r/rag — public pain points

• Served bundles arrive as image-only scans that cannot be searched, and OCR falls apart precisely on contracts, tables, and handwriting

  When disclosure or served bundles arrive as scans, the content is effectively invisible: thousands of image-only PDFs with no text layer mean searching for a single word is impossible, and users resort to duct-taped workarounds (Apple Shortcuts, scripts, one-off bulk OCR jobs) just to make the file readable. Worse, the OCR tools that exist degrade exactly where litigation depends on them — handwriting, messy layouts, and dense contract tables 'fall apart,' and the best open-source options vary wildly by document type with licensing traps that surface late. Litigators preparing for hearings need to jump to a specific passage in a scanned exhibit and rely on it; instead they are left manually re-reading pages they cannot search. No product delivers reliable, bulk, layout-faithful OCR plus search and pinpoint citation over real-world legal scans, so preparation time balloons and confidence in the record drops.

  2 mentionsr/rag
• Parsing silently destroys document structure, so AI answers over case files come back wrong and cannot be traced to the source

  Teams building document Q&A over real files repeatedly trace wrong answers back to extraction, not the model: headings are flattened, table rows are chopped in half, two-column pages are read straight across both columns, and continuations across pages are lost. Benchmarks show this is systemic — one widely used converter turned columns into fake tables so that only ~4% of sentences survived intact. For litigation work this is catastrophic, because the same extraction layer feeds chronologies, contradiction analysis, and AI answers: if a deposition heading or a table row is mangled, the system asserts the wrong fact with full confidence, and the page-pinpoint needed for citation and highlighting is gone. Nothing in the market guarantees structure-preserving, page-anchored extraction tuned for legal documents, and general converters silently degrade rather than warn. The unsolved need is parsing that keeps headings, table integrity, reading order, and bounding boxes so every answer can be pointed back to the exact source passage.

  1 mentionsr/rag

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