Document Processing Errors

At the start, the AI did not process documents correctly. Some data was lost or misinterpreted, which created additional manual work for the team.

Solution

Our ML engineers analyzed the issues and adjusted the processing pipeline to improve accuracy. After refining the data extraction logic, the system could extract, structure, and upload information without errors.

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Handling Multiple Document Formats

Investment data arrived in different formats – PDFs, Word documents, and PNG images – mostly through emails. Manual extraction of key information from these files, including data from graphs and charts, took too much time.

Solution

Our ML team developed a system that processed multiple file types. Optical Character Recognition (OCR) extracted text from images, PDFs, and other unstructured formats. The system also read financial data from graphs and charts, which was required for CRM records. Tailored parsing methods extracted information from structured documents. The AI processed emails and transferred the extracted data into the CRM accurately.

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Data Security and Compliance

Since the system relied on open-source AI models, data security and regulation compliance were critical. The client also needed to ensure that no sensitive information was exposed to third parties.

Solution

Uptech applied strict security protocols and encryption to protect sensitive data. The AI system runs on Azure OpenAI Service and AWS Virtual Private Cloud, both of which comply with major industry standards. These services do not transfer data to third parties. Compliance checks ensured that AI processing met all data protection requirements.

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Integration with Desktop CRM

The AI solution had to connect with the existing CRM without disrupting the team's workflow.

Solution

Our engineering team clarified the integration requirements and adjusted the system to connect with the CRM. The AI made it possible to transfer processed information into the CRM automatically, removing the need for manual input.

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Differences in Image Quality Across Devices

Medical images vary in quality due to differences in equipment (e.g., MRI or CT scanners). Variations in resolution, scale, and noise levels affect the reliability and accuracy of AI models.

Solution

To address this issue, we trained device-specific models tailored to the imaging equipment used in each clinic. We applied data normalization techniques to standardize the image inputs, ensuring consistency. Furthermore, transfer learning enabled the models to adapt to diverse imaging conditions, maintaining accuracy and reliability across all devices.