Digitizing Workflow Automation & Entity Extraction for a Credit Rating Company
About the Customer
The customer, a credit referencing major worldwide, was using a third party to extract entity details using manual keying, contributing to significant costs and erroneous data. STL Digital proposed the Document AI Solution, leveraging the AInnovTM framework, to implement an AI-based custom solution to extract entity details and automate workflow based on file arrival from suppliers.
Challenges
- Context-Aware Intelligence: Accurately interpreting domain-specific language to provide precise and relevant responses.
- Streamlined Search & Retrieval: Instantly access relevant information from large volumes of unstructured enterprise data without manual intervention.
- Complex Data Processing: Processing complex data from various formats, including scanned documents, contracts, reports, and manuals
- Data Accuracy & Consistency: Ensuring data is accurately and consistently extracted from the source minimizes errors and enhances trust.
- Manual data entry: Information retrieval is delayed due to manual data entry processes.
Our Solution
- Gen AI-powered Application: Intelligent extraction of entities from complex, unstructured documents by combining the contextual understanding of Gen AI with the structured processing capabilities of Document AI.
- Insights-based data Validation: Validate extracted rules using insights generated by analyzing historical data using BigQuery and custom rules to extract non-extracted data.
- Rule-based module: To extract information not retrieved through entity extraction.
- Human in the loop: A decision-based approach to redirecting documents to Humans in the loop or directly ingesting the details.
The Outcomes
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Increase in accuracy of the responses received as per the query
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~$800K in savings by digitizing entity extraction and bringing it in-houseAccuracy while handling tabular content
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