A fast-growing enterprise approached Glorywebs with the vision to build a smart, AI-powered ETL (Extract, Transform, Load) platform capable of handling unstructured and structured documents at scale. The goal was to automate document ingestion, extract critical data, and provide real-time, decision-ready outputs to downstream systems.
The client needed a flexible and robust solution that worked template-free, could process PDFs, images, and handwritten forms, and integrated smoothly with cloud systems and analytics pipelines. We developed “DataMorph”, an intelligent, scalable ETL tool powered by Python, React, and AI/ML models, designed to streamline document-heavy workflows in industries like lending, insurance, and compliance.
Handling varying formats like scanned PDFs, images, and handwritten notes with high accuracy was difficult.
Solution: We implemented AI-based OCR and NLP models with intelligent layout detection to extract key data fields without relying on static templates.
The client needed real-time document classification and data extraction across thousands of files.
Solution: We built an event-driven pipeline using Python and cloud functions to auto-detect document types and extract field-level data using pre-trained ML models.
Data had to be cleaned, structured, and pushed into BI dashboards and CRMs.
Solution: We designed flexible APIs and webhook-based integrations, allowing seamless data flow into tools like Tableau, Power BI, and Salesforce.
The platform needed custom rule sets for different business units and had to flag anomalies for human review.
Solution: We implemented a rule-engine layer and built an intuitive UI with React, allowing business users to create, test, and manage custom validations without code.
85% Faster Document Processing Time
98% Accuracy in Key Field Extraction
70% Reduction in Operational Costs
Real-Time Insights For Stakeholders
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