Platform Structure

Organizations

Consider an Organization as a central hub that contains all your Workspaces, Document Types, and uploaded documents which are accessible when you log into the Affinda platform. Typically, the name of your Organization matches the company name provided during initial registration.

Within Affinda, an Organization functions as a collaborative space accommodating multiple users. The Organization Owner, who initially sets up the trial account, can manage the accounts of other users, assigning and adjusting their access to specific queues as necessary.

If your company's Organization account already exists, it is recommended to create additional user accounts directly within user settings. Inviting colleagues in this manner allows them immediate access without having to complete the trial registration process.

Workspaces

A Workspace enables you to organize related document processes efficiently. Each Workspace can handle one or several Document Types and is typically used as a broader organizational structure, representing either a specific client (useful for business process outsourcing companies) or a particular department within your organization.

Document Types

A Document Type defines a category of documents that you wish to classify and extract information from. Documents of the same type should have similar structural and semantic characteristics, and require extraction of the same fields. This grouping allows for consistent and efficient use of extraction rules or models. Common examples include invoices, purchase orders, and bank statements.

Other Concepts

Model Memory

Model Memory is a validated set of reference data and documents that Affinda's models use to enhance accuracy over time. By leveraging Retrieval-Augmented Generation (RAG), Model Memory enables Affinda to dynamically reference previously validated documents, allowing the model to predict future documents more accurately without requiring constant retraining.

Fingerprinting

An advanced algorithm that identifies similar documents by analyzing unique textual and visual features. This creates a distinctive 'fingerprint' for each document, enabling precise matching and retrieval of relevant examples from Model Memory. These examples are then provided to the model to enhance accuracy and context awareness when processing newly uploaded documents.