AI Document Intelligence in Legal Workflows
Law firms manage thousands of unstructured documents. The product question is not “can AI read PDFs?” — it is “what must be true before a desk trusts the output?”
Question
Why do document AI tools stall inside IP and legal practices even when OCR accuracy looks strong in demos?
Research
Desks re-type government bulletins into matter systems. PDFs are bilingual, layout-noisy, and legally consequential. Most tools stop at text extraction or a chat box. Operators still own deadlines, class codes, and client risk. Understanding means fields mapped to a matter model — with provenance — not a pile of OCR text.
Observations
- Extraction ≠ understandingRaw text without field mapping and confidence still forces re-keying.
- Silent writes destroy trustAnything that mutates a matter file without a human gate will be rejected by serious firms.
- Reminders close the loopIntelligence that does not attach to renewals and filing calendars is incomplete.
Hypothesis
An AI workflow layer — OCR → classification → retrieval → human review → reminders — will ship where chat-over-PDF fails, because it mirrors how desks already work.
Experiment
Prototype direction: layout-aware OCR, structured extraction, confidence-gated review queue, merge into NepalIPMS matters, then deadline/reminder hooks. Interactive stages documented in the architecture gallery.
Conclusion
Document intelligence is a product pattern, not a model feature. The review screen is the product. See the shipped outcome in OCR Engine, the build story in the case study, and the reusable shape in Document Intelligence Pattern.