Case Study
Streamlining Primary Data Processing
with AI Automation
Challenge
The client needed to extract structured primary data from a large volume of scanned documents. However, traditional manual data extraction methods were expensive given their budget constraints. The challenge was to convert complex, unstructured content into usable digital formats accurately and efficiently, without driving up costs or timelines.
DDD Solution
DDD implemented a hybrid AI-enabled extraction and human-in-the-loop quality workflow to balance speed, accuracy, and affordability.
The client provided scanned PDFs to DDD. We ingested the documents into an AI-powered extraction system to automatically identify and extract key data fields and toplines. Extracted data was reviewed by trained DDD specialists to validate accuracy, apply corrections, add missing information, define sub-populations, and remove irrelevant or redundant questions.
The cleaned and validated outputs were exported into a structured JSON format. DDD imported the JSON files into a dedicated QA platform, where additional refinements were made, such as configuring multiple-response checkboxes and correcting content logic. The finalized documents were then transferred to the client for final review and approval within their QA environment.
Impact
- Enabled high-volume primary data extraction at a significantly lower cost compared to fully manual approaches.
- Successfully converted the majority of scanned data files using AI-driven automation.
- Maintained high data quality through targeted human review and QA.
- Accelerated turnaround times allow the client to access usable structured data faster and within budget.