Automate Preliminary Estimates for Your Construction Business

Most small construction companies manage estimates in Excel. While flexible, spreadsheets are prone to copy-paste errors and lack version control. A single mistake in a formula can quietly throw off a project total by thousands of dollars, directly impacting profitability on a 5-10 project per year schedule where every bid counts.

All-in-one construction management tools like Buildertrend or CoConstruct offer estimating modules, but they are not built to ingest unstructured data. These platforms expect you to manually type line items from a subcontractor's PDF into their web forms. The software manages the data once it is structured, but it does not solve the primary bottleneck: getting the data out of the messy, inconsistent documents you receive every day. You are still the human bridge between a plumber's PDF quote and the software's database.

Consider a typical scenario: you receive three bids for framing. One is a two-page PDF with detailed exclusions, another is a simple list in an email body, and the third is a scanned, hand-marked drawing. To compare them, you must manually find the total cost, line items, and terms in each document and transcribe them into a master spreadsheet. This takes two hours of focus and carries a high risk of missing a critical detail, like an exclusion for lumber disposal, that makes one bid appear cheaper than it is.

The structural problem is that existing software is designed for data management, not data extraction. The tools provide rigid forms, but the raw material for your estimate arrives as unstructured documents. The software cannot read, so you are left with the tedious and error-prone job of translating unstructured bids into the clean, structured format the system requires. This manual gap is where time is lost and expensive mistakes are made.

The first step would be an audit of your existing bid documents. Syntora would analyze 20-30 recent subcontractor quotes and material lists to identify common formats, key terminology, and critical variations. This audit directly informs the parsing strategy for the AI model and establishes a performance baseline. You would receive a clear report showing which documents can be automated with high confidence and which may require a final human review.

The core of the system would be an AI processing pipeline built in Python. This pipeline uses the Claude API, which excels at extracting structured data like line items, quantities, and costs from unstructured PDFs and text. A FastAPI service would provide a simple interface where you upload all documents for a given project. The service sends each document to the Claude API for analysis, validates the structured JSON output with Pydantic, and stores the clean data in a Supabase database. This entire extraction and storage process completes in under 60 seconds per document.

The delivered system is a simple web application where your team can view all extracted bid data in a single, standardized table. The interface allows for quick side-by-side comparison, highlights outliers, and lets you export the final, clean data to a CSV file for import into your accounting software. The application is deployed on Vercel with the backend running on AWS Lambda, resulting in a system that costs less than $50 per month to operate.