Automate Construction Bid Proposals with Custom AI

Small construction firms often rely on a mix of general project management tools and manual processes for bidding. An estimator might use BuildingConnected to find opportunities but then manages the actual bid compilation in Excel. They receive the primary RFP as a PDF and dozens of subcontractor quotes as separate PDF email attachments, each with a completely different format. The core task becomes manually reading every document to find and transcribe line items, material specs, and prices into a master spreadsheet.

This manual process is the root of the problem. Consider a 15-person general contractor bidding on a commercial tenant fit-out. The estimator spends 4 hours reading a 50-page RFP to build their spreadsheet columns. Then, for each of the 15 subcontractor quotes received, they must find the corresponding costs, inclusions, and exclusions and type them into the correct cells. One typo in a unit price from an HVAC quote can silently erode the project's entire margin.

Software like Procore or Sage Estimating is built to manage projects post-award or to create estimates from structured inputs. These platforms are not designed to be powerful document ingestion engines. They expect clean data. They do not have the embedded AI capability to read a plumber's uniquely formatted PDF quote, understand that "copper pipe, 3/4 inch" corresponds to a specific line item in the RFP, and extract the price. Their architecture is fundamentally that of a database, not a language-processing system.

The result is that estimators, your most valuable pre-construction resource, spend their days as highly-paid data entry clerks. The bottleneck isn't their expertise; it's the time it takes to manually extract and normalize data from unstructured documents before they can even begin their real job of analyzing the numbers and building a winning bid.

The first step is a discovery call to audit your existing bid process. Syntora would review examples of RFPs and subcontractor quotes you typically handle. We would map out the exact data points you need to compare, from line-item costs to specific material grades and exclusions. This audit produces a clear scope document outlining the data extraction model and a fixed price for the build.

The technical approach would use the Claude API for its large context window and high accuracy in parsing complex documents. A central FastAPI service would provide a simple interface for your estimator to upload all bid-related PDFs. This service would trigger an AWS Lambda function that sends each document to Claude with a tailored prompt to extract the required data into a structured JSON format. This data is then stored in a Supabase database, providing a normalized foundation for comparison.

The final deliverable is a secure, private web application where your estimator can view all bids side-by-side. The interface would highlight the lowest bidder for each line item and flag any subcontractor quotes that are missing required information. This system doesn't replace your estimator's judgment; it eliminates the 4-6 hours of manual transcription so they can apply their expertise to the clean, organized data. You receive the full source code and documentation.