Automate Bid Preparation with a Custom AI System

Most construction firms rely on manual review by senior estimators. An estimator receives a 100-page Invitation to Bid (ITB) as a PDF and must spend hours reading through it, highlighting deadlines, insurance requirements, bonding capacity, and scope details. This work is tedious and carries significant risk. Missing a single requirement for a specific liability insurance rider can get an otherwise perfect bid disqualified.

Project management software like Procore or Autodesk Construction Cloud is excellent for managing projects post-award, but offers little for pre-bid analysis. These platforms act as a system of record, not an analytical engine. You can upload an ITB document to their repository, but the analysis still falls on a person to read every word. Tools like Bluebeam are effective for digital markup, but they only digitize the manual highlighting process. They do not understand the text or identify potential risks automatically.

Consider a 15-person general contractor receiving a dozen bid invitations a week. Their lead estimator spends half of every Monday just reading these documents to perform triage, deciding which bids are even worth pursuing. A critical deadline buried on page 87 is easily missed, while hours are wasted analyzing a different bid the firm was never qualified to win. The core activity becomes document management, not strategic estimation.

The structural problem is that project management platforms are built as databases with rigid schemas. They are designed to store structured information like dates, contacts, and budgets. They are not architected to ingest unstructured, highly variable text from a new PDF every day and interpret its meaning. Solving this requires a system built for natural language processing from the ground up.

The engagement would begin with a thorough audit of your past bid packages. Syntora would analyze 10-20 recent ITBs and RFPs to understand their structure, format variations, and the specific language used. This discovery process identifies the exact data points that matter to your business, such as insurance minimums, specific safety certifications, or liquidated damages clauses. The audit's output is a clear data extraction plan that guides the entire build.

The technical approach would center on a Python service built with FastAPI, using the Claude API for its advanced reasoning and large context window capable of handling 100+ page documents. When a user uploads a bid document, the FastAPI service sends the text to the Claude API with a series of structured prompts. These prompts instruct the model to first classify the bid, then extract key entities, and finally generate a concise summary of risks and non-standard requirements. All extracted, structured data would be saved to a Supabase PostgreSQL database.

The delivered system would be a simple, secure web application where your team can drag-and-drop new bid documents. In under 3 minutes, the application displays a dashboard with a go/no-go recommendation, a checklist of all contractual requirements, and a summary of potential risks. This data can then be pushed via API to automatically create a new opportunity in Procore, pre-filling dozens of fields and saving your estimators hours of manual data entry.