The biggest risks in an acquisition are rarely sitting in one obvious document. They are scattered across bank statements, payroll records, contracts, financials, and owner explanations. Kudra Due Diligence uses AI agents to connect those dots, uncover hidden risks, normalize EBITDA, and generate source-linked diligence QoE reports, so buyers can see what others miss before the deal closes. -------- Looking for searchers to test AI-powered technical due diligence on live deals We recently launched a new platform, focused on using specialized AI agents for due diligence. The early beta tests have been very positive, particularly around a problem I think gets less attention in SMB acquisitions: technical and operational diligence. A lot of risks do not show up clearly in the P&L. A seller may say the plant has plenty of spare capacity, the equipment is well maintained, no major CAPEX is needed, or recent quality issues are contained. Each document can support that story on its own. But when you cross-check maintenance logs, fixed asset records, production data, quality reports, SOPs, CAPEX plans, and operational meeting notes, a very different picture can emerge. This is what we're building Kudra's AI agents to investigate. The goal is not to replace an engineer or technical expert. The platform performs the first-pass analysis across the technical data room, surfaces inconsistencies and unsupported assumptions, and traces each finding back to the evidence. Relevant findings can then be reviewed by domain experts. We're opening a small program for searchers and SMB acquisition investors currently evaluating technically complex businesses, including manufacturing, industrial, engineering, hardware, and similar companies. We're looking for a few participants willing to test Kudra on an active or completed deal and give us direct feedback on the analysis. Happy to provide access at no cost as part of the program. Message me directly if this is relevant to a deal you're working on.