# Structured Data Extraction > Structured data extraction uses AI to turn unstructured construction documents, such as sub proposals, RFPs, and specs, into typed fields like line items, prices, exclusions, and dates that can be sorted, compared, and loaded into an estimate or CRM. - Stages: Estimating, Preconstruction, Business Development - Concepts: Document Extraction, LLMs - Published: 2026-08-28 - Canonical: https://buildr.com/library/structured-data-extraction ## Why it matters in construction Almost everything a precon team receives is unstructured. Sub proposals arrive as PDFs and email bodies. RFPs are 80-page documents with the bid date buried on page 14. Specs are prose. The data an estimator or BD lead needs, the price, the exclusions, the due date, the required bonding, is in there, but someone has to read it and rekey it into a spreadsheet or a CRM. That rekeying takes hours and introduces errors. Structured data extraction does the first read and produces fields a computer can use, shifting human time from typing to verification. ## How it works 1. **Define the target schema.** You tell the model what fields you want. For a sub proposal that might be scope item, quantity, unit, unit price, total, inclusions, exclusions, alternates, and validity period. For an RFP it might be owner, delivery method, bid due date, bonding requirement, and prequalification deadline. 2. **Feed the document.** The PDF text (or, for scans, the OCR output) goes to a language model with the schema and instructions to fill it from the document only. 3. **Get typed output.** The model returns JSON or a table. Numbers come back as numbers, dates as dates, and lists as lists, so the result can be loaded into a leveling sheet or a pipeline record. 4. **Keep provenance.** Each field carries a pointer to the page and passage it came from. That makes the output reviewable. 5. **Validate.** Simple checks catch the obvious problems: totals that do not sum, dates in the past, a unit price ten times the norm for that trade. The model can identify the same fields across different proposal formats. A sub's homegrown format may need review, but it does not require a separate template by default. ## Example in practice In a hypothetical scenario, a commercial GC gets an ITB for a 120,000 sq ft distribution center. Precon runs the 90-page RFP through extraction and gets a pipeline record with the bid date, a mandatory pre-bid walk on the 9th, a 10 percent bid bond requirement, and a note that the owner requires a WBE participation plan. The WBE requirement appears on page 61, where it is easy to miss in a manual review. On bid day, the same extraction runs on 14 electrical proposals. Twelve come out clean. Two get flagged because the model could not find a total that matched the line items, and on review one sub had left out their own lighting controls line. The estimator calls them before award instead of after. ## Go deeper - [AI Bid Leveling in Construction: A GC's Practical Guide](/blog/ai-bid-leveling.md) - [AI for Estimators: 5 Practical Uses that Work Today (Not Someday)](/blog/ai-for-construction-estimators.md) - [Invitation to Bid Software: A Guide for 2026](/blog/invitation-to-bid-software.md) ## How Buildr applies this Buildr extracts line items, inclusions, and exclusions from every sub proposal into a leveling sheet, with each value linked back to the source text. See [Buildr Estimating](/estimating). ## Related terms - [AI Bid Leveling](/library/ai-bid-leveling.md): AI bid leveling reads subcontractor proposals, pulls out prices and exclusions, and puts them into a like-for-like comparison. It helps estimators judge complete scope before they award work instead of treating the lowest number as the answer. - [OCR vs. LLM Document Understanding](/library/ocr-vs-llm-document-understanding.md): OCR converts scanned construction documents into text. LLM document understanding reads that text, or the page image itself, and interprets its meaning. Precon tools often use both: OCR handles the pixels and the language model interprets the content. - [Hallucination](/library/hallucination.md): A hallucination occurs when an AI language model states something confidently that is untrue or unsupported by the source. For example, it might invent a unit price or an exclusion that does not appear in a subcontractor's proposal. - [AI RFP Analysis](/library/ai-rfp-analysis.md): AI RFP analysis uses language models to read a construction request for proposals, extract the requirements, deadlines, evaluation criteria, and risk terms, and summarize them so a general contractor can decide whether and how to pursue the job. - [Scope Gap Detection](/library/scope-gap-detection.md): Scope gap detection uses language models to compare subcontractor proposals with a construction bid package. It flags work that no sub has priced, that a sub has excluded, or that overlaps between trades before the general contractor commits to a number. ## Referenced by - [Activity Capture](/library/activity-capture.md): Activity capture uses AI to log emails, meetings, calls, and site visits against the right contacts and opportunities in a construction CRM. It gives business development teams a usable relationship history without manual data entry. - [AI Project Closeout](/library/ai-project-closeout.md): AI project closeout uses document extraction and agents to collect, classify, and verify the O&M manuals, warranties, as-builts, and lien waivers required at turnover. It tracks outstanding items by subcontractor. - [AI Takeoff](/library/ai-takeoff.md): AI takeoff uses computer vision and language models to read construction drawings, identify building elements, and count or measure quantities so estimators can price a project without tracing every sheet by hand. - [Data Readiness](/library/data-readiness.md): Data readiness is how complete, consistent, and accessible a construction company's project, pipeline, cost, and staffing records are. It determines whether AI tools can produce useful forecasts and comparisons or confident answers built on gaps. ## FAQ ### What is the difference between structured data extraction and OCR? OCR converts pixels into text. Structured extraction takes that text and decides what it means, so a line reading 'ACT ceilings, 12,400 SF, $4.10' becomes a scope item, a quantity, a unit, and a unit price in separate fields. ### How do you know the extracted data is right? Good tools keep a link from every field back to the sentence or table cell it came from, and they leave a field blank rather than guess when the source is ambiguous. Spot-checking the flagged and low-confidence fields catches most errors. ### Does the model need a template for each subcontractor? No. That was a limitation of older template-based tools. A language model can often handle a new sub's unfamiliar format without building a separate template first.