Automate Construction Estimation with Custom AI

Many construction SMBs use estimating software like Stack for takeoffs but default to spreadsheets for bid comparison. Larger platforms like Procore Financials include bid management modules, but their data intake is rigid. You must manually key in line items from subcontractor PDFs into Procore's pre-defined bid sheets. The system does not read the source document; it only provides the form to fill out.

Consider an estimator at a 20-person general contracting firm who receives eight bids for an HVAC package. Each PDF is formatted differently. One subcontractor groups labor and materials; another lists them separately. A third uses a different part numbering system. The estimator spends three hours creating a master spreadsheet, copying and pasting line items to normalize them for comparison. They miss a small note on one bid excluding refrigerant recovery, a $3,500 cost.

Procore and similar platforms are databases with forms, not document intelligence systems. Their architecture is designed for structured data entry, not for parsing the unstructured data in PDFs. They cannot adapt to the dozens of unique quote formats subcontractors use. These tools require manual labor to bridge the gap between the document you receive and the data fields in their system. The core problem is the lack of a flexible AI layer for document understanding.

The result of this manual process is not just wasted time. It introduces a high risk of data entry errors, like the missed refrigerant cost, which directly impacts project margin. It also slows down the comparison process, reducing leverage in negotiations and increasing the risk of selecting a sub whose bid looks cheapest but contains significant scope gaps.

The first step is an audit of your current bid packages. Syntora would review 10-15 past subcontractor bids for different trades to identify common formats, key data points, and edge cases. We would map out exactly how line items, material codes, labor rates, and exclusions need to be extracted and normalized. You receive a scope document detailing this parsing logic for your approval before any code is written.

The technical approach uses a FastAPI service deployed on AWS Lambda for cost-effective, on-demand processing. When a new bid PDF is uploaded, a Python function sends it to the Claude API. A carefully engineered prompt instructs the AI to extract line items, quantities, and costs into a structured JSON format. Pydantic models then validate the extracted data against your required schema, flagging any missing fields or format errors for review.

The delivered system is a simple web interface for uploading bid PDFs. The system processes them in under 90 seconds and presents a standardized comparison table highlighting cost differences and scope gaps. This extracted data can also be sent directly to your existing accounting or project management system via its API, completely eliminating manual data entry. You own the full source code, and the system runs in your own AWS account.