AcquiAtlas
AI-Driven M&A Pipeline & Deal Intelligence Platform
Client
AcquiAtlas
Year
2024–Present
Role
Founder · Product Strategy · Data Architecture · UX Systems
At a Glance
70%
Time Saved
3x
Deal Velocity
+85%
Team Efficiency
Context
Independent investors and small funds relied on spreadsheets, emails, and PDFs to evaluate deals—resulting in slow decisions, inconsistent underwriting, and lost opportunities.
Problem
- 01Fragmented deal data across multiple tools
- 02Inconsistent valuation logic between analysts
- 03Manual diligence workflows causing delays
- 04No shared source of truth for deal teams
Approach
How we tackled the problem.
Designed a unified M&A workflow from intake → diligence → valuation
Built structured document ingestion (PDFs, financials, forms)
Developed AI agents for risk, valuation, and growth analysis
Modeled the UI after high-clarity property and asset pages
Solution
What we built.
End-to-end deal pipeline with KANBAN + milestones
AI-generated deal briefs with buyer/seller perspectives
Tiered valuation models (SDE, EBITDA, DCF)
Secure virtual data room with access controls
Results
Measurable outcomes from the engagement.
- ✓~70% reduction in time to first-pass deal evaluation
- ✓Improved consistency across underwriting decisions
- ✓Enabled smaller teams to operate at institutional quality
Learnings
What I took away from this project.
AI earns trust through transparency
Structure outperforms sophistication
Decision clarity is the real product
Tech Stack
Let's build something.
Interested in working on a similar challenge? I'd love to hear from you.
Get in Touch