Case Study:
Optical Character Recognition (OCR)
Description
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A large marketing firm conducts market analysis of pharmaceutical companies. -
There are dozens of 90-page questionnaires, which are filled out annually by several thousand doctors. People read the questionnaires and manually enter the data from them into the customer’s systems/databases. The firm required a solution that could automate this process. -
We built an AI-powered OCR solution to automate data extraction from questionnaires. The system accurately scans and interprets both handwritten and typed responses, extracting relevant information with high precision. This significantly reduces the need for manual data entry and enhances overall processing efficiency.
Challenges
- Human fatigue and oversight lead to errors, compromising data accuracy and decision-making.
- Manual, labor-intensive processes are time-consuming and costly, straining resources and limiting efficiency.
- Excessive bureaucracy slows down operations, making it harder to make quick decisions.
Solution
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91% reduction in operational costs
Forecasts the impact of parameter changes on cost, resource use, and strategic outcomes -
Agile architecture
Enables quick adaptation to changing business needs and document formats. -
13x faster processing speed
Compared to manual data entry—freeing up teams and accelerating turnaround times. -
ZERO drop-off in quality
Consistent performance over time, with no drop-off in recognition quality as workload scales.
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