A QoE Production Engine for a Boutique Diligence Firm
A boutique transaction-advisory firm producing quality-of-earnings reports for lower-middle-market deals, where every engagement begins with a messy data room and a long manual cleanup before any analysis can start.
What they were dealing with
Every engagement started with cleanup. The data room arrived messy: multi-year statements in inconsistent formats, scanned K-1s, complex cap tables, and contracts with dense tables. Someone restructured it all by hand before the real work could begin.
Tie-outs ran late into the night. Analysts spent hours tying the trial balance to source and normalizing EBITDA, often at 2 AM into a deal, and sometimes finished an analysis only to realize the inputs were wrong and it was useless.
The work repeated every quarter and every deal. Clients sent data late and messy, the team waited, cleaned, and restructured, then started the report from scratch.
AI had been tried and distrusted. The recurring complaint: someone would say the AI said something, and nobody could tie it back to the actual source. In diligence, a number you cannot trace is a number you cannot use.
The solution
Every figure links back to its source document, and the system flags discrepancies for a human instead of silently changing anything. AI output is treated as a draft the analyst checks, never a conclusion to trust on its own.
A data-room ingestion step that reads the messy room, including scanned K-1s and inconsistent multi-year statements, and extracts it into standardized schedules, ending the manual restructuring on every deal.
A tie-out and reconciliation assistant that cross-checks the trial balance against source, flags where things do not tie, and shows the supporting document for each figure, so the analyst reviews exceptions instead of tying out by hand at 2 AM.
An EBITDA normalization helper that surfaces candidate add-backs and adjustments with their supporting evidence, leaving the judgment on what is legitimate to the analyst.
A first-draft report generator that assembles the workpapers and draft QoE report from the validated, tied-out data, preserving partner review on every conclusion.
A diligence dashboard showing the status of every schedule, what has tied out, what is flagged, and what is still outstanding on the PBC list.
The impact
~70%
Less time on data-room cleanup and standardizing inconsistent statements
~60%
Less time tying out the trial balance, with exceptions flagged for review
100%
Of figures traceable to source, so every number can be checked
First draft
Workpapers and report drafted from validated data, partner review preserved
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