Generate Construction Bids in Seconds, Not Hours

Most construction firms rely on proposal templates in tools like PandaDoc or Proposify. These platforms manage formatting and delivery but do not help write the proposal. An estimator still has to manually read a 100-page RFP, copy-paste requirements into the template, and then search a shared drive for relevant case studies. This manual process is slow and prone to error, often causing teams to miss subtle requirements that lead to costly change orders.

Some teams try using general AI tools like ChatGPT by pasting in the RFP text. This approach fails because of context limits, privacy concerns, and a lack of grounding in your company’s actual data. The AI will hallucinate project details, invent safety certifications you don't possess, and produce generic text that fails to differentiate your company from the competition.

Without a system that is purpose-built for construction bids and trained on your own successful projects, the AI's output is untrustworthy. It creates more editing work than it saves and cannot be relied upon for business-critical documents.

Syntora would approach the development of an automated bid proposal system through a structured engineering engagement. The first step involves a discovery phase to audit your existing documents and data sources. Based on this audit, we would design and build a document processing pipeline using Python. For digital PDFs, this pipeline would integrate PyMuPDF for direct text and table extraction. For scanned documents or blueprints with embedded text, an OCR service like AWS Textract would be used to convert images to machine-readable text. We have built document processing pipelines using Claude API for financial documents, and that technical pattern is directly applicable to handling construction RFPs and related project documentation.

The extracted text would then be sent to the Claude API. Syntora would develop a series of structured prompts to identify and extract critical data points, such as project scope, deadlines, bonding requirements, insurance minimums, and key personnel roles. These structured outputs would be stored in a Supabase Postgres database, creating a foundation for historical bid analysis.

To generate a proposal draft, a FastAPI application would query the Supabase database for the new RFP's requirements. It would then retrieve relevant context from your existing systems, such as connecting to a Procore API for project histories or your ERP for cost codes. This curated context, potentially including your most relevant past projects, would be fed back to the Claude API to draft each section of the proposal.

The delivered system would be designed for deployment as serverless functions on AWS Lambda, optimized for availability and cost efficiency. Syntora would provide your team with a simple web interface for RFP uploads. The system would be engineered to produce a complete proposal draft as a Microsoft Word document, ready for review and finalization by an estimator. A typical build timeline for a system of this complexity, including discovery, development, and initial deployment, often ranges from 8 to 14 weeks, depending on the volume and variability of client data. The client would need to provide access to example RFPs, relevant project history data, and internal proposal templates. Deliverables would include the deployed cloud application, source code, comprehensive documentation, and knowledge transfer.