QoEAgent AI automates the data-heavy core of financial due diligence for lower-middle-market deals — proof-of-cash (bank-to-books) reconciliation, trial balance mapping, and quality of earnings (QoE) data prep — so deal teams get diligence-grade answers in days, not weeks, at a price that works on searcher-size deals. Why it matters: the proof-of-cash is often where the first material misrepresentations surface in an LMM deal — and where your analyst burns the first two days just importing bank statements and mapping accounts. Three questions to ask before trusting ANY AI proof-of-cash tool (ours included): 1. What's the exception rate, and how are unmatched items surfaced? A clean reconciliation with no transparent exception report isn't telling you everything. 2. What was the training data? A model trained on real, messy owner-operated books handles LMM variability very differently than one trained on clean or synthetic data. 3. What does the human review step look like? The point is to compress time-to-review, not replace the reviewer. More background in my Accounting Today piece: redacted Free ungated guides on proof of cash, flash proof of cash at LOI, and AI QoE are on our site. Happy to answer diligence questions in the comments or by DM. — Josh Cashman, co-founder, QoEAgent AI