After spending time with searchers and operators in the ETA community, we kept hearing the same thing: due diligence is either painfully slow, prohibitively expensive, or both. A QoE firm takes 4-6 weeks and costs $15K-$30K. Doing it manually means hoping you catch everything. Neither feels right for a $1M-$5M deal where time and capital are your scarcest resources. So we built an AI due diligence platform designed specifically for SMB acquisitions. What it actually does You upload your data room. Agents pre-process every document into a structured data layer first, extracting figures, dates, and terms from each source into a normalized schema. Then the agents run cross-document reconciliation simultaneously: P&L against tax returns against bank statements against contracts against payroll filings. It reconciles. Every finding is traceable. When the agents detect a contradiction, the output tells you the exact document, the exact page, and the exact field where each side of the contradiction lives. These are some of the analysis it runs on every deal: - Revenue reconciliation: P&L against bank deposits and tax return gross receipts, month by month across 3 years. Every gap sourced. - Payroll reconciliation: accounting payroll against Form 941 filings and W-3 transmittals. Unexplained gaps flagged with period and document references. - EBITDA normalization: every add-back identified with source document, account code, and confidence level. - Contract risk matrix: every customer contract analyzed for change-of-control clauses, expiry dates, and revenue at risk. Each clause cited to exact section and page. - AR/AP quality: aging cross-referenced against revenue and billing records. Disputed balances and concentration risks flagged. - Legal and tax exposure: IRS correspondence and litigation cross-referenced against financial statements. Why this is different from using Claude or ChatGPT directly We get this question a lot. The short answer: a real data room has hundreds of documents and thousands of pages, at that scale, generic AI hits context limits, produces non-deterministic outputs, and has no guarantee that a critical clause on page 47 of a contract got the same attention as page 1 of the income statement. More importantly, when Claude tells you "there appears to be a discrepancy," it cannot tell you which line of which document produced that conclusion, you cannot hand that to your attorney or lender and defend it. And across hundreds of documents, the hallucination risk is real: a missed change-of-control clause or an undetected IRS notice doesn't show up as an error, it shows up as a bad deal that closes at the wrong price. In our case, the agents pre-processes every document into a structured context layer before any AI reasoning begins, runs reconciliation logic deterministically across the full data room, and traces every finding to the exact document, page, and field on both sides of the contradiction. What beta participants get - Full platform access at no cost for your active deal - A complete sourced due diligence report, financial contradictions, legal risk flags, payroll reconciliation, contract risk matrix, EBITDA bridge, and recommended price adjustments - Direct access to our team throughout your diligence process, we want your feedback and we'll be hands-on If you find it useful, we'd love a testimonial. If you find gaps, we want to know. Either way you get a thorough analysis of your deal at zero cost Who we're looking for Searchers or independent sponsors with a deal currently under LOI or actively in diligence. Deal size $500K to $10M purchase price. Any industry. Investors who back multiple searchers and want to see what a Kudra analysis looks like before recommending it to your portfolio, we'll run a full demo on a real or synthetic data room, your choice.