CMR+

Scalable enterprise solution drives high volume

Really Intelligent Document Processing

Scalable enterprise solution drives high volume

CMR+ is specifically designed to handle high volumes of document processing, offering a robust platform that can efficiently manage and process large quantities of documents.

One of the key advantages of CMR+ is its scalability. As your document processing needs grow, CMR+ can seamlessly scale to accommodate the increasing volume of documents. Whether you’re dealing with thousands, millions, or even billions of documents, CMR+ can handle the load without compromising performance or efficiency. This scalability ensures that your document processing workflows can keep pace with your organization’s growth and evolving demands.

CMR+ utilizes advanced distributed computing technologies, allowing for parallel processing and distributed workload management. This means that document processing tasks can be distributed across multiple servers or processing nodes, enabling faster and more efficient execution. By leveraging the power of parallel processing, CMR+ significantly reduces processing times, enabling you to process high volumes of documents within tight deadlines.

Furthermore, CMR+ incorporates intelligent load balancing mechanisms. These mechanisms ensure that the document processing workload is evenly distributed across available resources, preventing bottlenecks and maximizing throughput. As a result, you can achieve optimal performance and efficiency, even during peak processing periods.

To enhance scalability and performance, CMR+ is designed to leverage cloud computing resources. By harnessing the power of cloud infrastructure, we can dynamically allocate computing resources based on demand, ensuring that you have the necessary processing power and storage capacity to handle high volumes of documents effectively. This cloud-based approach also provides flexibility, cost-efficiency, and easy access to the solution from anywhere, facilitating seamless integration into your existing infrastructure.

Additionally, CMR+ offers robust monitoring and reporting capabilities. You can gain real-time insights into the document processing workflows, track processing times, identify potential bottlenecks, and monitor system performance. This visibility enables you to optimize and fine-tune your processes, ensuring maximum efficiency and throughput.

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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.

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    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.