filings, market data) into structured company profiles, ready for investment committee review. Improves speed, scale and accuracy for Deal Advisory teams working on company research analysis and consolidation of information.DDQ Similarity Scoring
Compare due diligence questionnaire responses across deals to identify inconsistencies, standard language and outlier responses that warrant deeper investigation. This solution supports legal and compliance colleagues by leveraging historical queries and responses to pre-draft accurate and relevant responses for human review, reducing response times from days to hours.
You'll see the natural language prompts, the workflow logic, how edge cases are handled, and what the outputs look like. This is a technical session, designed to share what production-grade AI implementation looks like specifically for private equity.
What You'll Learn
How to structure AI workflows using natural language
What makes a due diligence workflow production-ready versus proof-of-concept
How quickly these systems can be built and deployed in practice
Real examples of workflow logic, edge case handling and output quality
The path from "can AI do this?" to "here's a working system"
Speakers
Diogo Carrapato Sousa
Founding AI Engineer
Diogo is the Founding AI Engineer at WeBuild-AI. His career in consulting includes work at Accenture and Mesh-AI, as both a Data Scientist and AI Engineer. He has extensive experience building custom AI solutions for enterprise customers, with a focus on the investment, utilities and manufacturing industries.
Stylianos Oikonomou
Principal AI Engineer at WeBuild-AI
Stylianos is a Principal AI Engineer at WeBuild-AI, previously working at Builder.ai and Logically with a focus on natural language processing across multiple industries, including financial services and logistics. Stylianos is also an accomplished academic, with multiple published and peer-reviewed papers in the field of machine learning, data systems and data science.
