Custom AI Automation for Construction Project Scheduling

Most general contractors rely on Microsoft Project or Primavera P6 for scheduling. These are powerful visualization tools, but they require 100% manual data entry. A project manager spends days reading through hundreds of pages of architectural plans, subcontractor bids, and material spec sheets to manually build a Gantt chart. Every task, duration, and dependency is typed in by hand.

Project management platforms like Procore or Autodesk Build have scheduling modules, but they face the same limitation. They are systems of record, not systems of intelligence. They can store a schedule, but they cannot create one from raw documents. When a change order or RFI arrives, the PM must manually trace its impact through the entire plan, a process that takes hours and is prone to error. For example, a new spec for structural steel can delay foundation work, which in turn pushes back MEP and finishing crews, but the software does not flag these downstream conflicts automatically.

The structural problem is that these tools are databases with a calendar view. They lack a language-processing layer to understand the unstructured text where the real project data lives: in PDF bids, email change orders, and text-based daily logs. Without the ability to parse these documents, the entire scheduling process remains a time-consuming, reactive, and manual task dependent on one person's memory and diligence.

The first step is a document audit. Syntora would analyze a sample set of your project documents from 2-3 recently completed builds, including bids, plans, RFIs, and daily logs. The goal is to map the data flow and identify the specific entities to be extracted, such as tasks, durations, materials, and dependencies. This audit produces a clear plan for what the AI needs to learn from your specific paperwork.

We would build the core of the system with the Claude API for document parsing. We've built similar document processing pipelines for financial services, and the same pattern of text extraction and data structuring applies directly to construction documents. A FastAPI service would receive the extracted data, apply your company's specific business logic (e.g., add a 15% weather contingency for winter projects), and generate a complete schedule. The data would be stored in a Supabase database to track historical performance and improve future estimates. A typical build for this system is 4-6 weeks.

The delivered system is a private API that integrates with your workflow. Your team could upload new project documents to a simple web portal, and the system would return a file ready for import into MS Project or Procore in under 5 minutes. The system runs on AWS Lambda, keeping hosting costs under $50/month. The result is a tool that gives your project managers a 90% complete schedule in minutes, not days.