DDLoop

Australian legal tech startup automating due diligence for M&A, insolvency, and investment transactions by pulling and cross-referencing data from government registers (ASIC company extracts, director/shareholder histories, PPSR security interests, IP Australia patent/trademark records, insolvency practitioner appointments). Founded 2022 by Jack Rathie (ex-King & Wood Mallesons, UNSW Law) and Nico Kunz (University of Melbourne). Startmate W23 alumni. Won first place at 2025 Legal Pitch Night ahead of Amender and Clearly by Lext. Selected for LawTech Hub by Lander & Rogers (2025). Presented at 2025 ALPMA Summit + ALTACON. Offices in Sydney, Melbourne, and London. Also runs MergerLoop (Australian merger announcement newsletter). Notable customers include Talbot Sayer (leading Australian M&A firm) and MinterEllison (one of Australia’s largest law firms), both with published case studies confirming time savings in due diligence workflows. MinterEllison adoption suggests enterprise viability for large Australian law firms. Founder Jack Rathie experienced the manual due diligence pain firsthand at KWM and built DDLoop to solve it — genuine practitioner-to-founder story. Security posture strong for a startup: SOC 2 Type I & II certified, Australian data residency, AES-256 encryption, enterprise SSO support. DDLoop appears to be the only dedicated Australian government register due diligence automation tool — competitors like Luminance focus on document-based DD rather than register-based DD. 5/5 rating from 50 reviews on DDLoop website (not independently hosted). Mentioned positively on r/legaltech ‘most unique legaltech product’ thread. Bootstrapped per Tracxn (frontmatter shows $303.8K funding — source unclear). ALTA member.

Company Info

• Founded: 2023
• Team size: 1-10 employees
• Funding: $303.8K
• HQ: Australia
• Sector: M&A

What We Haven’t Verified

This page was assembled from publicly available information. Feature claims and workflow mappings are based on what the vendor and third-party listings publish — not hands-on testing or practitioner feedback.