Turn work documents into job records

Overview

Designed and implemented an AI-powered document processing pipeline to extract operational work requests from inbound documents and automatically create structured records in the client’s job management system.

The solution enabled reliable intake of documents from multiple external sources while ensuring accurate job creation and allocation for field staff.

Business problem

• The organisation received a high volume of work requests and work orders from government and external agencies.
• Existing OCR-based processes were inconsistent, often misreading fields or failing to transform data into the correct formats required by the job management system.
• This led to duplicate job entries, manual correction steps, and additional administrative workload for operations teams.

Solution implemented

• Implemented a proprietary AI extraction and text transformation pipeline designed to interpret semi-structured operational documents.
• Extracted key job request data and transformed it into the formats required by the client’s job management platform.
• Introduced a multi-step verification process to validate extracted data before system submission.
• Automatically created job records and prepared them for allocation to field staff.

Outcome

• Eliminated most manual handling previously required to process inbound work requests.
• Reduced duplicate job entries and data inconsistencies within the job management system.
• Improved operational efficiency by allowing work requests to move directly from document intake to job allocation workflows.
• Established a scalable model for handling additional document sources and higher processing volumes.