Intelligent Data Annotation Platform to Accelerate ML Training for Infrastructure Consulting
Client Overview
A U.S.-based leader in real estate engineering, infrastructure consulting, and construction management, the client serves critical sectors such as energy and utilities, government, industrial, and transportation. Known for delivering end-to-end solutions that ensure compliance, enhance operational efficiency, and support sustainable development, the client is deeply focused on digitizing and automating legacy processes across the infrastructure lifecycle. With growing volumes of unstructured data, they recognized the need for an intelligent solution to streamline document processing and fuel accurate decision-making.
Transformed Complex Land Survey Documents into Structured Intelligence
As part of a broader digital transformation initiative, the client needed to optimize the data annotation process to train a machine learning (ML) model on thousands of land survey documents. The key challenge was balancing high annotation accuracy with cost-effectiveness and operational efficiency. Manual efforts were time-consuming, inconsistent, and expensive. A scalable, automated solution was essential to extract and label critical entities like distance, direction, and curve details from complex deed documents.
01
Enable Accurate and Scalable ML Training
Build a high-quality, annotated dataset to support machine learning model development with consistent and reliable entity recognition.
02
Automated Entity Extraction from Legal Texts
Reduce manual overhead by automating identifying and tagging key information from over 2,000 deed documents.
03
Minimize Annotation Costs
Streamline the data annotation process without compromising the quality and accuracy of the labels.
04
Support Seamless Data Integration
Store, manage, and access extracted entities through a scalable cloud-native infrastructure for downstream consumption.
05
Ensure Deployment Readiness and Operationalization
Package and deploy the ML pipeline with flexibility, enabling continuous improvement and integration into the client’s existing tech ecosystem.
Streamlined Land Deed Processing with ML-Powered Data Annotation and Extraction
We implemented an end-to-end, ML-powered data annotation and extraction solution tailored for land deed processing. The solution transformed unstructured legal documents into structured, machine-readable data, accelerating ML model training and enhancing operational throughput.
Here’s how Indium’s solution delivered value:
Cloud-Based Document Retrieval and Storage
Land deed documents were securely stored in AWS S3 and efficiently retrieved for preprocessing and analysis.
Intelligent Entity Extraction Engine
An LSTM-CRF-based Named Entity Recognition (NER) model was developed to identify 12 specific entities across 2,000+ documents, including distance, direction, and curve attributes.
Efficient Data Labeling with GATE
Leveraged GATE (General Architecture for Text Engineering) software to streamline and semi-automate the data annotation process—balancing quality with cost control.
Structured Data Persistence
We began by meticulously understanding the existing data flows ("AS IS") from the policy issuance and agency systems into the designated data platform (data warehouse or data lake). This comprehensive mapping exercise ensured a seamless data integration process.
XML-Based Data Conversion
Annotated outputs were converted into XML format, making them ready for direct consumption by the ML model without additional transformation overhead.
Robust and Scalable MLOps Deployment
The complete pipeline, including the model and orchestration logic, was containerized using AWS ECR and deployed on AWS EC2 instances for scalability and ease of management.
Achieved Quantifiable Outcomes in Appeals Processing
01
85% F1 Score on ML Model Performance
The high-quality annotated data significantly improved model accuracy, enabling reliable entity recognition for large-scale legal document processing.
02
30% Reduction in Annotation Time
Automated entity extraction and annotation tools drastically reduce manual effort, accelerating the training pipeline.
03
Marked Reduction in Annotation Costs
The client significantly reduced the costs typically associated with large-scale annotation projects by combining GATE-driven semi-automation with strategic use of cloud resources.