I built a forensic accounting engine for divorce

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My friend was quoted $50k by a forensic accountant to trace "separate property" (inheritance) through a joint bank account during a divorce. I realized the math they use (Lowest Intermediate Balance Rule, or LIBR) is deterministic, so I automated it. Bank statements are messy (scanned, skewed), and proving "Chain of Custody" is critical for legal admissibility. A Django app that ingests PDFs or shots and uses Mistral OCR-3 to parse transactions (way better reliability than Tesseract for tables), and runs the LIBR algorithm to identify exactly which dollars belong to whom. Recent Updates (Based on HN Feedback -- https://news.ycombinator.com/item?id=46350044 ): I just refactored the backend to support a strict Chain of Custody: Immutable Reports: Reports are now snapshot-locked upon generation. Cryptographic Integrity: Every report generates a SHA-256 hash of the raw calculation vector + the final PDF. This means you can mathematically prove the report hasn't been tampered with since generation. LIBR Verification: Added a regression suite to handle edge cases like "zero-balance dips," ensuring the tracing algorithm adheres to the See v. See (1966) legal standard. The code isn't fully open-source yet, but it's free to use right now. I'd love feedback on the PDF generation logic or the OCR accuracy! https://exitprotocols.com/

Visit product View on Hacker NewsDec 25, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, code, open · Missing: mac, agents, macos

33%33% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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