Product Updates

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Embeddable Review URL

• The document validation interface can be embedded in downstream workflows with the release of our embeddable review URL to all users
• This feature was previously available only to existing customers using the legacy version of the platform

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Number Data Type

• We have consolidated all number data types into a single field type
• Users can adjust the number of decimal places under ‘Decimal Options’

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New Documentation Hub

• Affinda Academy: Step-by-step tutorials, from onboarding basics to advanced playbooks, so you can master the platform at your own pace.
• Configuration Guide: A comprehensive reference to configure Affinda for your exact use case and workflows.
• API Reference: Developer-friendly documentation to get your integration up and running fast.
• Resume Parsing Guide: Tailored resources for customers using our Resume Parser and related recruitment products.
• Additional Resources: Product updates, billing and security information, and answers to frequently asked questions—all in one place.
• AI Assistant: An AI-powered assistant, trained on our documentation, ready to provide instant troubleshooting help at every stage of your customer journey.

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Picklist Field Configuration

• Users can now easily create and configure picklist fields within the Affinda Platform.

• Options are (typically short) lists of values that are added manually through the interface
• The values are sent to the model when the document is processed, and the model predicts the appropriate value from the list

• An array of parameters (one or many) that acts as a “lookup” or “master” list
• Typically, this will be master or source data from a customer’s system (e.g. ERP, CRM)
• Data Sources can be created by uploading an Excel or CSV file through the Affinda Platform and then maintained through either periodic updates through the same interface, or by syncing the data programmatically
• The raw data extracted from the document is mapped to the Data Source using string matching (either Fuzzy, Partial, or Exact)

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User Queue Management

• Improved logic to ensure that multiple users are not reviewing the same document simultaneously
• After a user confirms a document, the next document in the queue that is not being looked at by another user will be presented
• In the cases where multiple users have the same document open, the user who opens the document last will have a warning displayed to notify them of another user in the document

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Image & Checkbox Field Types

• Users can now add and configure image and checkbox fields
• Checkbox Fields:
  • Label: Use when one or more options may be checked. Returns the label text for each selected option.
  • True/False: Use for a single checkbox. Returns True if checked, False if unchecked.
• Image Fields: Returns the identified image for Signatures, Headshots and Seals of Authenticity
• The image models are only applied when new documents are uploaded after the fields are created. Previously uploaded documents will need to be re-parsed for the model to extract the relevant image/data.

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Purchase Credits via Billing Page

• Purchase additional credits directly through the Billing Page in your Affinda Organization - no sales call required.
• Enable Auto-Reload to top up your balance when it gets low automatically
• For high-volume or custom credit packages, our Sales team can configure tailored deals that can still be paid through the platform or via standard invoicing.

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Validation Rule Creation

• Validation rules can now be created through the Affinda Platform by specifying the rule in natural language
• Describe the logic you’d like to apply and reference the relevant fields you’d like to include and Affinda will automatically generate the corresponding rules for you

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Text Transformations

• Users can now apply transformations to refine extracted text by applying a natural language prompt to be clean, reformat, or transform the data for better usability
• With the description of what transformation to apply, Affinda will process this using either:
  • Large Language Models (LLMs) for dynamic text refinement
  • Code-based transformations, where possible, ensuring minimal variability in standardized data
• This applies to fields of ‘text’ data type only

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Self-Learning Classification

• Affinda has launched new capabilities for document classification - our general model applied on new Workspaces now knows your specific document types and continues to improve as new documents are added to Model Memory
• When a new document is uploaded and needs to be classified, the model uses two key inputs:
  1. Document Type Names and Descriptions
  2. Reference Documents from Model Memory

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Instant Learning Capability

• Affinda models now benefit by default from instant learning capabilities
• Learn from Just One Document: Our system now only needs one example of a particular invoice format to perform accurate data extraction. This eliminates the need to upload multiple examples for model training.
• Immediate Adaptability: When you upload a single document of a specific format, our models will immediately apply the learnings to the next document of the same type. This minimizes the setup and preparation time.
• Higher Accuracy: With continuous improvements driven by state-of-the-art AI, this latest release excels at recognizing layouts and specific data structures. This ensures high accuracy without extensive training.

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Enhanced Document Splitting Model

• Affinda has launched an improved document splitting model that works well across any document type
• This model has been designed to identify specific cues that indicate a new document, including:
  • Change in page numbering sequence (e.g. Page 1)
  • Change in key party within the document (e.g. an invoice from a different supplier is identified)
  • Change in key document identifier

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Resume Summary launch

• Our new Resume Summary tool for Recruitment Technology automates concise overviews of candidates’ experience, skills, and qualifications from their resumes
• The summaries match the resume’s language, making the tool suitable for all regions
• Personal details like names, ages, and nationalities are excluded to ensure fair, unbiased recruitment and focus on job fit

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‘Raw Values’ display

• To provide greater control and visibility to customers, we have introduced a new setting that allows users to view the ‘raw’ text extracted from the document alongside the ‘parsed’ value that has been formatted and post-processed
• This means that users can see both values side by side and ensure that the post-processing is accurate.

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New Resume Redactor

• Affinda has launched a new version of our Resume Redactor, delivering higher accuracy and greater configurability
• The model works well across both resumes and cover letters and can be applied across a wider range of fields

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General Redaction capability

• Any document can now be redacted using our new ‘redaction’ API endpoint

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Status Page

• Status Page launched to keep customers informed about the performance of our AP and web application across all of our server locations
• To access the Status Page and explore its features, visit 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