Document Understanding: How BoostDraft Reads a Contract
Document understanding is about extracting structured meaning from unstructured text. BoostDraft uses this to power every feature — from definition popups to proofreading.
What 'understanding' a document really means
When we say BoostDraft 'understands' a contract, we mean it can extract specific kinds of structured information from unstructured text:
- Defined terms: identifying 'Company' as a defined term that appears throughout the document - Cross-references: detecting 'as defined in Section 4.2(b)' and verifying the reference exists - Clause structure: knowing that a numbered paragraph is a sub-clause of the one above it - Inconsistencies: finding that 'Effective Date' is defined differently in two places
Each of these requires a different NLP technique, and the PM needs to understand which technique powers which feature.
The core NLP pipeline
BoostDraft's document processing pipeline likely works roughly like this:
1. Ingestion — read the Word document structure (paragraphs, tables, footnotes) 2. Tokenization — split text into tokens (words, punctuation, legal shorthand) 3. Linguistic analysis — tag each token with its part of speech, detect sentence boundaries 4. Entity extraction — identify defined terms, party names, dates, monetary values 5. Structural analysis — map the clause hierarchy, detect cross-references 6. Consistency checking — compare extracted entities across the full document 7. Suggestion generation — surface actionable corrections or shortcuts to the user
As PM, you don't build this pipeline — but you define what goes into step 7. What suggestions are shown? With what confidence? In what UI?
Advanced Go players 'read' many moves ahead — they visualize the full sequence before playing. NLP document understanding is similar: the model reads the full document before surfacing any suggestion, building a complete map of the text's structure.
What the PM owns in the pipeline
The PM at BoostDraft owns the quality thresholds and UX decisions at the output stage:
- At what confidence score does a suggestion get shown vs. suppressed? - How is an inconsistency surfaced — inline, in a sidebar, as a modal? - What's the copy on a false-positive suggestion? How does the user dismiss it without feeling annoyed? - What feedback signal do we capture when a user accepts or rejects a suggestion?
These decisions directly impact model quality over time, because user feedback becomes training signal.