Create Faster, More Accurate Construction Bids With AI

Many construction firms find their estimating departments bogged down by manual, repetitive tasks. Estimators often spend hours flipping through 50+ drawing pages per project, meticulously extracting material quantities and scope details. Tools like PlanSwift are excellent for initial quantity takeoffs, but the data rarely flows seamlessly into the complex Excel pricing engines where the final bids are assembled. This leads to a critical point of failure: manual data entry from takeoff software into Excel spreadsheets.

Consider a specialty contractor preparing a bid for a multi-floor commercial project. After completing the takeoff in PlanSwift, an estimator must manually transfer hundreds of line items and square footage totals into their master Excel template. A single missed 'typical floor' label, indicating floors 2-17 are identical, can cause a catastrophic square footage undercount if not caught during manual review, turning a potentially profitable project into a significant loss. Beyond drawings, processing subcontractor bids for specific trades means opening multiple, uniquely formatted PDF proposals – each requiring careful scrutiny for scope inclusions, exclusions, material lead times, and labor rates – before laboriously copying the relevant data into a comparison spreadsheet.

This manual transfer work consumes valuable time from senior estimators, often 1-8 hours per project, forcing them to act as data entry clerks rather than focusing on value engineering or risk assessment. The operational impact is severe: a scaling bottleneck where a team of three estimators struggles to handle 30+ takeoffs per week, directly limiting the number of bids a firm can submit. This also introduces significant financial risk, as overlooked exclusions or transcription errors lead to standing behind wrong quotes, eroding margins and damaging client relationships. The core issue is that existing tools are not designed for intelligent data extraction and reconciliation, leaving the critical intelligence and high-risk data transfer solely to human effort.

Syntora approaches these challenges as a services engagement, starting with a comprehensive discovery phase. The first step involves a document audit and workflow analysis. We would review a representative sample of your architectural drawings (e.g., reflected ceiling plans, floor plans) and subcontractor bid documents to identify critical data points, common formats, and your existing intake-to-bid process, including how you utilize tools like PlanSwift and your specific Excel pricing templates. This analysis allows us to define a precise data model that captures all essential fields for your business, from material quantities and ceiling types to scope exclusions and lead times.

The core of the delivered system would be a data extraction pipeline built with Python and FastAPI. For architectural drawing analysis, we would implement a robust approach leveraging Gemini Vision, capable of reading and interpreting drawing elements and text. This pipeline processes documents, extracts key information, and stores the structured JSON output in a Supabase database. Our proven 5-pass verification pipeline with outlier trimming ensures high accuracy, often within 2-3% of manual takeoffs, by reconciling extracted data from multiple passes.

For populating your existing Excel pricing templates, we avoid hardcoding cell addresses. Instead, the system would intelligently discover target cell locations by scanning column A labels within your Excel files using openpyxl, writing only the quantity or data cells while meticulously preserving all your built-in pricing formulas and macros. This ensures your existing financial logic remains intact and auto-recalculates. The final output can include generating HTML quotes showing zone-by-zone scope, material quantities, and final prices, or integrating directly with systems like QuickBooks or Google Workspace based on your requirements. This architecture typically runs on cost-effective cloud infrastructure, such as AWS Lambda. Syntora delivers the full Python source code, detailed documentation, and a runbook to empower your team for ongoing maintenance.