Quote intake from PDFs was manual
Construction quotes arrived as PDFs and scans, so teams re-keyed fields and lost time on inconsistent documents.
Case Study / Document AI
Construction Quote Document AI
OCR, LLM extraction, and business rules for structured construction quotes — with human review before anything is final.
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The challenge
Find the real problem.
Quote intake from PDFs and scans was slow and inconsistent.
Construction teams needed structured quote data without trusting raw model output or staying stuck in manual re-keying.
The build combined OCR, LLM extraction, and rules on AWS, with a human review checkpoint as part of the production workflow.
Extraction alone was not enough
OCR without business rules and review still produced unreliable quote data operators could not trust.
Human review had to stay in the loop
Quotes could not go live from model output alone — a checkpoint was required before a quote was treated as final.
This had to be a workflow, not a chat demo
The client needed a production document path on AWS, not a one-off chatbot that forgot the source document.
What we built
Connect the pieces.
Document AI for construction quotes: OCR, LLM extraction, and rules on AWS.
We built a document AI workflow on AWS that combines OCR, LLM extraction, and business rules, with a human review step before a quote is treated as final.
The system turns messy PDFs and scans into structured quote data operators can verify — production workflow, not a chat demo.
OCR over quote documents
Textract-class OCR patterns pull text and layout signals from PDFs and scans so downstream extraction has a reliable source.
LLM field extraction
Language models extract quote fields into structured forms while keeping the source document in view for operators.
Business rules layer
Rules validate and normalize extracted data so inconsistent documents do not silently become bad quotes.
Human review checkpoint
Operators confirm or correct results before a quote is treated as final — bounded automation with clear ownership.
What we learned
Better with every build.
Document AI wins when extraction and judgment stay connected.
This engagement reinforced that document automation for quotes is an ops workflow: source documents, structured fields, rules, and review. No invented speed or conversion percentages — only the delivery pattern from the Upwork portfolio.
Document AI needs a review gate
Construction quotes are financial artifacts — model output alone is not enough.
Human review keeps trust while still cutting manual re-keying.
OCR + LLM + rules beat any single layer
Each layer catches a different failure mode in messy real-world documents.
Production document AI is a pipeline, not a single prompt.
Keep the source visible
Operators trust extraction when they can see the original document beside the structured fields.
That pattern shows up across legal and construction document work we ship.
Outcomes
A stronger foundation.
Production document AI workflow for construction quotes with OCR, LLM extraction, and business rules on AWS.
Human-review checkpoints so quotes become structured data operators can trust — not a chat demo.
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