Automate Bid Analysis and Improve Quote Accuracy with AI

Small construction firms often rely on spreadsheets and estimating software like ProEst or Stack. While powerful for takeoffs, their cost databases are static. When material prices change weekly, an estimator must manually update hundreds of line items or risk submitting an unprofitable bid because the software does not connect to live supplier pricing.

Consider a 15-person general contractor bidding on a commercial tenant improvement project. The bid package is a 75-page PDF with architectural drawings and a spec book. The estimator spends a full day manually highlighting every material requirement, then opens five different supplier websites to check current pricing. One typo on a part number can lead to a bid that is $10,000 too low.

This manual process is slow and prone to error. A single missed requirement in the spec book, like a specific fire rating for insulation, can lead to a failed inspection and costly rework. The core problem is that spreadsheets and standard estimating software treat data as static entries. They cannot parse unstructured documents or connect to dynamic, external data sources like live supplier pricing APIs.

The structural issue is that off-the-shelf tools are built for takeoff and database management, not for real-time data integration and document intelligence. Their architecture is not designed to ingest a new PDF, understand its semantic content, and trigger API calls to external vendors. This forces estimators into the role of a human data-entry clerk, a low-value task that introduces risk into the most critical part of the business.

The first step is an audit of your current bidding process. Syntora would map out every step from receiving a bid package to submitting a final proposal. We would analyze a sample of 5-10 recent bid packages and list all material suppliers to determine how their pricing data can be accessed. You would receive a scope document detailing the proposed data extraction and integration plan.

The technical approach uses a FastAPI service that leverages the Claude API to parse unstructured PDFs from bid invitations. Claude's large context window can process a 100-page spec book in a single call, extracting line items, material specifications, and quantities. This data is then checked against a Supabase database of historical pricing and live prices fetched via Python scripts. This architecture provides a response time of under 90 seconds for a typical bid package analysis.

The delivered system is a simple web interface where you upload a bid package PDF. Within 2-3 minutes, it produces a pre-filled estimate sheet with current material costs, links to supplier pages, and flags for items needing manual review. This system integrates with your existing accounting software to sync data, and you receive the full source code deployed on AWS Lambda, a runbook for maintenance, and full ownership. A typical build for this system is 4 to 6 weeks.