Why Financial Due Diligence Is Still Broken (and How Diligra’s AI Cuts Weeks to Hours)
Industry Insights
5 min read

Why Financial Due Diligence Is Still Broken (and How Diligra’s AI Cuts Weeks to Hours)

Mid-market M&A deals still burn 100-hour analyst weeks on copy-pasting PDFs into Excel, yet up to 90 % of acquisitions miss their targets. We unpack the root causes and show, with concrete numbers, how Diligra’s agentic AI turns a five-week slog into a one-day sprint, catching red flags legacy tools miss.

Diligra - Founders

The uneasy truth about today’s M&A workflow:

  • A confirmatory-diligence window for an average mid-market deal is still about five weeks.
  • Junior analysts lose entire days re-typing SEC filings or PDFs into Excel; even automation vendors report “hours per model” were common before their tools were adopted.
  • Despite that grind, 70–90 percent of mergers ultimately miss their value targets because red flags slipped through the process.

Why existing “AI” tools haven’t fixed it:

  • Excel at bulk PII masking, not at bridging EBITDA or checking QoE.
  • Other AIs normalises deal documents and becomes an internal knowledge hub, but stops short of live scenario modelling or stress tests.
  • Some let teams search transcripts and filings fast, yet it is not wired into your spreadsheet formulas or audit trail.

A concrete mid-market scenario:

  • Deal snapshot: 120 million-euro carve-out of a German B2B SaaS vendor with three audited years and 480 contracts.
  • Manual versus Diligra: importing five years of IS, BS and CF took 12 hours by hand, five minutes with Diligra; customer-concentration checks across 480 PDFs dropped from nine hours to seven minutes; QoE bridge creation fell from sixteen hours to nine minutes; drafting a 20-page findings deck shrank from six hours to two minutes.
  • Outcome: Diligra exposed a one-off reseller prepayment that lowered sustainable EBITDA by 2.3 million euros—an oversight type that cost HP 8.8 billion dollars in the Autonomy disaster.

Why Diligra catches what others miss:

  • An agentic architecture lets EDGAR, FRED and contract copilots run in parallel so numeric and textual clues converge in one reasoning graph.
  • A context-engineering layer prunes low-value tokens, so LLM agents can read thousands of pages without hallucinating.
  • Risk templates let users write plain-English rules like “flag any margin swing above fifteen percent YoY”, which Diligra turns into diligra-query tests automatically.

Outcome benchmarks:


Ninety-five-percent faster data ingestion, less than one-percent extraction error versus EDGAR ground truth, and enough saved time to run four extra bid iterations inside the same five-week window. One hidden liability can wipe out entire deal value, as many M&A post-mortems show.

Ready to replace sleepless Excel marathons with defensible, AI-verified numbers? Join our private beta (invite-only, pricing shared on onboarding).