Users can now add and configure image and checkbox fields within data extraction workflows.

Supported Field Types

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:

• Signature
• Headshot
• Seal of Authenticity

If you require extraction for other specific image types, please contact us to discuss your use case, and we can support your needs with a custom approach.

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Note on Existing Documents

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.

You can now buy additional credits directly through the Billing page in your Affinda Organization—no sales call required.

1. Choose how many credits you need
2. Pay securely via Stripe Checkout

Auto-Reload Now Available

Never run out of credits again. Enable Auto-Reload to automatically top up your balance when it gets low—no manual tracking needed.

Need More?

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.

Head to your Billing page to get started.

Affinda now empowers users to define and customize validation rules using simple natural language prompts. Just describe the logic you'd like to apply to your document fields, and Affinda will automatically generate the corresponding rules for you.

This powerful capability gives teams the flexibility to tailor validation workflows to match internal processes, ensuring that data is complete, accurate, and meets internal requirements before it moves downstream.

Steps to Create a Validation Rule

1. Go to Field Configuration
   In the document validation interface, click “Configure Fields” in the top right corner
2. Add a New Validation Rule
   Select edit on the field you want to apply logic to, and then select “Add Validation”
3. Describe the Rule in Natural Language
   1. The selected field will be pre-filled in the prompt
   2. To reference other fields, type @ and choose from the list
   3. Clearly describe the condition or constraint. Add detail for more complex logic.
4. Click “Generate”
   Affinda will produce the corresponding rule logic based on your description.

After generation, you can review the underlying code, tweak the prompt, and re-generate the rule as needed to refine the behaviour.

For more information, see Validation Rules.

Users can now apply transformations to refine extracted text by applying a natural language prompt. Users can specify how they want text to be cleaned, reformatted, or transformed 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.

Our new Resume Summary product streamlines the hiring process by automatically generating concise overviews of candidates' experience, skills, and qualifications directly from their resumes. As part of our Recruitment AI suite, this tool helps hiring teams quickly assess applicants without manually reviewing lengthy CVs.

Designed for global hiring, the summary is available in the same language as the resume, ensuring the product is suitable for customers in all regions. Personal details such as names, ages, and nationalities are excluded to support fair and unbiased recruitment, allowing hiring teams to focus solely on candidates’ qualifications and job fit.

Who Is It For?

Recruiters and Hiring Managers
Quickly evaluate large volumes of applications with concise candidate summaries, saving time while maintaining hiring quality. The removal of personal details promotes fair and objective decision-making.

HR Software Providers
Enhance recruitment platforms with AI-powered candidate screening, improving customer experience to reduce manual review processes.

Key Benefits

Faster Hiring Decisions
Instantly access key candidate insights without sifting through full resumes.

Improved Selection Process
Focus on relevant skills and experience to identify top candidates efficiently.

Fair & Unbiased Screening
Automatically removes personal identifiers, ensuring assessments are based purely on qualifications.

Scalability for High-Volume Hiring
Handle large application volumes effortlessly, making it ideal for growing businesses and peak hiring periods.

Multi-lingual skill mapping using our default taxonomy is now fully supported across all 50+ languages when using our NextGen Resume Parser.

What’s New?

While we’ve always extracted raw skills from resumes in all supported languages, our default skills mapping that utilises the Lightcast skills taxonomy has been expanded to match skills across all common languages.

Benefits for Customers

• Standardized Data: Regardless of the language of the resume, skills are mapped to a common taxonomy, ensuring consistent data across your applications.
• Improved Usability: Mapped skills are ready for analytics, reporting, and downstream processes, eliminating the need for manual data normalization.

Key Details

• Enabled by Default: This update is automatically live for all existing and new Collections using our NextGen Resume Parser with the Lightcast skills taxonomy enabled.
• Default Output in English: By default, mapped skills are returned in English. However, if you’d like them returned in the language of the original resume, we can enable this for your account.

If you’d like to customize this setting or learn more, don’t hesitate to reach out to our team.

We’re excited to share that our invoice processing models now feature instant learning from a single document. This upgrade uses the latest advancements in AI technology to deliver greater automation benefits to customers.

What’s New?

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:
Your time is valuable. When you upload a single invoice 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.

Benefits:

• Efficiency: Reduces the effort needed to achieve accurate data extraction with just one example document.

• Speed: Models adapt instantly to new documents, streamlining your workflow.

• Precision: The latest AI advancements lead to better recognition of complex fields and layouts, improving overall accuracy.

We're excited to announce that Affinda has launched an upgraded version of our document splitting model, delivering enhanced performance for Accounts Payable document types.

Customers can easily activate this feature by selecting the new model within their Workspace settings. With this improvement, when a file is uploaded, Affinda will automatically detect and split multiple documents (such as various invoices) within the original file. This enhancement not only saves you time but also ensures that no documents are overlooked.

The upgraded Accounts Payable splitting model is now available at no cost to all our customers. It comes pre-configured to automatically split documents whenever a new invoice or other AP document is identified within a file.

For those with specific requirements, we also offer the ability to implement custom splitting models tailored to your unique use cases and document processing needs. If you’re interested in creating a custom splitting model, please contact Affinda to learn more.

We've updated the auto-validation feature in our app, enhancing how our ability to delivery straight-through processing of your documents to reduce the level of user intervention.

What's New?
You now have three customizable options for auto-validation:

1. Never: Auto-validation is disabled.
2. Validation Rules Passed: Auto-validates if all validation rules applied to that field are satisfied. In this case, field confidence will have no bearing on whether the field is auto-validated or not.
3. Validation Rules + Confidence Threshold Met: Both the confidence threshold and all validation rules must be satisfied for auto-validation. You can set the desired confidence threshold directly in the app.

These settings can be applied both at the Collection level and at a Field level within the Collections Settings page. Existing Collections remain unaffected while new Collections will have a confidence threshold not applied as default.

For more information on Validation Rules, see Machine Validation Engine.

The Affinda solution applies a range of post-processing steps to the raw text extracted from a document to ensure it is usable for downstream processing. Examples of this include:

• Formatting dates into the standard ISO format
• Removing any symbols or characters from currency values
• Matching the raw text in the document to a defined list of allowed values

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.

How to enable this setting?

The setting can be enabled in the Validation Tool by clicking the '...' and selecting 'Show Raw Values'. It can also be disabled in the same way by clicking 'Hide Raw Values'. This setting will persist across user settings.