Automating Subcontractor Onboarding with AI-Driven Document Data Extraction

Integrate an intelligent subcontractor onboarding system that leverages AI document processing, document data extraction, and unstructured data processing to transform inconsistent Excel/CSV uploads into clean, analytics-ready subcontractor profiles - with accurate CSI trade mapping and minimal manual effort.

60% Faster Onboarding‍
90% Reduction in Manual Data Entry
75% Scalable CSI Trade Accuracy

Business Requirements

Our client’s construction management platform enables general contractors to upload subcontractor lists - but these files come in many different formats, layouts, and structures. Traditional upload handling and manual review were slow, error-prone, and unscalable.

They needed a solution that could:

Automatically interpret unstructured subcontractor lists without rigid templates
Extract and standardize key subcontractor fields via AI document processing
Map subcontractor trades to valid CSI Division codes through AI data mapping
Scale efficiently without ongoing manual effort
Integrate seamlessly with existing construction management software workflows

To address these challenges, our client required an AI-driven pipeline capable of transforming raw Excel/CSV uploads into clean, standardized subcontractor records - improving onboarding velocity and data quality across projects.

Solution Overview

An end-to-end subcontractor data processing solution that ensures accuracy, consistency, and scalability across high-volume onboarding workflows.

Subcontractor List Ingestion

Integrated directly with PlanHub’s UI upload mechanism to receive Excel and CSV files
Accepted varied layouts and column naming conventions via advanced intelligent document processing
Pre-validated files for structural consistency before downstream processing

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Intelligent Data Extraction

Applied AI-powered models to read and understand all file structures
Extracted critical subcontractor data including company name, contact details, address fields, and trade classifications
Handled common formatting issues (e.g., text casing, international phone formats, missing headers)

Normalization & Trade Mapping

Converted extracted data into clean, normalized profiles using data standardization software
Employed AI data mapping to match subcontractor trade text to appropriate CSI division names and codes
Ensured uniform classification across projects and reduced ambiguity in trade categorization

Scalable Serverless Architecture

Deployed using serverless architecture on AWS for cost-effective auto-scaling
AI models triggered per upload, reducing redundant compute overhead
Integrated into their backend to automatically update subcontractor profiles after processing

Business Impact & Results

Accelerated Subcontractor Onboarding: Automated document data extraction reduced onboarding time and eliminated manual processing delays.

Improved Data Accuracy and Consistency: AI-driven parsing and CSI trade mapping standardized subcontractor data across projects.

Scalable, Low-Overhead Operations: A serverless architecture enables seamless scaling without added infrastructure complexity.

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