Document Automation takes data in from a large range of formats and uses
multiple processing steps and various technologies to convert that data into structured,
usable information. This structured information is then typically used in downstream steps for
updating systems of records, making decisions, or further processing aggregated
data.
Document Automation uses AI technologies for processing documents in the
following phases:
Image enhancement: The preprocessing process begins with image enhancement
and is required only for images that are of low resolution or poor quality, which
can impact optical character recognition (OCR) results. Image enhancement greatly
improves the capability to convert an image into digital characters. Our image
enhancement capabilities include binarization, de-skew, de-speckle,
noise-reduction, auto-brightness, and contrast. See IQ Bot Pre-processor package.
Document classification: IDP uses NLP, unsupervised and supervised
learning, and OCR engines to classify documents based on their type and content.
This process enables efficient routing of documents to the appropriate processing
workflows. Use the Document classifier or Advanced Classifier package actions to
classify documents. See Document Classifier package and Advanced Classifier package.
Document extraction: AI algorithms are used to extract relevant data from
classified documents. Data can include text and numeric values. Use the Document
Extraction package for data extraction. Create a learning instance in Document Automation
Document validation: Document validation is done by applying regular
expression (regex), rules, and scripts to assess, match, and manage the extracted
data for accuracy and relevance to the specific industry or business context.
Additionally, enhanced validation with Automation Co-Pilot can further
verify the extracted data for suitability for the prescribed purpose or
process.
Human-in-the-loop validation: Automation Co-Pilot capabilities
enable exceptions to field and table value identification to be addressed directly
in the application without the need to access or learn a new system. For specific
document types, validation uses supervised learning to provide a rapid feedback
loop and fine-tune AI training by correcting data via human input.
Approval task: Automation Co-Pilot provides the capability to
manage and assign approval tasks to users.
Automation Co-Pilot can be integrated with third-party applications such as
Microsoft Teams, Salesforce, and ServiceNow for users to perform human-in-the-loop validation and validation tasks within such
applications.
