CMR+

Integration with 3rd party platforms

Really Intelligent Document Processing

Integration with 3rd party platforms

CMR+ offers seamless integration capabilities with third-party platforms, enabling you to leverage the power of CMR+ within your existing software ecosystem.

With our integration capabilities, you can easily connect CMR+ with popular third-party applications, systems, and services. This includes but is not limited to customer relationship management (CRM) platforms, enterprise resource planning (ERP) systems, content management systems (CMS), workflow management tools and Case Management like Pega, and more. By integrating with these platforms, you can incorporate document processing functionalities directly into your established workflows, enhancing productivity and streamlining processes.

CMR+ provides a range of integration options to suit different needs. This includes APIs (Application Programming Interfaces), webhooks, SDKs (Software Development Kits), and pre-built connectors. These integration tools allow for bi-directional data exchange, enabling you to send documents for processing, retrieve processed data, and seamlessly integrate document processing capabilities into your existing applications or services.

The integration process is designed to be user-friendly and efficient. We provide comprehensive documentation, code samples, and developer resources to facilitate smooth integration with third-party platforms. Additionally, our technical support team is available to assist you throughout the integration process, ensuring a successful and hassle-free integration experience.

By integrating CMR+ with third-party platforms, you can achieve a unified and centralized document processing solution. This means that you can leverage the strengths of both platforms and create a seamless workflow that spans across systems. For example, you can automatically extract data from documents using CMR+ and directly populate it into your CRM or ERP system, eliminating manual data entry and improving data accuracy.

Moreover, our integration capabilities allow for customization and flexibility. You can tailor the integration to suit your specific requirements, mapping data fields, configuring workflows, and integrating with specific modules or functionalities of the third-party platforms. This level of customization ensures that the integration seamlessly fits into your unique business processes.

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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.