Automate Construction Bid Management and Estimation

Most general contractors manage bids using Procore or Autodesk Construction Cloud combined with spreadsheets. These project management platforms are excellent for tracking a project post-award, but their bidding modules treat bids as simple documents. They do not automatically extract line items, quantities, or exclusions from the 15 different PDF formats you receive from subcontractors.

Bluebeam is the go-to for PDF markup and takeoffs, but it is not a data extraction engine. An estimator still must manually copy-paste every line item from a 50-page mechanical bid into an Excel 'bid leveling' sheet. This process takes up to two full days for a complex project and is prone to costly transcription errors. A single misplaced decimal on a concrete pour bid can erase an entire project's margin.

Consider a 25-person GC bidding on a commercial tenant fit-out. They receive 12 subcontractor bids for the HVAC package alone. One is a scanned PDF, another is a Word doc, and the rest are software-generated PDFs with completely different line item structures. The junior estimator spends 10 hours building a comparison sheet, trying to normalize 'Rooftop Unit Model A' from one bid with 'RTU-A' from another. They miss a note about 'overtime labor not included' buried on page 18 of one bid, leading to a $30,000 budget surprise after the contract is signed.

The structural issue is that off-the-shelf construction software is designed for project management, not unstructured data processing. Their data models are rigid and expect standardized inputs. Subcontractor bidding is inherently non-standard, so vendors provide document storage instead of data extraction. This leaves a critical gap where your most important cost data requires hours of manual work to become useful.

The first step is an audit of your current bidding process. Syntora would review examples of bids you typically receive from at least 5 different subcontractors across various trades. We would map out every field you currently track in your bid leveling sheets and identify key data points for comparison, such as line item descriptions, quantities, unit costs, and exclusions. This audit produces a clear data schema for the extraction model.

The core of the system would be an AI data extraction pipeline built in Python using the Claude API. The Claude API is chosen for its large context window, allowing it to analyze multi-page PDFs up to 75,000 words in one pass. A FastAPI service would provide an endpoint where you upload the bid PDFs. The service sends the document to Claude with a structured prompt to extract the data into a JSON format based on the schema from our audit. Results are stored in a Supabase Postgres database.

The deliverable is a simple web interface where your estimators can upload bid PDFs and receive a standardized bid comparison sheet within 3 minutes. The system would flag ambiguities or missing items for human review. This interface connects to your existing workflow by exporting data as a CSV for your master estimate. You receive the full source code, and the system runs on your own AWS Lambda infrastructure for less than $50 per month.