CMR+

Auto QC for higher productivity

Really Intelligent Document Processing

Auto QC for higher productivity

CMR+ offers an Auto Quality Control (QC) feature that enhances productivity by automating the verification process and allowing users to customize confidence scores.

With Auto QC, CMR+ automatically assesses the accuracy and reliability of the processed documents based on predefined criteria. This eliminates the need for manual, time-consuming document reviews, resulting in higher productivity and faster turnaround times.

One key aspect of our Auto QC feature is the ability to customize confidence scores. Confidence scores are assigned to the extracted data, classifications, or predictions made by the ML models during document processing. Users can define confidence score thresholds that determine the level of confidence required for the system to automatically accept or flag the results.

By customizing confidence scores, users can establish quality control thresholds that align with their specific requirements and desired level of accuracy. For instance, if a user sets a high confidence score threshold, only the most accurate and reliable results will be automatically accepted, while results falling below the threshold will be flagged for manual review. This customization enables users to strike the right balance between automation and human oversight, optimizing productivity and maintaining control over the verification process.

Furthermore, CMR+ provides flexibility in defining quality control criteria beyond confidence scores. Users can establish additional validation rules or conditions based on specific data patterns, business rules, or regulatory requirements. These rules can be incorporated into the Auto QC process, ensuring that the processed documents meet the necessary criteria for accuracy, compliance, or other relevant factors.

The Auto QC feature is designed to be user-friendly and adaptable. Users can easily configure and fine-tune the quality control settings based on their evolving needs. Additionally, the platform provides real-time feedback and monitoring capabilities, allowing users to track the quality control results and make adjustments as necessary.

By leveraging the Auto QC feature in CMR+, users can achieve higher productivity by automating the verification process. The ability to customize confidence scores and define quality control criteria ensures that the document processing workflows align with their specific accuracy requirements and compliance standards. Ultimately, this leads to improved efficiency, reduced manual effort, and enhanced productivity in document processing tasks.

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The CMR Process

Input Sources & Document Types

  • Auto Ingestion
  • Image
  • Jpeg, TIFF, PDF
  • MS Word
  • RPA
  • DMS

Document Optimisation & Indexing

  • Noise Reduction
  • Orientation/Skew Correction
  • Background suppression
  • Classification & Indexing

Data
Extraction

  • Structured
  • Un-structured
  • Natural Language
  • Handwritten

Data
Enrichment

  • Business Rules
  • Look ups
  • API

Human in the Loop

  • Verification
  • Training
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Machine Learning

Reports & Analytics

Workflow Management

Queue, exeption
& approval management

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Data Transport & Mobilisation

RPA, APIs & Micro-services

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Client Systems

Training as a part of the workflow

CMR+ integrates training as a part of the workflow, allowing the ML models to learn and improve as it processes more and more documents. By incorporating feedback, corrections, and iterative learning, the models continually refine their algorithms and enhance their accuracy. This capability ensures that CMR+ provides increasingly accurate and efficient document processing results over time, aligning closely with your organization’s specific needs and evolving document patterns.

Training as a part of the workflow

CMR+ integrates training as a part of the workflow, allowing the ML models to learn and improve as it processes more and more documents. By incorporating feedback, corrections, and iterative learning, the models continually refine their algorithms and enhance their accuracy. This capability ensures that CMR+ provides increasingly accurate and efficient document processing results over time, aligning closely with your organization’s specific needs and evolving document patterns.
CMR+ offers the capability to incorporate training as part of the workflow, allowing the machine learning (ML) models to continuously learn and improve as they process more and more documents.

The training component of CMR+ enables you to provide feedback and corrections to the ML models based on the results of document processing. This feedback loop helps the models identify and correct any errors or inaccuracies in the extracted data, classifications, or predictions. By incorporating training into the workflow, the ML models can learn from these corrections and adjust their algorithms accordingly, leading to improved accuracy and performance over time.

The process of training the ML models within CMR+ is typically straightforward and user-friendly. When discrepancies or errors are identified and flagged during document processing, citizen developers can provide the correct information or annotations. The platform then leverages this feedback to refine the underlying ML models, updating their knowledge and enhancing their ability to accurately process similar documents in the future.

Additionally, CMR+ employs advanced techniques such as active learning, which optimizes the training process by intelligently selecting specific documents for human review. By focusing training efforts on the most challenging or uncertain cases, the ML models can learn more effectively and efficiently, saving time and resources while improving performance.
Furthermore, the training component can be integrated into an iterative workflow, where the ML models are continuously retrained as new labeled data becomes available. This ongoing learning approach ensures that the models stay up-to-date with evolving document patterns, industry-specific terminology, or changes in document formats. As a result, the performance and accuracy of the models steadily improve with each iteration.

The training-as-part-of-workflow capability in CMR+ offers several advantages. It enables the ML models to adapt and learn from real-world scenarios, allowing for continuous improvement and refinement. This iterative learning process ultimately enhances the accuracy, reliability, and efficiency of the document processing workflows, ensuring that the models align closely with the specific requirements and nuances of your organization.