QoEAgent AI automates the data preparation behind quality of earnings (QoE) analysis for lower-middle-market deals: proof-of-cash reconciliation, trial balance mapping, bank statement processing, and agreement extraction. Diligence-grade answers in days, not weeks, at a price that works on the deals searchers actually buy. Reposting this here from my earlier discussion post, per the moderators' guidance, since it references our product. Why this matters: the proof-of-cash is often where the first material misrepresentations surface in a lower-middle-market deal - and it's also where your analyst burns the first two days just importing bank statements and mapping accounts. AI tools now automate that data prep, but the marketing blurs what they actually do. Three questions that cut through it before you trust the output of ANY AI diligence 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 lower-middle-market 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. The firms getting value treat AI as infrastructure - automating the plumbing so the experienced accountant spends more time on judgment. I wrote more on this for Accounting Today: redacted Free ungated guides at redacted - proof of cash, flash proof of cash at the letter of intent (LOI) stage, and AI QoE. Happy to answer anything in the comments or by DM.