AI Technology Due Diligence
Artificial intelligence is fundamentally changing how technology due diligence is conducted in M&A transactions. Where traditional approaches rely on manual document review and consultant-led interviews, AI-powered platforms can ingest entire data rooms, extract structured intelligence, and surface material risks in a fraction of the time.
For private equity firms and corporate acquirers, this means faster deal execution, more consistent assessments, and better-informed investment decisions.
Diligenze is an AI-native platform that brings these capabilities together — purpose-built for technology diligence in M&A.
Why This Matters in M&A
Deal timelines are compressing while technology complexity is increasing. Manual diligence processes cannot keep pace — resulting in incomplete assessments, missed risks, and post-close surprises that erode deal value. AI enables deal teams to achieve comprehensive coverage without extending timelines or increasing headcount.
Common Challenges
- Manual document review creates bottlenecks under deal timelines
- Inconsistent assessment quality across different advisors and deals
- Key technology risks buried in hundreds of data room files
- No structured way to compare maturity across portfolio companies
- Findings lack traceability to source evidence
How Diligenze Helps
Diligenze uses AI to automate the most time-intensive parts of technology diligence: document ingestion, fact extraction, risk identification, and maturity scoring. Assessments are structured around industry frameworks including CIS Controls, NIST, SOC 2, and ISO 27001.
Every finding is evidence-linked and scored for severity, giving deal teams confidence in results. The platform supports the full deal lifecycle — from pre-LOI screening through post-close remediation planning.
Example Workflow
- 1Upload CIM, data room exports, and technical documentation
- 2AI ingests and extracts technology facts, risks, and dependencies
- 3Structured assessments score maturity across architecture, security, and governance
- 4Risk findings are categorised, scored, and linked to evidence
- 5Generate investor-ready reports and post-close remediation plans